CN111339874A - Single-stage face segmentation method - Google Patents

Single-stage face segmentation method Download PDF

Info

Publication number
CN111339874A
CN111339874A CN202010100612.3A CN202010100612A CN111339874A CN 111339874 A CN111339874 A CN 111339874A CN 202010100612 A CN202010100612 A CN 202010100612A CN 111339874 A CN111339874 A CN 111339874A
Authority
CN
China
Prior art keywords
mask
module
grid
layer
image
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
CN202010100612.3A
Other languages
Chinese (zh)
Inventor
余孟春
谢清禄
王显飞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Melux Information Technology Co ltd
Original Assignee
Guangzhou Melux Information Technology Co ltd
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 Guangzhou Melux Information Technology Co ltd filed Critical Guangzhou Melux Information Technology Co ltd
Priority to CN202010100612.3A priority Critical patent/CN111339874A/en
Publication of CN111339874A publication Critical patent/CN111339874A/en
Pending legal-status Critical Current

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
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of 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
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Image Analysis (AREA)

Abstract

The invention relates to the technical field of image processing, in particular to a single-stage face segmentation method. A method for single-stage face segmentation comprises the steps of firstly, uniformly gridding an input face image by using a Grid module, providing gridding class labels for classification branches, and providing reference masks for mask branches; extracting basic features of the gridded face image by using an FCN module; fusing the three-level characteristics output by the FCN module by using an JFU module to obtain richer characteristic information; processing the output of the JFU module by using a Category module to realize the function of classifying branches, predicting whether each grid image has a face part, and if the face part exists, giving a corresponding grid position, Category and confidence coefficient to provide reference information for mask branches; and processing the output of the JFU module by using a Mask module, acquiring a segmentation Mask of a human face part, and predicting a Mask image corresponding to each grid.

