WO2021164100A1 - 图像处理方法及装置、电子设备和存储介质 - Google Patents

图像处理方法及装置、电子设备和存储介质 Download PDF

Info

Publication number
WO2021164100A1
WO2021164100A1 PCT/CN2020/081364 CN2020081364W WO2021164100A1 WO 2021164100 A1 WO2021164100 A1 WO 2021164100A1 CN 2020081364 W CN2020081364 W CN 2020081364W WO 2021164100 A1 WO2021164100 A1 WO 2021164100A1
Authority
WO
WIPO (PCT)
Prior art keywords
feature
features
density
images
target
Prior art date
Application number
PCT/CN2020/081364
Other languages
English (en)
French (fr)
Inventor
郭森辉
徐静
陈大鹏
赵瑞
Original Assignee
深圳市商汤科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳市商汤科技有限公司 filed Critical 深圳市商汤科技有限公司
Priority to JP2021526214A priority Critical patent/JP7114811B2/ja
Priority to SG11202105513VA priority patent/SG11202105513VA/en
Priority to US17/328,432 priority patent/US20210279508A1/en
Publication of WO2021164100A1 publication Critical patent/WO2021164100A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/771—Feature selection, e.g. selecting representative features from a multi-dimensional feature space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
    • G06V10/806—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of extracted features