Description

Single-stage face segmentation method
Technical Field
The invention relates to the technical field of image processing, in particular to a single-stage face segmentation method.
Background
The face segmentation technology is mainly used for positioning and segmenting face parts such as eyes, a nose, lips and the like, and is an important component of the face recognition technology. The general face segmentation method adopts a Two-Stage (Two Stage) mode, i.e. a technical scheme of firstly detecting and then segmenting, for example, adopting Mask-RCNN to segment the face. Firstly, detecting the boundary frame of each part in the face image, then intercepting the image in the boundary frame, and segmenting the mask of the face part. The human face segmentation mode divides a task into two subtasks, namely detection and segmentation, each subtask is independently completed, and the detection effect of each part of the human face directly influences the subsequent segmentation precision. Meanwhile, the mode of firstly detecting and then dividing weakens the global context information of all parts of the human face, and cannot achieve good adaptive effect on human face shielding and complex human face postures.
The invention discloses a method for single-Stage Face Segmentation, which can complete the positioning and Segmentation of each part of a Face only by a One Stage network. The method only needs the example mask label during training, does not need the bounding box information of the example, can learn from end to end, simplifies the training of the model and achieves better segmentation effect. The method comprises the steps that a segmentation network is divided into two branches, wherein one branch is used for predicting whether a human face part exists at a certain position in an image, and if the human face part exists at the certain position in the image, the position and the category are given and are called as classification branches; the other branch is used for generating a mask of the human face part and is called a mask branch.
The relationship of space consistency exists among all parts of the human face, for example, two eyes are respectively positioned at the upper left and the upper right of a nose, lips are positioned below the nose, the relationship cannot change along with the posture and the position of the human face, and a certain distance exists among the central points of all the parts. Therefore, the segmentation method disclosed by the invention is very suitable for face segmentation, and the face segmentation effect which is simpler and faster than that of the traditional two-stage segmentation method and has higher segmentation precision can be achieved by combining the two branch networks.
Disclosure of Invention
Based on the background, the invention provides a single-stage face segmentation method to improve the speed and accuracy of face segmentation.
In order to achieve the above object, the method for single-stage face segmentation provided by the present invention specifically comprises the following implementation steps:
step 1, firstly, uniformly gridding an input face image by using a Grid module, providing gridding class labels for classification branches, and providing reference masks for mask branches;
specifically, the Grid module uniformly grids the input face image by uniformly dividing the input face image into S rows and S columns, that is, S2The grid subgraph takes the left eye, the right eye, the nose, the lips and other parts and backgrounds as reference segmentation marks, 5 types of class marks are used in total, the grid mark where the center point of each part of the face is located is the class of the part, the rest grids are all marked as the background class, finally, a SxS matrix is generated and used as a reference label of a classification branch, correspondingly, each grid corresponds to a mask image, and S is included2A trellis, so the mask branch will output S2Each channel corresponds to a grid, and each mask image only corresponds to one object in one category.
Furthermore, the classification branch and the mask branch are used as two different tasks for completing face segmentation together, the two branches use different Loss functions in training, and the classification branch uses Cross Engine Loss, which is recorded as LCEAs shown in formula I, the mask branch adopts Dice Loss, which is denoted as LDiceAs shown in formula two:
Figure BSA0000201973360000021
in the formula I, x [ j ] and x [ class ] are both the output of a prediction layer, and x [ class ] is the value of the real class;
Figure BSA0000201973360000022
in the formula two, px，yTo predict the pixel value at location (x, y) in the mask, qx，yIs the pixel value at location (x, y) in the real mask;
during the training process, the overall loss function is as shown in formula three:
L＝Lc+λLDiceformula three
In the third formula, λ is a loss function coefficient, and is used for balancing the loss function weights of the two branches.
Step 2, extracting basic features of the gridded face image by using an FCN module;
specifically, the FCN module is composed of 1 convolutional layer of 3x3 and 4 Block layers, where a Block layer includes 4 convolutional layers of 3x3, 1 convolutional layer of 1x1 and 2 summation layers, an input image first passes through the convolutional layer of Stride 2, then sequentially passes through 4 Block layers, each Block layer performs feature dimension reduction once, and finally, the output of the last three Block layers is taken as the three-way input of the next JFU module
Step 3, fusing the three-level characteristics output by the FCN module by using an JFU module to obtain richer characteristic information;
specifically, the JFU module is composed of a convolutional layer, an upsampling layer, a cascade layer and an expansion convolutional layer, three inputs respectively correspond to outputs of three-level Block layers of the FCN module, the three inputs have different channel numbers and feature plane sizes, feature planes with the same channel number are obtained after passing through the convolutional layers respectively, feature planes with the same size are obtained after passing through the upsampling layer respectively, the three feature planes are cascaded and then pass through convolutional layers with different expansion coefficients, the expansion coefficients are 1, 2, 4 and 8 respectively, and finally the four feature planes are cascaded to serve as inputs of a next module.
Step 4, processing the output of the JFU module by using a Category module to realize the function of classifying branches, predicting whether each grid image has a face part, and if the face part exists, giving a corresponding grid position, Category and confidence coefficient to provide reference information for mask branches;
specifically, the Category module is composed of N convolutional layers, 1 prediction layer and 1 Softmax layer, wherein the prediction layer is composed of 1 convolutional layer with the number of output channels being C, the process of the Category module is that an input feature plane passes through the N convolutional layers, then passes through the 1 prediction layer and then passes through the Softmax layer, the network output size of the classification branch is sxsxsxxc, namely the feature plane size is SxS, the number of channels is C, the Category of C is predicted at each grid position, the Category with the highest confidence coefficient is selected, and the grid prediction result of SxS is formed.
And step 5, processing the output of the JFU module by using a Mask module, acquiring a segmentation Mask of a face part, and predicting a Mask image corresponding to each grid.
Specifically, the Mask module mainly comprises M convolutional layers, 1 prediction layer and 1 upsampling layer, wherein the prediction layer comprises an output channel with the number of S2The Mask module is used for realizing the function of Mask branch, predicting the Mask image corresponding to each grid, and the output size of the Mask branch is HxWxS2I.e. the mask has a feature plane size of HxW and the number of channels is S2In the division prediction, firstly, the position (i, j) and the class C of the grids are positioned in the prediction result of the classification branch, and the channel position (i.e., (i × S + j) of the corresponding mask image in the mask branch is found, wherein the mask image is the mask image of the face part class C.
According to the technical scheme provided by the invention, in the design of the whole network, the limitation of a two-stage segmentation algorithm is avoided, namely the two-stage segmentation algorithm is detected and then segmented, so that the complexity of model training is reduced, more global context information is fused, and the accuracy of model prediction is improved. The consistency of each part of the human face exists in space, the relative positions are fixed, and the central points of each part have certain distance. According to the characteristics of all parts of the human face, the human face segmentation network is decomposed into two branches, namely a classification branch and a mask branch.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a block diagram of the FCN module of the present invention;
FIG. 3 is a Block diagram of the Block module of the present invention;
FIG. 4 is a block diagram of the JFU module of the present invention;
FIG. 5 is a block diagram of a Category module of the present invention;
fig. 6 is a structural diagram of a Mask module of the present invention.
Detailed Description
In order to make the purpose and technical solution of the present invention clearer, the following will clearly and completely describe the technical solution of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of the present invention, and other embodiments obtained by those skilled in the art without inventive work should fall within the scope of the present invention.
Fig. 1 is a flowchart illustrating an implementation of a method for single-level face segmentation according to an exemplary embodiment, and as shown in fig. 1, the method for single-level face segmentation according to the embodiment of the present invention specifically includes the following implementation steps:
step 1, firstly, uniformly gridding an input face image by using a Grid module, providing gridding class labels for classification branches, and providing reference masks for mask branches;
as shown in fig. 1, the Grid module uniformly grids the input face image by uniformly dividing the input face image into S rows and S columns, i.e. S2The grid subgraph takes the left eye, the right eye, the nose, the lips and other parts and backgrounds as reference segmentation marks, 5 types of class marks are used in total, the grid mark where the center point of each part of the face is located is the class of the part, the rest grids are all marked as the background class, finally, a SxS matrix is generated and used as a reference label of a classification branch, correspondingly, each grid corresponds to a mask image, and S is included2A trellis, so the mask branch will output S2Each channel corresponds to a grid, and each mask image only corresponds to one object in one category.
The classification branch and the mask branch are used as two different tasks and used for jointly completing face segmentation, the two branches use different Loss functions in training, and the classification branch uses Cross Engine Loss and is marked as LCEAs shown in formula I, the mask branch adopts Dice Loss, which is denoted as LDiceAs shown in formula two:
Figure BSA0000201973360000041
in the formula I, x [ j ] and x [ class ] are both the output of a prediction layer, and x [ class ] is the value of the real class;
Figure BSA0000201973360000042
in the formula two, px，yTo predict the pixel value at location (x, y) in the mask, qx，yIs the pixel value at location (x, y) in the real mask;
during the training process, the overall loss function is as shown in formula three:
L＝Lc+λLDiceformula three
In the third formula, λ is a loss function coefficient, and is used for balancing the loss function weights of the two branches.
Step 2, extracting basic features of the gridded face image by using an FCN module;
fig. 2 is a structural diagram of an FCN module, fig. 3 is a structural diagram of a Block module, the FCN module is composed of 1 convolution layer of 3 × 3 and 4 Block layers, and the Block layers include 4 convolution layers of 3 × 3, 1 convolution layer of 1 × 1 and 2 summation layers, an input image passes through the convolution layer with Stride equal to 2 first, then passes through 4 stages of Block layers in sequence, each Block layer performs a feature dimension reduction, and finally the output of the last three stages of Block layers is taken as the three-way input of the next JFU module.
Step 3, fusing the three-level characteristics output by the FCN module by using an JFU module to obtain richer characteristic information;
fig. 4 is a structural diagram of an JFU module, where the JFU module is composed of a convolutional layer, an upsampling layer, a cascade layer, and an expansion convolutional layer, where three inputs respectively correspond to outputs of three Block layers of an FCN module, the three inputs have different channel numbers and feature plane sizes, and after passing through the convolutional layers, feature planes with the same channel number are obtained, and then passing through the upsampling layer, feature planes with the same size are obtained, the three feature planes are cascaded, and then pass through convolutional layers with different expansion coefficients, where the expansion coefficients are 1, 2, 4, and 8, and finally the four feature planes are cascaded to serve as inputs of a next module.
Step 4, processing the output of the JFU module by using a Category module to realize the function of classifying branches, predicting whether each grid image has a face part, and if the face part exists, giving a corresponding grid position, Category and confidence coefficient to provide reference information for mask branches;
fig. 5 is a structural diagram of a Category module, where the Category module is composed of N convolutional layers, 1 prediction layer, and 1 Softmax layer, where N is set to be N ═ 4 in this embodiment, where a prediction layer is composed of 1 convolutional layer with the number of output channels being C, a flow of the Category module is that an input feature plane passes through the N convolutional layers, then passes through the 1 prediction layer, and then passes through the Softmax layer, a network output size of a classification branch is sxsxsxsxxc, that is, the feature plane size is SxS, the number of channels is C, each grid position is subjected to prediction of a C Category, and a Category with the highest confidence is selected, so as to form a grid prediction result of SxS.
Step 5, processing the output of the JFU module by using a Mask module, acquiring a segmentation Mask of a face part, and predicting a Mask image corresponding to each grid;
fig. 6 is a structural diagram of a Mask module, where the Mask module mainly includes M convolutional layers, 1 prediction layer and 1 upsampling layer, M is set to be M ═ 4 in this embodiment, and the prediction layer includes one output channel with S number2The Mask module is used for realizing the function of Mask branch, predicting the Mask image corresponding to each grid, and the output size of the Mask branch is HxWxS2I.e. the mask has a feature plane size of HxW and the number of channels is S2In the division prediction, firstly, the position (i, j) and the class C of the grid are positioned in the prediction result of the classification branch, and the channel position (i.e., (i × S + j) of the corresponding mask image in the mask branch is found, wherein the mask image is the face partMask map for class C.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. It will be understood that the disclosure is not limited to the embodiments described and disclosed above, but is intended to cover all modifications and changes that may be made without departing from the scope of the disclosure, as defined in the appended claims.