Definitions

  • the present disclosure relates to the field of computer technology, and in particular to an image processing method and device, electronic equipment, and storage medium.
  • Clustering can group multiple targets (such as human faces) belonging to the same category. For example, images belonging to the same person in an image library can be clustered together to distinguish images of different people.
  • images belonging to the same person in an image library can be clustered together to distinguish images of different people.
  • the features of the target in the image can be extracted and the features can be clustered.
  • the present disclosure proposes a technical solution for image processing.
  • an image processing method including: determining the density of each of the first features according to the first features of a plurality of first images to be processed, and the density of the first features represents The number of first features whose distance to the first feature is less than or equal to the first distance threshold; according to the density of the target feature, the density chain information corresponding to the target feature is determined, wherein the target feature is any A first feature, the density chain information corresponding to the target feature includes N features, and the i-th feature of the N features is one of the first neighbor features of the i-1th feature of the N features One, and the density of the i-th feature is greater than the density of the i-1th feature, N, i is a positive integer and 1 ⁇ i ⁇ N, and the first neighboring feature includes the same as the i-1th feature.
  • the density chain information corresponding to the target feature further includes second neighbor features of the N features
  • the second neighbor feature of the i-1th feature of the N features includes At least one first feature whose distance from the i-1th feature is less than or equal to the third distance threshold, according to the density chain information corresponding to each of the first features, respectively, for each of the first features
  • the feature adjustment to obtain the second feature of the plurality of first images includes: for the target feature, the N features and the second neighbor features of the N features are respectively fused to obtain the target The N fusion features of the feature; determine the correlation feature between the N fusion features according to the N fusion feature of the target feature; determine the relationship between the N fusion features of the target feature and the associated feature The second feature of the first image corresponding to the target feature.
  • determining the second feature of the first image corresponding to the target feature according to the N fusion features of the target feature and the associated feature includes: separately comparing the associated feature with The N fusion features are spliced to obtain N splicing features; the N splicing features are normalized to obtain N weights of the N fusion features; according to the N weights, all The N fusion features are fused to obtain the second feature of the first image corresponding to the target feature.
  • the method before the determining the density of each of the first features according to the first features of the multiple first images to be processed, the method further includes: according to the first features of the multiple first images.
  • a feature graph network is established.
  • the feature graph network includes multiple nodes and connections between the nodes, each of the nodes includes one third feature, and the value of the connection represents The distance between the node and the neighboring nodes of the node, the neighboring nodes of the node include the K nodes with the smallest distance from the node, and K is a positive integer; the feature graph network is graphed Product processing to obtain the first features of the plurality of first images.
  • the i-th feature of the N features is the feature with the highest density among the first neighboring features of the i-1th feature of the N features.
  • the method before the establishment of the feature map network according to the third features of the plurality of first images, the method further includes: performing feature extraction on the plurality of first images to obtain The third feature of the plurality of first images.
  • the clustering the second features of the plurality of first images to obtain the processing result of the plurality of first images includes: The second feature is clustered to determine at least one image group, each of the image groups includes at least one first image; respectively, the target category corresponding to the at least one image group is determined, and the target category represents the first image
  • the processing result includes the at least one image group and the target category corresponding to the at least one image group.
  • an image processing apparatus including:
  • the density determination module is configured to determine the density of each first feature according to the first features of the multiple first images to be processed, and the density of the first feature indicates that the distance between the first feature and the first feature is less than Or the number of first features equal to the first distance threshold; the density chain determination module is used to determine the density chain information corresponding to the target feature according to the density of the target feature, wherein the target feature is any one of the first features ,
  • the density chain information corresponding to the target feature includes N features, the i-th feature of the N features is one of the first neighbor features of the i-1th feature of the N features, and
  • the density of the i-th feature is greater than the density of the i-1th feature, N, i is a positive integer and 1 ⁇ i ⁇ N, and the first nearest neighbor feature includes the distance between the i-1th feature and the i-1th feature.
  • the density chain information corresponding to the target feature further includes second neighbor features of the N features
  • the second neighbor feature of the i-1th feature of the N features includes At least one first feature whose distance from the i-1th feature is less than or equal to a third distance threshold
  • the feature adjustment module includes: a fusion sub-module, which is configured to perform an adjustment to the target feature The N features and the second neighbor features of the N features are respectively fused to obtain N fusion features of the target feature; a feature sub-module is used to determine the N fusion features according to the N fusion features of the target feature The associated features between the fusion features; a feature determination sub-module for determining the second feature of the first image corresponding to the target feature according to the N fusion features of the target feature and the associated feature.
  • the feature determination submodule is used to: stitch the associated features with the N fusion features to obtain N stitching features; and normalize the N stitching features According to the N weights, the N fusion features are fused to obtain the second feature of the first image corresponding to the target feature.
  • the device before the density determination module, further includes: a graph network establishing module, configured to establish a feature graph network according to the third feature of the plurality of first images, the feature The graph network includes a plurality of nodes and connections between the nodes, each of the nodes includes one of the third characteristics, and the value of the connection represents the distance between the node and the neighboring nodes of the node
  • the neighboring nodes of the node include K nodes with the smallest distance from the node, and K is a positive integer; the graph convolution module is used to perform graph convolution processing on the feature graph network to obtain the multiple The first feature of the first image.
  • the i-th feature of the N features is the feature with the highest density among the first neighboring features of the i-1th feature of the N features.
  • the device before the graph network establishment module, the device further includes: a feature extraction module, configured to perform feature extraction on the multiple first images to obtain the multiple first images The third feature.
  • a feature extraction module configured to perform feature extraction on the multiple first images to obtain the multiple first images The third feature.
  • the result determination module includes: a clustering submodule, configured to cluster the second features of the plurality of first images, and determine at least one image group, each of the images The group includes at least one first image; the category determination sub-module is used to determine the target category corresponding to the at least one image group, the target category represents the identity of the target in the first image, and the processing result includes all The at least one image group and the target category corresponding to the at least one image group.
  • an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the foregoing method.
  • a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • a computer program including computer readable code, and when the computer readable code runs in an electronic device, a processor in the electronic device executes the above method.
  • the density of multiple image features can be determined, the density chain information of the feature can be determined according to the feature density, the feature can be adjusted according to the density chain information, and the adjusted feature can be clustered to obtain the processing result.
  • the spatial density distribution of is adjusted to the features, which can improve the clustering effect of the image.
  • Fig. 1 shows a flowchart of an image processing method according to an embodiment of the present disclosure.
  • Fig. 2 shows a schematic diagram of a density chain determination process in an image processing method according to an embodiment of the present disclosure.
  • Fig. 3 shows a schematic diagram of density chain information in an image processing method according to an embodiment of the present disclosure.
  • 4a, 4b, 4c, and 4d show schematic diagrams of image processing procedures according to an embodiment of the present disclosure.
  • Fig. 5 shows a block diagram of an image processing apparatus according to an embodiment of the present disclosure.
  • Fig. 6 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • Fig. 7 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • Fig. 1 shows a flowchart of an image processing method according to an embodiment of the present disclosure. As shown in Fig. 1, the method includes:
  • step S11 the density of each first feature is determined according to the first features of the multiple first images to be processed, and the density of the first feature indicates that the distance between the first feature and the first feature is less than or The number of first features equal to the first distance threshold;
  • the density chain information corresponding to the target feature is determined according to the density of the target feature, where the target feature is any one of the first features, and the density chain information corresponding to the target feature includes N features ,
  • the i-th feature of the N features is one of the first neighboring features of the i-1th feature of the N features, and the density of the i-th feature is greater than the i-1th feature
  • the density of features, N, i is a positive integer and 1 ⁇ i ⁇ N
  • the first neighbor feature includes at least one first feature whose distance from the i-1th feature is less than or equal to the second distance threshold
  • the target feature is the first of the N features;
  • each of the first features is adjusted according to the density chain information corresponding to each of the first features to obtain the second features of the plurality of first images;
  • step S14 clustering the second features of the plurality of first images to obtain the processing result of the plurality of first images.
  • the image processing method can be executed by electronic equipment such as a terminal device or a server, and the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, or a cordless
  • UE user equipment
  • PDAs personal digital assistants
  • the method can be implemented by a processor invoking computer-readable instructions stored in a memory.
  • the method can be executed by a server.
  • the multiple first images to be processed may be images collected by an image collection device (for example, a camera), or partial images intercepted from collected images, or the like.
  • the first image includes the target to be recognized (for example, a human face, a human body, a vehicle, etc.).
  • the targets in the multiple first images may be targets of the same category (for example, the face of the same person), so the targets of the same category may be clustered together to facilitate subsequent processing.
  • the present disclosure does not limit the method of acquiring the first image and the specific type of the target in the first image.
  • the feature information in multiple first images can be extracted by, for example, a convolutional neural network, and the extracted feature information can be used as the first feature; preliminary processing can also be performed on the extracted feature information, Use the processed feature information as the first feature.
  • the present disclosure does not limit the method of obtaining the first feature and the type of the convolutional neural network used to extract the feature.
  • the density of each of the first features may be determined according to the first features of the multiple first images to be processed.
  • the distance between the density of the first feature and the first feature is less than or equal to the number of first features of the first distance threshold. That is to say, the number of surrounding features within a certain range of each first feature can be determined according to the distribution of the features in space, as the density of the location of each first feature.
  • the specific value of the first distance threshold can be set according to the actual situation, which is not limited in the present disclosure.
  • step S12 for any one of the plurality of first features (which can be referred to as the target feature), according to the density of the target feature, a denser surrounding the target feature can be searched for.
  • the first feature (higher than the density of the target feature), or the first feature with the highest density among the first features greater than the density of the target feature, and a mark pointing to the first feature is established.
  • the above-mentioned processing is performed separately for each first feature to form a tree structure.
  • the first feature with the highest density can be found for each first feature along the tree structure, so that a density chain can be found, which is called density chain information.
  • the first neighbor feature of the i-1th feature can be found, including the distance from the i-1th feature At least one first feature less than or equal to the second distance threshold; and a first neighbor feature whose density is greater than the density of the i-1th feature is determined as the i-th feature of the N features, where N, i is Positive integer and 1 ⁇ i ⁇ N.
  • N, i is Positive integer and 1 ⁇ i ⁇ N.
  • each of the first features is adjusted separately to obtain the second of the plurality of first images.
  • the density chain information can be input into the Long-Short Term Memory (LSTM) for processing, and the dependence between the various features in the density chain information can be learned to obtain a new feature, that is, the density chain information Corresponding to the second feature of the first image, so as to realize the adjustment of the corresponding first feature.
  • LSTM Long-Short Term Memory
  • the second features of the plurality of first images may be clustered to obtain the processing result of the plurality of first images.
  • the processing result may include one or more image groups (or image feature groups) obtained by clustering and the target category corresponding to each image group.
  • the processing result includes the face image group of the same person and the identity of the person.
  • the present disclosure does not limit the specific method of clustering.
  • the density of multiple image features can be determined, the density chain information of the feature can be determined according to the feature density, the feature can be adjusted according to the density chain information, and the adjusted feature can be clustered to obtain the processing result.
  • the spatial density distribution of is adjusted to the features, which can improve the clustering effect of the image.
  • the method further includes: performing feature extraction on the multiple first images respectively to obtain the third features of the multiple first images.
  • each of the first images may be input into, for example, a convolutional neural network for feature extraction, to obtain feature information of each first image, which may be referred to as a third feature.
  • the extracted third feature can be used as the first feature; the extracted third feature can also be preliminarily processed, and the processed feature can be used as the first feature.
  • the present disclosure does not limit the specific method of feature extraction.
  • the feature information of the target in the image can be obtained for subsequent processing.
  • the method further includes:
  • the feature map network including a plurality of nodes and connections between the nodes, each of the nodes includes one of the third features,
  • the value of the line indicates the distance between the node and the neighboring nodes of the node, and the neighboring nodes of the node include K nodes with the smallest distance from the node, and K is a positive integer;
  • Image convolution processing is performed on the feature map network to obtain first features of the multiple first images.
  • the extracted image features can be preliminarily processed through image convolution.
  • the third feature of multiple first images can be mapped to establish a feature map network.
  • the feature graph network includes multiple nodes, and each node is a third feature.