Claims (7)

1. A method for single-stage face segmentation is characterized by comprising the following implementation steps:
step 1, firstly, uniformly gridding an input face image by using a Grid module, providing gridding class labels for classification branches, and providing reference masks for mask branches;
step 2, extracting basic features of the gridded face image by using an FCN module;
step 3, fusing the three-level characteristics output by the FCN module by using an JFU module to obtain richer characteristic information;
step 4, processing the output of the JFU module by using a Category module to realize the function of classifying branches, predicting whether each grid image has a face part, and if the face part exists, giving a corresponding grid position, Category and confidence coefficient to provide reference information for mask branches;
and step 5, processing the output of the JFU module by using a Mask module, acquiring a segmentation Mask of a face part, and predicting a Mask image corresponding to each grid.
2. The method of claim 1, wherein the Grid module of step 1 uniformly grids the input face image by uniformly dividing the input face image into S rows and S columns, i.e. S2The sub-graph of each grid takes the left eye, the right eye, the nose, the lips and other parts and backgrounds as reference segmentation marks, 5 types of class marks are provided, the grid mark where the center point of each part of the face is positioned is the class of the part, the rest grids are all marked as background classes, and finally an SxS matrix is generated and used as the reference of classification branchesTest labels, each grid corresponding to a mask image, with S2A trellis, so the mask branch will output S2Each channel corresponds to a grid, and each mask image only corresponds to one object in one category.
3. The method of claim 1, wherein the classification branch and the mask branch in step 1 are used as two different tasks to jointly perform face segmentation, the two branches use different Loss functions in training, and the classification branch uses Cross entry Loss, denoted as LCEAs shown in formula I, the mask branch adopts Dice Loss, which is denoted as LDiceAs shown in formula two:
Figure FSA0000201973350000011
in the formula I, x [ j ] and x [ class ] are both the output of a prediction layer, and x [ class ] is the value of the real class;
Figure FSA0000201973350000012
in the formula two, px，yTo predict the pixel value at location (x, y) in the mask, qx，yIs the pixel value at location (x, y) in the real mask;
during the training process, the overall loss function is as shown in formula three:
L＝Lc+λLDiceformula three
In the third formula, λ is a loss function coefficient, and is used for balancing the loss function weights of the two branches.
4. The method of claim 1, wherein the FCN Block of step 2 is composed of 1 convolutional layer of 3x3 and 4 Block layers, and the Block layers include 4 convolutional layers of 3x3, 1 convolutional layer of 1x1 and 2 summation layers, and the input image passes through the convolutional layer of Stride 2 first and then passes through the 4 Block layers in sequence, each Block layer performs a feature dimension reduction, and finally the output of the last three Block layers is taken as the three inputs of the next JFU Block.
5. The method of claim 1, wherein the JFU module in step 3 is composed of a convolutional layer, an upsampling layer, a cascade layer, and an expansion convolutional layer, three inputs respectively correspond to outputs of three Block layers of the FCN module, the three inputs have different channel numbers and feature plane sizes, feature planes with the same channel number are obtained after passing through the convolutional layer, feature planes with the same size are obtained after passing through the upsampling layer, the three feature planes are cascaded, then four convolutional layers with different expansion coefficients are passed through, the expansion coefficients are 1, 2, 4, and 8, and finally the four feature planes are cascaded to serve as inputs of a next module.
6. The method of claim 1, wherein the Category module in step 4 is composed of N convolutional layers, 1 prediction layer and 1 Softmax layer, wherein the prediction layer is composed of 1 convolutional layer with C output channels, the process of the Category module is that an input feature plane passes through the N convolutional layers, then passes through the 1 prediction layer and then passes through the Softmax layer, the network output size of a classification branch is sxsxsxsxsxxc, that is, the feature plane size is SxS, the channel number is C, each grid position is subjected to C Category prediction, and the Category with the highest confidence is selected to form the grid prediction result of SxS.
7. The method of claim 1, wherein the Mask module in step 5 mainly comprises M convolutional layers, 1 prediction layer and 1 upsampling layer, wherein the prediction layer comprises an output channel with the number S2The Mask module is used for realizing the function of Mask branch, predicting the Mask image corresponding to each grid, and the output size of the Mask branch is HxWxS2I.e. the size of the feature plane of the mask is HxW, generalThe channel number is S2In the division prediction, firstly, the position (i, j) and the class C of the grids are positioned in the prediction result of the classification branch, and the channel position (i.e., (i × S + j) of the corresponding mask image in the mask branch is found, wherein the mask image is the mask image of the face part class C.
CN202010100612.3A 2020-02-18 2020-02-18 Single-stage face segmentation method Pending CN111339874A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010100612.3A CN111339874A (en) 2020-02-18 2020-02-18 Single-stage face segmentation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010100612.3A CN111339874A (en) 2020-02-18 2020-02-18 Single-stage face segmentation method