  • the K neighboring nodes closest to the node that is, the smallest distance
  • the connection (or edge) between the node and the K neighboring nodes can be established, and a value can be assigned to each connection.
  • the value of the line can represent the distance (or similarity) between the node and the neighboring nodes of the node.
  • the above processing is performed on each node separately, and a feature graph network can be established, which includes multiple nodes and connections between each node.
  • Those skilled in the art can use various methods in related technologies to determine the neighbor nodes of each node.
  • the present disclosure does not limit the method of determining neighbor nodes and the number K of neighbor nodes.
  • graph convolution can be used to calculate the feature map network, and a feature is recalculated for each node.
  • This feature is a comprehensive feature after fusing neighbor feature information. It can be called the first feature. In this way, first features of multiple first images can be obtained.
  • the present disclosure does not limit the specific calculation method of graph convolution.
  • the density of each first feature can be determined in step S11 according to the distribution of the features in space, that is, each first feature The number of surrounding features within a certain range.
  • the density chain information of the target feature can be obtained.
  • the density chain information includes N features, and the target feature is the first of the N features.
  • the i-th feature of the N features is the feature with the highest density among the first neighboring features of the i-1th feature of the N features. That is, the first neighbor feature of the i-1th feature can be found, including at least one first feature whose distance from the i-1th feature is less than or equal to the second distance threshold; Among the neighbor features, the density is greater than the density of the i-1th feature, and the first nearest neighbor feature with the highest density is determined as the i-th feature of the N features.
  • Fig. 2 shows a schematic diagram of a density chain determination process in an image processing method according to an embodiment of the present disclosure.
  • each circle represents the first feature.
  • the target feature v k its density chain information can be expressed as C(v k ), including a set of first features arranged from low to high density with the target feature v k as a starting point.
  • k represents the feature number and is a positive integer.
  • the density chain information corresponding to the target feature further includes second neighbor features of the N features, and the second neighbor feature of the i-1th feature of the N features includes At least one first feature whose distance from the i-1th feature is less than or equal to the third distance threshold.
  • each feature in the density chain is associated with its nearest neighbors (called the second neighbor feature), and the N features in the density chain and the second neighbor feature of the N features are collectively used as the density chain information .
  • the present disclosure does not limit the specific value of the third distance threshold.
  • Fig. 3 shows a schematic diagram of density chain information in an image processing method according to an embodiment of the present disclosure.
  • the density chain information can be expressed as C(v k ), and the density chain information C(v k ) includes N features And the second nearest neighbor feature of N features
  • step S13 according to the density chain information corresponding to each of the first features, each of the first features is adjusted separately to obtain the second of the plurality of first images.
  • step S13 may include:
  • For the target feature fuse the N features and the second neighbor features of the N features respectively to obtain N fusion features of the target feature;
  • the second feature of the first image corresponding to the target feature is determined.
  • the i-th feature can be fused with the second neighbor feature of the i-th feature, that is, the i-th feature and the i-th feature
  • the second neighbor feature of the feature is directly superimposed (concat), or the i-th feature and the second neighbor feature of the i-th feature are weighted and superimposed (concat) according to a preset weight value to obtain the i-th fused feature.
  • the N fusion features of the target feature can be input into the pre-trained LSTM network for processing, learn the dependencies between the N fusion features, and output the correlation features between the N fusion features (also It can be called query feature Query).
  • query feature Query also It can be called query feature Query.
  • the step of determining the second feature of the first image corresponding to the target feature according to the N fusion features of the target feature and the associated feature may include:
  • the N fusion features are fused to obtain the second feature of the first image corresponding to the target feature.
  • the associated features can be spliced with N fusion features to obtain N splicing features (also called key feature Key); for example, the Softmax function can be used to normalize the N splicing features to obtain A total of N weights are obtained for the weights of each fusion feature; furthermore, according to the weights of each fusion feature, the N fusion features can be weighted averaged to obtain a new feature, that is, the target The feature corresponds to the second feature of the first image, thereby realizing the adjustment process of the target feature. In this way, by performing the above-mentioned processing on each first feature, the second feature of the plurality of first images can be obtained.
  • N splicing features also called key feature Key
  • the Softmax function can be used to normalize the N splicing features to obtain A total of N weights are obtained for the weights of each fusion feature; furthermore, according to the weights of each fusion feature, the N fusion features can be weighted averaged to obtain a new feature, that is,
  • the features can be adjusted according to the spatial density distribution of the features, and the clustering effect of the image can be improved.
  • FIGS. 4a, 4b, 4c, and 4d show schematic diagrams of image processing procedures according to an embodiment of the present disclosure.
  • multiple third features can be obtained, where circles and triangles can respectively represent features of targets of different categories.
  • Figure 4a shows the initial feature distribution. As shown in Figure 4a, the distribution of the third feature is relatively scattered, and the effect of direct clustering is poor.
  • multiple third features can be mapped to obtain a feature map network, which includes multiple nodes and connections between neighboring nodes; after the creation of the graph, graph convolution is used for calculation to achieve local feature fusion , Get multiple first features.
  • Figure 4b shows the feature distribution after the graph convolution process. As shown in Figure 4b, after the graph convolution process, the distance between adjacent first features becomes smaller, which can improve the effect of clustering.
  • pointing marks can be established in the order of density from low to high to form a tree structure, as shown in FIG. 4c. Furthermore, the density chain information of each first feature can be determined.
  • the density chain information of each first feature can be input into the LSTM network, and each first feature can be adjusted to obtain multiple adjusted second features.
  • Figure 4d shows the final feature distribution. As shown in Figure 4d, it can be seen that after adjustment, the distance between the second features of the same category is significantly reduced, which makes clustering easier and can significantly improve the effect of clustering.
  • step S14 may include:
  • the processing result includes the at least one image group and the target category corresponding to the at least one image group.
  • clustering may be used to aggregate the first images including objects in the same category.
  • the second features of the multiple first images may be clustered to determine at least one image group, and each of the image groups includes at least one first image.
  • Those skilled in the art can use any clustering method in the related technology to implement the clustering process, and the present disclosure does not limit this.
  • the target category corresponding to the at least one image group may be determined respectively.
  • the target in the first image is a face or a human body
  • the target category represents the identity of the person in the first image (for example, customer A), and the identity information of the person in each image group can be determined through face recognition.
  • a processing result is finally obtained, and the processing result includes the at least one image group and the target category corresponding to the at least one image group. In this way, images of different people can be distinguished for easy viewing or subsequent analysis and processing.
  • the density-oriented idea is adopted to re-learn the features according to the spatial density distribution of the features, and the features are individually learned and adjusted through the graph convolution and LSTM network, which are both faster and more effective.
  • the existing learning algorithms are better, and solve the problem of poor fine-grainedness of traditional methods and poor overall effect of the algorithm.
  • the method according to the embodiment of the present disclosure can be superimposed with the clustering method in the related art, and has strong scalability. That is, if the process of the clustering method in the related art includes the step of obtaining features -> clustering, the superimposed process includes the step of obtaining features -> feature relearning -> new features -> clustering. After being superimposed, the effect of the clustering method in related technologies can be improved.
  • Application scenarios of the method according to the embodiments of the present disclosure include, but are not limited to, face clustering, general data clustering, etc., which can be applied to fields such as intelligent video analysis, security monitoring, etc., and effectively improve the effect of image analysis and processing.
  • the present disclosure also provides image processing devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any image processing method provided in the present disclosure.
  • image processing devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any image processing method provided in the present disclosure.
  • Fig. 5 shows a block diagram of an image processing device according to an embodiment of the present disclosure. As shown in Fig. 5, the device includes:
  • the density determination module 51 is configured to determine the density of each first feature according to the first features of the multiple first images to be processed, and the density of the first feature represents the distance from the first feature The number of first features that are less than or equal to the first distance threshold;
  • the density chain determination module 52 is configured to determine the density chain information corresponding to the target feature according to the density of the target feature, where the target feature is any one of the first features, and the density chain information corresponding to the target feature includes N features, the i-th feature of the N features is one of the first neighbor features of the i-1th feature of the N features, and the density of the i-th feature is greater than the i-th feature -1 feature density, N, i is a positive integer and 1 ⁇ i ⁇ N, the first neighbor feature includes at least one whose distance from the i-1th feature is less than or equal to the second distance threshold The first feature, the target feature is the first of the N features;
  • the feature adjustment module 53 is configured to respectively adjust each of the first features according to the density chain information corresponding to each of the first features to obtain the second features of the plurality of first images;
  • the result determination module 54 is configured to cluster the second features of the plurality of first images to obtain the processing result of the plurality of first images.
  • the density chain information corresponding to the target feature further includes second neighbor features of the N features
  • the second neighbor feature of the i-1th feature of the N features includes At least one first feature whose distance from the i-1th feature is less than or equal to a third distance threshold
  • the feature adjustment module includes: a fusion sub-module, which is configured to perform an adjustment to the target feature The N features and the second neighbor features of the N features are respectively fused to obtain N fusion features of the target feature; a feature sub-module is used to determine the N fusion features according to the N fusion features of the target feature The associated features between the fusion features; a feature determination sub-module for determining the second feature of the first image corresponding to the target feature according to the N fusion features of the target feature and the associated feature.
  • the feature determination submodule is used to: stitch the associated features with the N fusion features to obtain N stitching features; and normalize the N stitching features According to the N weights, the N fusion features are fused to obtain the second feature of the first image corresponding to the target feature.
  • the device before the density determination module, further includes: a graph network establishing module, configured to establish a feature graph network according to the third feature of the plurality of first images, the feature The graph network includes a plurality of nodes and connections between the nodes, each of the nodes includes one of the third characteristics, and the value of the connection represents the distance between the node and the neighboring nodes of the node
  • the neighboring nodes of the node include K nodes with the smallest distance from the node, and K is a positive integer; the graph convolution module is used to perform graph convolution processing on the feature graph network to obtain the multiple The first feature of the first image.
  • the i-th feature of the N features is the feature with the highest density among the first neighboring features of the i-1th feature of the N features.
  • the device before the graph network establishment module, the device further includes: a feature extraction module, configured to perform feature extraction on the multiple first images to obtain the multiple first images The third feature.
  • a feature extraction module configured to perform feature extraction on the multiple first images to obtain the multiple first images The third feature.
  • the result determination module includes: a clustering submodule, configured to cluster the second features of the plurality of first images, and determine at least one image group, each of the images The group includes at least one first image; the category determination sub-module is used to determine the target category corresponding to the at least one image group, the target category represents the identity of the target in the first image, and the processing result includes all The at least one image group and the target category corresponding to the at least one image group.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the embodiment of the present disclosure also proposes a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.
  • the embodiments of the present disclosure also provide a computer program product, which includes computer-readable code.
  • the processor in the device executes the image processing method for implementing the image processing method provided by any of the above embodiments. instruction.
  • the embodiments of the present disclosure also provide another computer program product for storing computer-readable instructions, which when executed, cause the computer to perform the operations of the image processing method provided by any of the foregoing embodiments.
  • the electronic device can be provided as a terminal, server or other form of device.
  • FIG. 6 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
  • the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
  • the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
  • the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
  • the memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method to operate on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic Disk Magnetic Disk or Optical Disk.
  • the power supply component 806 provides power for various components of the electronic device 800.
  • the power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
  • the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
  • the component is the display and the keypad of the electronic device 800.
  • the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
  • the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
  • the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field-available A programmable gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.
  • FIG. 7 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • the electronic device 1900 may be provided as a server.
  • the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs.
  • the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-described methods.
  • the electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as the instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user's computer through any kind of network-including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the user's computer) connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
  • the computer-readable program instructions are executed to realize various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function.
  • Executable instructions may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the computer program product can be specifically implemented by hardware, software, or a combination thereof.
  • the computer program product is specifically embodied as a computer storage medium.
  • the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc. Wait.
  • SDK software development kit

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Probability & Statistics with Applications (AREA)
  • Human Computer Interaction (AREA)
  • Image Analysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

本公开涉及一种图像处理方法及装置、电子设备和存储介质，所述方法包括：根据待处理的多个第一图像的第一特征，分别确定各第一特征的密度；根据目标特征的密度，确定与目标特征对应的密度链信息，目标特征为任意一个第一特征，与目标特征对应的密度链信息包括N个特征，N个特征的第i个特征为第i-1个特征的第一近邻特征中的一个，且第i个特征的密度大于第i-1个特征的密度；根据与各第一特征对应的密度链信息，分别对各第一特征进行调整，得到多个第一图像的第二特征；对多个第一图像的第二特征进行聚类，得到多个第一图像的处理结果。本公开实施例能够提高图像的聚类效果。

Description

图像处理方法及装置、电子设备和存储介质
本申请要求在2020年2月18日提交中国专利局、申请号为202010098842.0、发明名称为“图像处理方法及装置、电子设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本公开涉及计算机技术领域，尤其涉及一种图像处理方法及装置、电子设备和存储介质。
背景技术
聚类可将属于同一类别的多个目标(例如人脸)聚在一起，例如，可将图像库中属于同一人的图像聚类在一起，从而将不同人的图像区分开。在相关技术中，可提取图像中目标的特征，并对特征进行聚类。
发明内容
本公开提出了一种图像处理技术方案。
根据本公开的一方面，提供了一种图像处理方法，包括：根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度，所述第一特征的密度表示与所述第一特征之间的距离小于或等于第一距离阈值的第一特征的数量；根据目标特征的密度，确定与所述目标特征对应的密度链信息，其中，所述目标特征为任意一个第一特征，与所述目标特征对应的密度链信息包括N个特征，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中的一个，且所述第i个特征的密度大于所述第i-1个特征的密度，N，i为正整数且1＜i≤N，所述第一近邻特征包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征，所述目标特征为所述N个特征中的第一个；根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征；对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。
在一种可能的实现方式中，与所述目标特征对应的密度链信息还包括所述N个特征的第二近邻特征，所述N个特征的第i-1个特征的第二近邻特征包括与所述第i-1个特征之间的距离小于或等于第三距离阈值的至少一个第一特征，所述根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征，包括：针对所述目标特征，对所述N个特征及所述N个特征的第二近邻特征分别进行 融合，得到所述目标特征的N个融合特征；根据所述目标特征的N个融合特征，确定所述N个融合特征之间的关联特征；根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征。
在一种可能的实现方式中，根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征，包括：将所述关联特征分别与所述N个融合特征进行拼接，得到N个拼接特征；对所述N个拼接特征进行归一化，得到所述N个融合特征的N个权值；根据所述N个权值，对所述N个融合特征进行融合，得到与所述目标特征对应的第一图像的第二特征。
在一种可能的实现方式中，所述根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度之前，所述方法还包括：根据所述多个第一图像的第三特征，建立特征图网络，所述特征图网络包括多个节点及所述节点之间的连线，每个所述节点包括一个所述第三特征，所述连线的值表示所述节点与所述节点的近邻节点之间的距离，所述节点的近邻节点包括与所述节点之间的距离最小的K个节点，K为正整数；对所述特征图网络进行图卷积处理，得到所述多个第一图像的第一特征。
在一种可能的实现方式中，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中密度最大的特征。
在一种可能的实现方式中，所述根据所述多个第一图像的第三特征，建立特征图网络之前，所述方法还包括：对所述多个第一图像分别进行特征提取，得到所述多个第一图像的第三特征。
在一种可能的实现方式中，所述对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果，包括：对所述多个第一图像的第二特征进行聚类，确定至少一个图像组，每个所述图像组中包括至少一个第一图像；分别确定所述至少一个图像组对应的目标类别，所述目标类别表示所述第一图像中目标的身份，所述处理结果包括所述至少一个图像组以及所述至少一个图像组对应的目标类别。
根据本公开的一方面，提供了一种图像处理装置，包括：
密度确定模块，用于根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度，所述第一特征的密度表示与所述第一特征之间的距离小于或等于第一距离阈值的第一特征的数量；密度链确定模块，用于根据目标特征的密度，确定与所述目标特征对应的密度链信息，其中，所述目标特征为任意一个第一特征，与所述目标特征对应的密度链信息包括N个特征，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中的一个，且所述第i个特征的密度大于所述第i-1个特征的密度，N，i为正整数且1＜i≤N，所述第一近邻特征包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征，所述目标特征为所述N个特征中的第一个；特征调整模块， 用于根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征；结果确定模块，用于对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。
在一种可能的实现方式中，与所述目标特征对应的密度链信息还包括所述N个特征的第二近邻特征，所述N个特征的第i-1个特征的第二近邻特征包括与所述第i-1个特征之间的距离小于或等于第三距离阈值的至少一个第一特征，所述特征调整模块，包括：融合子模块，用于针对所述目标特征，对所述N个特征及所述N个特征的第二近邻特征分别进行融合，得到所述目标特征的N个融合特征；特征子模块，用于根据所述目标特征的N个融合特征，确定所述N个融合特征之间的关联特征；特征确定子模块，用于根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征。
在一种可能的实现方式中，所述特征确定子模块用于：将所述关联特征分别与所述N个融合特征进行拼接，得到N个拼接特征；对所述N个拼接特征进行归一化，得到所述N个融合特征的N个权值；根据所述N个权值，对所述N个融合特征进行融合，得到与所述目标特征对应的第一图像的第二特征。
在一种可能的实现方式中，所述密度确定模块之前，所述装置还包括：图网络建立模块，用于根据所述多个第一图像的第三特征，建立特征图网络，所述特征图网络包括多个节点及所述节点之间的连线，每个所述节点包括一个所述第三特征，所述连线的值表示所述节点与所述节点的近邻节点之间的距离，所述节点的近邻节点包括与所述节点之间的距离最小的K个节点，K为正整数；图卷积模块，用于对所述特征图网络进行图卷积处理，得到所述多个第一图像的第一特征。
在一种可能的实现方式中，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中密度最大的特征。
在一种可能的实现方式中，所述图网络建立模块之前，所述装置还包括：特征提取模块，用于对所述多个第一图像分别进行特征提取，得到所述多个第一图像的第三特征。
在一种可能的实现方式中，所述结果确定模块包括：聚类子模块，用于对所述多个第一图像的第二特征进行聚类，确定至少一个图像组，每个所述图像组中包括至少一个第一图像；类别确定子模块，用于分别确定所述至少一个图像组对应的目标类别，所述目标类别表示所述第一图像中目标的身份，所述处理结果包括所述至少一个图像组以及所述至少一个图像组对应的目标类别。
根据本公开的一方面，提供了一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
根据本公开的一方面，提供了一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。
根据本公开的一方面，提供了一种计算机程序，所述计算机程序包括计算机可读代码，当所述计算机可读代码在电子设备中运行时，所述电子设备中的处理器执行上述方法。
根据本公开的实施例，能够确定多个图像特征的密度，根据特征密度确定特征的密度链信息，根据密度链信息对特征进行调整，对调整后的特征进行聚类以得到处理结果，通过特征的空间密度分布对特征进行调整，能够提高图像的聚类效果。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，这些附图示出了符合本公开的实施例，并与说明书一起用于说明本公开的技术方案。
图1示出根据本公开实施例的图像处理方法的流程图。
图2示出根据本公开实施例的图像处理方法中的密度链确定过程的示意图。
图3示出根据本公开实施例的图像处理方法中的密度链信息的示意图。
图4a、图4b、图4c及图4d示出根据本公开实施例的图像处理过程的示意图。
图5示出根据本公开实施例的图像处理装置的框图。
图6示出根据本公开实施例的一种电子设备的框图。
图7示出根据本公开实施例的一种电子设备的框图。
具体实施方式
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意 一个或多个元素。
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
图1示出根据本公开实施例的图像处理方法的流程图，如图1所示，所述方法包括：
在步骤S11中，根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度，所述第一特征的密度表示与所述第一特征之间的距离小于或等于第一距离阈值的第一特征的数量；