Publications (1)

Publication Number Publication Date
CN111339874A true CN111339874A (en) 2020-06-26

Family

ID=71184124

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010100612.3A Pending CN111339874A (en) 2020-02-18 2020-02-18 Single-stage face segmentation method

Country Status (1)

Country Link
CN (1) CN111339874A (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112381835A (en) * 2020-10-29 2021-02-19 中国农业大学 Crop leaf segmentation method and device based on convolutional neural network
CN112801008A (en) * 2021-02-05 2021-05-14 电子科技大学中山学院 Pedestrian re-identification method and device, electronic equipment and readable storage medium
CN116863342A (en) * 2023-09-04 2023-10-10 江西啄木蜂科技有限公司 Large-scale remote sensing image-based pine wood nematode dead wood extraction method
CN116883670A (en) * 2023-08-11 2023-10-13 智慧眼科技股份有限公司 Anti-shielding face image segmentation method
CN112801008B (en) * 2021-02-05 2024-05-31 电子科技大学中山学院 Pedestrian re-recognition method and device, electronic equipment and readable storage medium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109670429A (en) * 2018-12-10 2019-04-23 广东技术师范学院 A kind of the monitor video multiple target method for detecting human face and system of Case-based Reasoning segmentation
CN110781776A (en) * 2019-10-10 2020-02-11 湖北工业大学 Road extraction method based on prediction and residual refinement network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109670429A (en) * 2018-12-10 2019-04-23 广东技术师范学院 A kind of the monitor video multiple target method for detecting human face and system of Case-based Reasoning segmentation
CN110781776A (en) * 2019-10-10 2020-02-11 湖北工业大学 Road extraction method based on prediction and residual refinement network