在步骤S12中，根据目标特征的密度，确定与所述目标特征对应的密度链信息，其中，所述目标特征为任意一个第一特征，与所述目标特征对应的密度链信息包括N个特征，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中的一个，且所述第i个特征的密度大于所述第i-1个特征的密度，N，i为正整数且1＜i≤N，所述第一近邻特征包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征，所述目标特征为所述N个特征中的第一个；
在步骤S13中，根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征；
在步骤S14中，对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。
在一种可能的实现方式中，所述图像处理方法可以由终端设备或服务器等电子设备执行，终端设备可以为用户设备(User Equipment，UE)、移动设备、用户终端、终端、蜂窝电话、无绳电话、个人数字处理(Personal Digital Assistant，PDA)、手持设备、计算设备、车载设备、可穿戴设备等，所述方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。或者，可通过服务器执行所述方法。
在一种可能的实现方式中，待处理的多个第一图像可以是由图像采集设备(例如摄像头)采集的图像，或者从采集图像中截取的局部图像等。第一图像中包括待识别的目标(例如人脸、人体、车辆等)。其中，多个第一图像中的目标可能为同一类别的目标(例如同一个人的人脸)，因此可通过聚类将同一类别的目标聚在一起，以便于后续处理。本公开对第一图像的获取方式以及第一图像中目标的具体类型不作限制。
在一种可能的实现方式中，可例如通过卷积神经网络提取多个第一图像中的特征信息，将提取到的特征信息作为第一特征；也可对提取到的特征信息进行初步处理，将处理后的特征信息作为第一特征。本公开对第一特征的获取方式以及用于提取特征的卷积神经网络的类型不作限制。
在一种可能的实现方式中，在步骤S11中，可根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度。第一特征的密度与该第一特征之间的距离小于或等于第一距离阈值的第一特征的数量。也就是说，可根据特征在空间中的分布，确定出每个第一特征的一定范围内周围特征的个数，作为每个第一特征所处位置的密度。本领域技术人员可根据实际情况设定第一距离阈值的具体取值，本公开对此不作限制。
在一种可能的实现方式中，在步骤S12中，对于多个第一特征中的任意一个(可称为目标特征)，根据该目标特征的密度，可寻找该目标特征周围一个密度较大的第一特征(大于目标特征的密度)，或大于目标特征的密度的第一特征中密度最大的第一特征，并建立一个指向该第一特征的标记。对于每个第一特征分别进行上述处理，可形成一个树状结构。可对每个第一特征顺着树状结构找到密度最大的一个第一特征，这样可寻找得到一条密度链，称为密度链信息。
在一种可能的实现方式中，对于目标特征，可确定出与该目标特征对应的密度链信息。设该密度链信息包括N个特征，则目标特征为N个特征中的第一个。可寻找到目标特征的第一近邻特征，包括与该目标特征之间的距离小于或等于第二距离阈值的第一特征，如果各个第一近邻特征的密度均小于或等于目标特征的密度，则N＝1，也即与该目标特征对应的密度链信息包括目标特征本身。如果存在密度大于目标特征的密度的第一近邻特征，则将该第一近邻特征作为密度链信息中的下一个特征。本公开对第二距离阈值的具体取值不作限制。
在一种可能的实现方式中，对于N个特征的第i-1个特征，可寻找到第i-1个特征的第一近邻特征，包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征；并将密度大于所述第i-1个特征的密度的一个第一近邻特征，确定为N个特征的第i个特征，N，i为正整数且1＜i≤N。以此类推，可得到所有的N个特征，也即得到与该目标特征对应的密度链信息。
在一种可能的实现方式中，在步骤S13中，根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征。可例如将密度链信息输入长短期记忆网络(Long-Short Term Memory，LSTM)中处理，学习密度链信息中的各个特征之间的依赖关系，得到一个新的特征，也即与该密度链信息对应的第一图像的第二特征，从而实现对相应的第一特征的调整。
在一种可能的实现方式中，在步骤S14中，可对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。该处理结果可包括聚类得到的一个或多个图像组(或图像特征组)以及各个图像组对应的目标类别。例如在第一图像为人脸图像时，处理结果包括同一人物的人脸图像组及该人物的身份。本公开对聚类的具体方式不作限制。
根据本公开的实施例，能够确定多个图像特征的密度，根据特征密度确定特征的密度链信息，根据密度链信息对特征进行调整，对调整后的特征进行聚类以得到处理结果，通过特征的空间密度分布对特征进行调整，能够提高图像的聚类效果。
在一种可能的实现方式中，在步骤S11之前，所述方法还包括：对所述多个第一图像分别进行特征提取，得到所述多个第一图像的第三特征。
举例来说，针对待处理的多个第一图像，可将各个第一图像分别输入例如卷积神经网络中进行特征提取，得到各个第一图像的特征信息，可称为第三特征。可将提取到的第三特征作为第一特征；也可对提取到的第三特征进行初步处理，将处理后的特征作为第一特征。本公开对特征提取的具体方式不作限制。
通过这种方式，可以得到图像中目标的特征信息，以便后续处理。
在一种可能的实现方式中，在提取到第三特征后，在步骤S11之前，所述方法还包括：
根据所述多个第一图像的第三特征，建立特征图网络，所述特征图网络包括多个节点及所述节点之间的连线，每个所述节点包括一个所述第三特征，所述连线的值表示所述节点与所述节点的近邻节点之间的距离，所述节点的近邻节点包括与所述节点之间的距离最小的K个节点，K为正整数；
对所述特征图网络进行图卷积处理，得到所述多个第一图像的第一特征。
举例来说，可以通过图卷积对提取到的图像特征进行初步处理。可对多个第一图像的第三特征进行建图，建立特征图网络。该特征图网络包括多个节点，每个节点即为一个第三特征。对于每个节点，可寻找与该节点最近(也即距离最小)的K个近邻节点，建立该节点与K个近邻节点之间的连线(或称为边)，并为各个连线赋值。连线的值可表示该节点与该节点的近邻节点之间的距离(或相似度)。对各个节点分别进行上述处理，可得到建立特征图网络，其包括多个节点及各个节点之间的连线。本领域技术人员可采用相关技术中的各种方式确定各个节点的近邻节点，本公开对确定近邻节点的方式及近邻节点的数量K不作限制。
在一种可能的实现方式中，在建立特征图网络后，可采用图卷积对特征图网络进行计算，对每个节点重新计算一个特征，该特征是融合了邻居特征信息后的综合特征，可称为第一特征。这样，可以得到多个第一图像的第一特征。本公开对图卷积的具体计算方式不作限制。
通过这种方式，可以融合各特征周围较接近的邻居特征的信息，实现局部的特征融合，从而提高后续聚类处理的效果。
在一种可能的实现方式中，在得到多个第一图像的第一特征后，可根据特征在空间 中的分布，在步骤S11中确定各个第一特征的密度，也即每个第一特征的一定范围内周围特征的个数。在步骤S12中，对于多个第一特征中的任意一个(称为目标特征)，可获取该目标特征的密度链信息。该密度链信息包括N个特征，该目标特征为N个特征中的第一个。
在一种可能的实现方式中，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中密度最大的特征。也就是说，可寻找到第i-1个特征的第一近邻特征，包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征；将第一近邻特征中密度大于第i-1个特征的密度，且密度最大的第一近邻特征，确定为N个特征的第i个特征。
图2示出根据本公开实施例的图像处理方法中的密度链确定过程的示意图。如图2所示，各个圆圈表示第一特征，圆圈的颜色越深表示特征的密度越大，圆圈的颜色越深表示特征的密度越小。对于任意一个第一特征，也即目标特征v k，其密度链信息可表示为C(v k)，包括以目标特征v k为起点，密度由低到高排列的一组第一特征。k表示特征编号，为正整数。
在一种可能的实现方式中，与所述目标特征对应的密度链信息还包括所述N个特征的第二近邻特征，所述N个特征的第i-1个特征的第二近邻特征包括与所述第i-1个特征之间的距离小于或等于第三距离阈值的至少一个第一特征。也就是说，密度链中的每个特征都关联其最近的几个邻居(称为第二近邻特征)，将密度链中的N个特征以及N个特征的第二近邻特征共同作为密度链信息。本公开对第三距离阈值的具体取值不作限制。
图3示出根据本公开实施例的图像处理方法中的密度链信息的示意图。如图3所示，对于目标特征v k，密度链信息可表示为C(v k)，密度链信息C(v k)包括N个特征
Figure PCTCN2020081364-appb-000001
以及N个特征的第二近邻特征
Figure PCTCN2020081364-appb-000002
在一种可能的实现方式中，在步骤S13中，根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征。其中，步骤S13可包括：
针对所述目标特征，对所述N个特征及所述N个特征的第二近邻特征分别进行融合，得到所述目标特征的N个融合特征；
根据所述目标特征的N个融合特征，确定所述N个融合特征之间的关联特征；
根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征。
举例来说，对于目标特征的密度链信息中的第i个特征，可将该第i个特征与该第i个 特征的第二近邻特征进行融合，也即将第i个特征与该第i个特征的第二近邻特征直接叠加(concat)，或根据预设的权重值对第i个特征与该第i个特征的第二近邻特征进行加权叠加(concat)，得到第i个融合特征。对N个特征中的每一个特征都这样处理，可得到N个融合特征。
在一种可能的实现方式中，可将目标特征的N个融合特征输入预先训练的LSTM网络中处理，学习N个融合特征之间的依赖关系，输出N个融合特征之间的关联特征(也可称为查询特征Query)。本领域技术人员可根据实际情况设置LSTM网络，本公开对LSTM网络的网络结构不作限制。
在一种可能的实现方式中，根据目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征的步骤可包括：
将所述关联特征分别与所述N个融合特征进行拼接，得到N个拼接特征；
对所述N个拼接特征进行归一化，得到所述N个融合特征的N个权值；
根据所述N个权值，对所述N个融合特征进行融合，得到与所述目标特征对应的第一图像的第二特征。
也就是说，可将关联特征分别与N个融合特征进行拼接，得到N个拼接特征(也可称为关键特征Key)；通过例如Softmax函数分别对N个拼接特征进行归一化处理，可得到每个融合特征的权值，共得到N个权值；进而，可根据各个融合特征的权值，对N个融合特征进行加权平均(weighted average)，得到一个新的特征，也即与该目标特征对应的第一图像的第二特征，从而实现对目标特征的调整过程。这样，对每个第一特征进行上述处理，可得到所述多个第一图像的第二特征。
通过这种方式，能够根据特征的空间密度分布对特征进行调整，提高图像的聚类效果。
图4a、图4b、图4c及图4d示出根据本公开实施例的图像处理过程的示意图。在示例中，对多个第一图像进行特征提取后，可得到多个第三特征，其中圆圈和三角可分别表示不同类别的目标的特征。图4a示出了初始的特征分布情况，如图4a所示，第三特征的分布较为分散，直接聚类时的效果较差。
在示例中，可对多个第三特征进行建图，得到特征图网络，其包括多个节点及近邻节点之间的连线；图建立完成后使用图卷积进行计算，实现局部的特征融合，得到多个第一特征。图4b示出了经图卷积处理后的特征分布情况，如图4b所示，经图卷积处理后，邻近的第一特征之间的距离变小，能够提高聚类的效果。
在示例中，可根据各个第一特征的密度，按照密度由低到高的顺序建立指向标记，形成树状结构，如图4c所示。进而，可确定出每个第一特征的密度链信息。
在示例中，可将各个第一特征的密度链信息分别输入LSTM网络，对各个第一特征进 行调整，得到调整后的多个第二特征。图4d示出了最终的特征分布情况，如图4d所示，可见经调整后，同一类别的第二特征之间的距离明显变小，更容易聚类，能够显著提高聚类的效果。
在一种可能的实现方式中，在完成特征调整(也可称为特征重学习)后，可在步骤S14中对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。其中，步骤S14可包括：
对所述多个第一图像的第二特征进行聚类，确定至少一个图像组，每个所述图像组中包括至少一个第一图像；
分别确定所述至少一个图像组对应的目标类别，所述目标类别表示所述第一图像中目标的身份，
所述处理结果包括所述至少一个图像组以及所述至少一个图像组对应的目标类别。
举例来说，可通过聚类将包括同一类别的目标的第一图像聚合在一起。可对多个第一图像的第二特征进行聚类，确定至少一个图像组，每个所述图像组中包括至少一个第一图像。本领域技术人员可采用相关技术中的任意聚类方式实现该聚类过程，本公开对此不作限制。
在一种可能的实现方式中，可分别确定所述至少一个图像组对应的目标类别。在第一图像中的目标为人脸或人体时，目标类别表示第一图像中的人的身份(例如为顾客A)，可通过人脸识别确定各个图像组中人物的身份信息。这样，经聚类及识别后，最终得到处理结果，该处理结果包括所述至少一个图像组以及所述至少一个图像组对应的目标类别。通过这种方式，可以将不同人的图像区分开，便于查看或进行后续的分析处理。
根据本公开实施例的方法，采用密度导向的思路，根据特征的空间密度分布对特征进行重学习，通过图卷积和LSTM网络对特征进行个性化的学习和调整，在速度与效果上均比已有的学习算法要更好，解决了传统方法细粒度差，算法总体效果不好的问题。
根据本公开实施例的方法，能够与相关技术中的聚类方法进行叠加，具有较强的可扩展性。也即，如果相关技术中的聚类方法的流程包括获得特征->聚类的步骤，则叠加后的流程包括获得特征->特征重学习->新特征->聚类的步骤。经叠加后，能够提高相关技术中的聚类方法的效果。
根据本公开实施例的方法的应用场景包括但不限于人脸聚类，一般数据聚类等，能够应用于智能视频分析，安防监控等领域，有效提高图像的分析处理效果。
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的具体执行顺序应当以其功能和可能的 内在逻辑确定。
此外，本公开还提供了图像处理装置、电子设备、计算机可读存储介质、程序，上述均可用来实现本公开提供的任一种图像处理方法，相应技术方案和描述和参见方法部分的相应记载，不再赘述。
图5示出根据本公开实施例的图像处理装置的框图，如图5所示，所述装置包括：
密度确定模块51，用于根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度，所述第一特征的密度表示与所述第一特征之间的距离小于或等于第一距离阈值的第一特征的数量；
密度链确定模块52，用于根据目标特征的密度，确定与所述目标特征对应的密度链信息，其中，所述目标特征为任意一个第一特征，与所述目标特征对应的密度链信息包括N个特征，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中的一个，且所述第i个特征的密度大于所述第i-1个特征的密度，N，i为正整数且1＜i≤N，所述第一近邻特征包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征，所述目标特征为所述N个特征中的第一个；
特征调整模块53，用于根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征；
结果确定模块54，用于对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。
在一种可能的实现方式中，与所述目标特征对应的密度链信息还包括所述N个特征的第二近邻特征，所述N个特征的第i-1个特征的第二近邻特征包括与所述第i-1个特征之间的距离小于或等于第三距离阈值的至少一个第一特征，所述特征调整模块，包括：融合子模块，用于针对所述目标特征，对所述N个特征及所述N个特征的第二近邻特征分别进行融合，得到所述目标特征的N个融合特征；特征子模块，用于根据所述目标特征的N个融合特征，确定所述N个融合特征之间的关联特征；特征确定子模块，用于根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征。
在一种可能的实现方式中，所述特征确定子模块用于：将所述关联特征分别与所述N个融合特征进行拼接，得到N个拼接特征；对所述N个拼接特征进行归一化，得到所述N个融合特征的N个权值；根据所述N个权值，对所述N个融合特征进行融合，得到与所述目标特征对应的第一图像的第二特征。
在一种可能的实现方式中，所述密度确定模块之前，所述装置还包括：图网络建立模块，用于根据所述多个第一图像的第三特征，建立特征图网络，所述特征图网络包括多个节点及所述节点之间的连线，每个所述节点包括一个所述第三特征，所述连线的值 表示所述节点与所述节点的近邻节点之间的距离，所述节点的近邻节点包括与所述节点之间的距离最小的K个节点，K为正整数；图卷积模块，用于对所述特征图网络进行图卷积处理，得到所述多个第一图像的第一特征。
在一种可能的实现方式中，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中密度最大的特征。
在一种可能的实现方式中，所述图网络建立模块之前，所述装置还包括：特征提取模块，用于对所述多个第一图像分别进行特征提取，得到所述多个第一图像的第三特征。
在一种可能的实现方式中，所述结果确定模块包括：聚类子模块，用于对所述多个第一图像的第二特征进行聚类，确定至少一个图像组，每个所述图像组中包括至少一个第一图像；类别确定子模块，用于分别确定所述至少一个图像组对应的目标类别，所述目标类别表示所述第一图像中目标的身份，所述处理结果包括所述至少一个图像组以及所述至少一个图像组对应的目标类别。
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法，其具体实现可以参照上文方法实施例的描述，为了简洁，这里不再赘述。
本公开实施例还提出一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是非易失性计算机可读存储介质或易失性计算机可读存储介质。
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
本公开实施例还提供了一种计算机程序产品，包括计算机可读代码，当计算机可读代码在设备上运行时，设备中的处理器执行用于实现如上任一实施例提供的图像处理方法的指令。
本公开实施例还提供了另一种计算机程序产品，用于存储计算机可读指令，指令被执行时使得计算机执行上述任一实施例提供的图像处理方法的操作。
电子设备可以被提供为终端、服务器或其它形态的设备。
图6示出根据本公开实施例的一种电子设备800的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等终端。
参照图6，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的接口812，传感器组件814，以及通信组件816。
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信， 相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在电子设备800的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理系统，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。
多媒体组件808包括在所述电子设备800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如所述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800接触的存在或不存在，电子设 备800方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。电子设备800可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上述方法。
图7示出根据本公开实施例的一种电子设备1900的框图。例如，电子设备1900可以被提供为一服务器。参照图7，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理，一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以完成上述方法。
本公开可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设 备。计算机可读存储介质例如可以是――但不限于――电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功 能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中，所述计算机程序产品具体体现为计算机存储介质，在另一个可选实施例中，计算机程序产品具体体现为软件产品，例如软件开发包(Software Development Kit，SDK)等等。
在不违背逻辑的情况下，本公开不同实施例之间可以相互结合，不同实施例描述有所侧重，为侧重描述的部分可以参见其他实施例的记载。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (11)