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
KAIMING HE ET AL.: "《Deep Residual Learning for Image Recognition》", 《COMPUTER VISION FOUNDATION》 *
QIJIE ZHAO ET AL,: "《M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network》", pages 50 - 8 *
SONGTAO LIU ET AL.: "《Receptive Field Block Net for Accurate and Fast Object Detection》", pages 1 - 16 *
XINLONG WANG ET AL.: "《SOLO: Segmenting Objects by Locations》", pages 1 - 10 *
奔跑的CODE: "《【学习笔记】resnet-18 pytorch源代码解读》", 《HTTPS://BLOG.CSDN.NET/LCN463365355/ARTICLE/DETAILS/92846776/》 *
牧酱: "《resnet网络结构分析》", 《HTTPS://BLOG.CSDN.NET/WCHZH2015/ARTICLE/DETAILS/93883771》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112381835A (en) * 2020-10-29 2021-02-19 中国农业大学 Crop leaf segmentation method and device based on convolutional neural network
CN112801008A (en) * 2021-02-05 2021-05-14 电子科技大学中山学院 Pedestrian re-identification method and device, electronic equipment and readable storage medium
CN112801008B (en) * 2021-02-05 2024-05-31 电子科技大学中山学院 Pedestrian re-recognition method and device, electronic equipment and readable storage medium
CN116883670A (en) * 2023-08-11 2023-10-13 智慧眼科技股份有限公司 Anti-shielding face image segmentation method
CN116883670B (en) * 2023-08-11 2024-05-14 智慧眼科技股份有限公司 Anti-shielding face image segmentation method
CN116863342A (en) * 2023-09-04 2023-10-10 江西啄木蜂科技有限公司 Large-scale remote sensing image-based pine wood nematode dead wood extraction method
CN116863342B (en) * 2023-09-04 2023-11-21 江西啄木蜂科技有限公司 Large-scale remote sensing image-based pine wood nematode dead wood extraction method

Similar Documents

Publication Publication Date Title
CN107945204B (en) Pixel-level image matting method based on generation countermeasure network
CN109859190B (en) Target area detection method based on deep learning
CN110378222B (en) Method and device for detecting vibration damper target and identifying defect of power transmission line
He et al. Enhanced boundary learning for glass-like object segmentation
CN111339874A (en) Single-stage face segmentation method
CN109712145A (en) A kind of image matting method and system
CN111640125A (en) Mask R-CNN-based aerial photograph building detection and segmentation method and device
CN112434618B (en) Video target detection method, storage medium and device based on sparse foreground priori
CN112686902B (en) Two-stage calculation method for brain glioma identification and segmentation in nuclear magnetic resonance image
CN109657538B (en) Scene segmentation method and system based on context information guidance
CN111401293A (en) Gesture recognition method based on Head lightweight Mask scanning R-CNN
CN111768415A (en) Image instance segmentation method without quantization pooling
CN111597920A (en) Full convolution single-stage human body example segmentation method in natural scene
CN108986101A (en) Human body image dividing method based on circulation " scratching figure-segmentation " optimization
CN112183649A (en) Algorithm for predicting pyramid feature map
CN112365511A (en) Point cloud segmentation method based on overlapped region retrieval and alignment
CN116645592A (en) Crack detection method based on image processing and storage medium
CN114612872A (en) Target detection method, target detection device, electronic equipment and computer-readable storage medium
CN116596966A (en) Segmentation and tracking method based on attention and feature fusion
CN115797629A (en) Example segmentation method based on detection enhancement and multi-stage bounding box feature refinement
CN116740516A (en) Target detection method and system based on multi-scale fusion feature extraction
CN113487610B (en) Herpes image recognition method and device, computer equipment and storage medium
CN112330699B (en) Three-dimensional point cloud segmentation method based on overlapping region alignment
CN112215301B (en) Image straight line detection method based on convolutional neural network
CN117475416A (en) Thermal power station pointer type instrument reading identification method, system, equipment and medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information
CB02 Change of applicant information

Address after: 510670 17 / F, building 3, Yunsheng Science Park, No. 11, puyuzhong Road, Huangpu District, Guangzhou City, Guangdong Province

Applicant after: GUANGZHOU MELUX INFORMATION TECHNOLOGY Co.,Ltd.

Address before: 510670 5th floor, building 5, No.8, science Avenue, Science City, Guangzhou high tech Industrial Development Zone, Guangzhou City, Guangdong Province

Applicant before: GUANGZHOU MELUX INFORMATION TECHNOLOGY Co.,Ltd.

DD01 Delivery of document by public notice
DD01 Delivery of document by public notice

Addressee: Yu Zheyuan

Document name: Notice of Termination of Procedure

DD01 Delivery of document by public notice
DD01 Delivery of document by public notice

Addressee: Yu Zheyuan

Document name: Rejection decision