  1. 一种图像处理方法，其特征在于，包括：
    根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度，所述第一特征的密度表示与所述第一特征之间的距离小于或等于第一距离阈值的第一特征的数量；
    根据目标特征的密度，确定与所述目标特征对应的密度链信息，其中，所述目标特征为任意一个第一特征，与所述目标特征对应的密度链信息包括N个特征，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中的一个，且所述第i个特征的密度大于所述第i-1个特征的密度，N，i为正整数且1＜i≤N，所述第一近邻特征包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征，所述目标特征为所述N个特征中的第一个；
    根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征；
    对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。
  2. 根据权利要求1所述的方法，其特征在于，与所述目标特征对应的密度链信息还包括所述N个特征的第二近邻特征，所述N个特征的第i-1个特征的第二近邻特征包括与所述第i-1个特征之间的距离小于或等于第三距离阈值的至少一个第一特征，
    所述根据与各个所述第一特征对应的密度链信息，分别对各个所述第一特征进行调整，得到所述多个第一图像的第二特征，包括：
    针对所述目标特征，对所述N个特征及所述N个特征的第二近邻特征分别进行融合，得到所述目标特征的N个融合特征；
    根据所述目标特征的N个融合特征，确定所述N个融合特征之间的关联特征；
    根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征。
  3. 根据权利要求2所述的方法，其特征在于，根据所述目标特征的N个融合特征以及所述关联特征，确定与所述目标特征对应的第一图像的第二特征，包括：
    将所述关联特征分别与所述N个融合特征进行拼接，得到N个拼接特征；
    对所述N个拼接特征进行归一化，得到所述N个融合特征的N个权值；
    根据所述N个权值，对所述N个融合特征进行融合，得到与所述目标特征对应的第一图像的第二特征。
  4. 根据权利要求1-3中任意一项所述的方法，其特征在于，所述根据待处理的多个 第一图像的第一特征，分别确定各个所述第一特征的密度之前，所述方法还包括：
    根据所述多个第一图像的第三特征，建立特征图网络，所述特征图网络包括多个节点及所述节点之间的连线，每个所述节点包括一个所述第三特征，所述连线的值表示所述节点与所述节点的近邻节点之间的距离，所述节点的近邻节点包括与所述节点之间的距离最小的K个节点，K为正整数；
    对所述特征图网络进行图卷积处理，得到所述多个第一图像的第一特征。
  5. 根据权利要求1-4中任意一项所述的方法，其特征在于，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中密度最大的特征。
  6. 根据权利要求4所述的方法，其特征在于，所述根据所述多个第一图像的第三特征，建立特征图网络之前，所述方法还包括：
    对所述多个第一图像分别进行特征提取，得到所述多个第一图像的第三特征。
  7. 根据权利要求1-6中任意一项所述的方法，其特征在于，所述对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果，包括：
    对所述多个第一图像的第二特征进行聚类，确定至少一个图像组，每个所述图像组中包括至少一个第一图像；
    分别确定所述至少一个图像组对应的目标类别，所述目标类别表示所述第一图像中目标的身份，
    所述处理结果包括所述至少一个图像组以及所述至少一个图像组对应的目标类别。
  8. 一种图像处理装置，其特征在于，包括：
    密度确定模块，用于根据待处理的多个第一图像的第一特征，分别确定各个所述第一特征的密度，所述第一特征的密度表示与所述第一特征之间的距离小于或等于第一距离阈值的第一特征的数量；
    密度链确定模块，用于根据目标特征的密度，确定与所述目标特征对应的密度链信息，其中，所述目标特征为任意一个第一特征，与所述目标特征对应的密度链信息包括N个特征，所述N个特征的第i个特征为所述N个特征的第i-1个特征的第一近邻特征中的一个，且所述第i个特征的密度大于所述第i-1个特征的密度，N，i为正整数且1＜i≤N，所述第一近邻特征包括与所述第i-1个特征之间的距离小于或等于第二距离阈值的至少一个第一特征，所述目标特征为所述N个特征中的第一个；
    特征调整模块，用于根据与各个所述第一特征对应的密度链信息，分别对各个所述 第一特征进行调整，得到所述多个第一图像的第二特征；
    结果确定模块，用于对所述多个第一图像的第二特征进行聚类，得到所述多个第一图像的处理结果。
  9. 一种电子设备，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为调用所述存储器存储的指令，以执行权利要求1至7中任意一项所述的方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序指令，其特征在于，所述计算机程序指令被处理器执行时实现权利要求1至7中任意一项所述的方法。
  11. 一种计算机程序，其特征在于，所述计算机程序包括计算机可读代码，当所述计算机可读代码在电子设备中运行时，所述电子设备中的处理器执行用于实现权利要求1至7中任意一项所述的方法。
PCT/CN2020/081364 2020-02-18 2020-03-26 图像处理方法及装置、电子设备和存储介质 WO2021164100A1 (zh)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2021526214A JP7114811B2 (ja) 2020-02-18 2020-03-26 画像処理方法及び装置、電子機器並びに記憶媒体
SG11202105513VA SG11202105513VA (en) 2020-02-18 2020-03-26 Image processing method and apparatus, electronic device and storage medium
US17/328,432 US20210279508A1 (en) 2020-02-18 2021-05-24 Image processing method, apparatus and storage medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010098842.0A CN111310664B (zh) 2020-02-18 2020-02-18 图像处理方法及装置、电子设备和存储介质
CN202010098842.0 2020-02-18

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US17/328,432 Continuation US20210279508A1 (en) 2020-02-18 2021-05-24 Image processing method, apparatus and storage medium

Publications (1)

Publication Number Publication Date
WO2021164100A1 true WO2021164100A1 (zh) 2021-08-26

Family

ID=71158298

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/081364 WO2021164100A1 (zh) 2020-02-18 2020-03-26 图像处理方法及装置、电子设备和存储介质

Country Status (6)

Country Link
US (1) US20210279508A1 (zh)
JP (1) JP7114811B2 (zh)
CN (1) CN111310664B (zh)
SG (1) SG11202105513VA (zh)
TW (1) TWI738349B (zh)
WO (1) WO2021164100A1 (zh)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111310664B (zh) * 2020-02-18 2022-11-22 深圳市商汤科技有限公司 图像处理方法及装置、电子设备和存储介质
CN115552477A (zh) * 2020-05-01 2022-12-30 奇跃公司 采用施加的分层归一化的图像描述符网络
US11860977B1 (en) * 2021-05-04 2024-01-02 Amazon Technologies, Inc. Hierarchical graph neural networks for visual clustering
CN113706367B (zh) * 2021-08-26 2024-05-17 北京市商汤科技开发有限公司 节点排布方式确定方法及装置、电子设备和存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20100116404A (ko) * 2009-04-22 2010-11-01 계명대학교 산학협력단 영상정보로부터 독립세포와 군집세포를 분류하는 방법 및 장치
CN108776787A (zh) * 2018-06-04 2018-11-09 北京京东金融科技控股有限公司 图像处理方法及装置、电子设备、存储介质
CN110135295A (zh) * 2019-04-29 2019-08-16 华南理工大学 一种基于迁移学习的无监督行人重识别方法
CN110781975A (zh) * 2019-10-31 2020-02-11 深圳市商汤科技有限公司 图像处理方法及装置、电子设备和存储介质
CN111310664A (zh) * 2020-02-18 2020-06-19 深圳市商汤科技有限公司 图像处理方法及装置、电子设备和存储介质

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5174068B2 (ja) * 2010-03-11 2013-04-03 株式会社東芝 信号分類装置
CN103207879B (zh) * 2012-01-17 2016-03-30 阿里巴巴集团控股有限公司 图像索引的生成方法及设备
DE102013012780A1 (de) * 2013-07-31 2015-02-05 Connaught Electronics Ltd. Verfahren zum Detektieren eines Zielobjekts durch Clusterbildung aus charakteristischen Merkmalen eines Bilds, Kamerasystem und Kraftfahrzeug
JP2015184061A (ja) * 2014-03-20 2015-10-22 株式会社東芝 抽出装置、方法及びプログラム
CN106874923A (zh) * 2015-12-14 2017-06-20 阿里巴巴集团控股有限公司 一种商品的风格分类确定方法及装置
CN108920580B (zh) * 2018-06-25 2020-05-26 腾讯科技（深圳）有限公司 图像匹配方法、装置、存储介质及终端
CN109801260B (zh) * 2018-12-20 2021-01-26 北京海益同展信息科技有限公司 牲畜个数的识别方法、装置、控制装置及可读存储介质
CN110348521A (zh) * 2019-07-12 2019-10-18 创新奇智(重庆)科技有限公司 图像处理聚类方法及其系统、电子设备

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20100116404A (ko) * 2009-04-22 2010-11-01 계명대학교 산학협력단 영상정보로부터 독립세포와 군집세포를 분류하는 방법 및 장치
CN108776787A (zh) * 2018-06-04 2018-11-09 北京京东金融科技控股有限公司 图像处理方法及装置、电子设备、存储介质
CN110135295A (zh) * 2019-04-29 2019-08-16 华南理工大学 一种基于迁移学习的无监督行人重识别方法
CN110781975A (zh) * 2019-10-31 2020-02-11 深圳市商汤科技有限公司 图像处理方法及装置、电子设备和存储介质
CN111310664A (zh) * 2020-02-18 2020-06-19 深圳市商汤科技有限公司 图像处理方法及装置、电子设备和存储介质

Also Published As

Publication number Publication date
JP2022524254A (ja) 2022-05-02
SG11202105513VA (en) 2021-09-29
TWI738349B (zh) 2021-09-01
JP7114811B2 (ja) 2022-08-08
CN111310664B (zh) 2022-11-22
TW202133030A (zh) 2021-09-01
CN111310664A (zh) 2020-06-19
US20210279508A1 (en) 2021-09-09

Similar Documents

Publication Publication Date Title
WO2021155632A1 (zh) 图像处理方法及装置、电子设备和存储介质
WO2021196401A1 (zh) 图像重建方法及装置、电子设备和存储介质
TWI749423B (zh) 圖像處理方法及裝置、電子設備和電腦可讀儲存介質
US20210326708A1 (en) Neural network training method and apparatus, and image processing method and apparatus
WO2021164100A1 (zh) 图像处理方法及装置、电子设备和存储介质
WO2021164469A1 (zh) 目标对象的检测方法、装置、设备和存储介质
WO2021056808A1 (zh) 图像处理方法及装置、电子设备和存储介质
WO2021031609A1 (zh) 活体检测方法及装置、电子设备和存储介质
WO2021008022A1 (zh) 图像处理方法及装置、电子设备和存储介质
TW202029055A (zh) 一種行人識別方法、裝置、電子設備及非臨時性電腦可讀儲存介質
WO2021031645A1 (zh) 图像处理方法及装置、电子设备和存储介质
WO2021036382A1 (zh) 图像处理方法及装置、电子设备和存储介质
CN109934275B (zh) 图像处理方法及装置、电子设备和存储介质
WO2016011741A1 (zh) 自动连接无线局域网的方法及装置
WO2019237870A1 (zh) 目标匹配方法及装置、电子设备和存储介质
CN110532956B (zh) 图像处理方法及装置、电子设备和存储介质
CN111259967B (zh) 图像分类及神经网络训练方法、装置、设备及存储介质
TW202105202A (zh) 影片處理方法及裝置、電子設備、儲存媒體和電腦程式
WO2020147414A1 (zh) 网络优化方法及装置、图像处理方法及装置、存储介质
CN110659690B (zh) 神经网络的构建方法及装置、电子设备和存储介质
WO2020220807A1 (zh) 图像生成方法及装置、电子设备及存储介质
WO2020192113A1 (zh) 图像处理方法及装置、电子设备和存储介质
CN111242303A (zh) 网络训练方法及装置、图像处理方法及装置
WO2021061045A2 (zh) 堆叠物体的识别方法及装置、电子设备和存储介质
WO2021227426A1 (zh) 确定性能参数的方法及装置、设备、存储介质和程序产品

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2021526214

Country of ref document: JP

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20919895

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 13.12.2022)

122 Ep: pct application non-entry in european phase

Ref document number: 20919895

Country of ref document: EP

Kind code of ref document: A1