CN108154066B - Three-dimensional target identification method based on curvature characteristic recurrent neural network - Google Patents

Three-dimensional target identification method based on curvature characteristic recurrent neural network Download PDF

Info

Publication number
CN108154066B
CN108154066B CN201611096314.1A CN201611096314A CN108154066B CN 108154066 B CN108154066 B CN 108154066B CN 201611096314 A CN201611096314 A CN 201611096314A CN 108154066 B CN108154066 B CN 108154066B
Authority
CN
China
Prior art keywords
dimensional
curvature
sequence
target
brnn
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.)
Active
Application number
CN201611096314.1A
Other languages
Chinese (zh)
Other versions
CN108154066A (en
Inventor
梁炜
李杨
郑萌
谈金东
彭士伟
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.)
Shenyang Institute of Automation of CAS
Original Assignee
Shenyang Institute of Automation of CAS
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 Shenyang Institute of Automation of CAS filed Critical Shenyang Institute of Automation of CAS
Priority to CN201611096314.1A priority Critical patent/CN108154066B/en
Publication of CN108154066A publication Critical patent/CN108154066A/en
Application granted granted Critical
Publication of CN108154066B publication Critical patent/CN108154066B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G06V20/647—Three-dimensional objects by matching two-dimensional images to three-dimensional objects
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Landscapes

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

Abstract

The invention relates to an image recognition technology, and provides a three-dimensional target recognition method based on a curvature characteristic recurrent neural network in order to effectively depict the characteristics of a three-dimensional target under different visual angles and aim at the problem of image noise in the process of recognizing the three-dimensional target. Firstly, the method obtains the combined curvature of the target three-dimensional model by calculating the local mean Gaussian curvature and the mean curvature of the target three-dimensional model, forms a curvature sketch of the three-dimensional model by extracting the local maximum value of the combined curvature, and generates a 360-degree two-dimensional image sequence by utilizing transmission projection transformation as the input of training a recurrent neural network; secondly, a Bidirectional Recurrent Neural Network (BRNN) is used as a three-dimensional model multi-view sequence feature learning method, and the identification category with the maximum correct probability is obtained by utilizing a softmax function in a softmax layer. The method can automatically extract the common characteristics of the three-dimensional target and the two-dimensional image, and can keep better robustness and higher target identification rate under the condition of image noise.

Description

Three-dimensional target identification method based on curvature characteristic recurrent neural network
Technical Field
The invention relates to the technical field of image recognition, in particular to a three-dimensional target recognition method based on a curvature characteristic recurrent neural network.
Background
Three-dimensional target recognition refers to a process of automatically detecting, positioning and recognizing a specified target mode from any given two-dimensional image scene, and is one of the key problems of computer vision research. With the continuous development of computer vision technology, three-dimensional target recognition is more and more widely applied to the fields of industrial detection, augmented reality, medical images and the like. However, due to the influence of factors such as illumination change, image noise and target shielding, it is difficult to extract common features of a three-dimensional target and two-dimensional images thereof under different viewing angles, and the problem to be solved is urgent to be identified.
The key of the three-dimensional target identification is to find out the two-dimensional expression of a three-dimensional target model and extract the common characteristics of the three-dimensional target and the two-dimensional image. The existing three-dimensional target identification method mainly comprises an artificial marking point-based method, a geometric feature-based method, a deep learning-based method and the like. The method based on the manual marking points needs to manually initialize the characteristic points in the two-dimensional image, and the method has no repeatability because of the need of manual interaction; the method based on the geometric features realizes target identification by extracting information such as a center line skeleton, a contour shape and the like of a target, but the method has poor identification effect under the condition that noise exists in an image; the method based on deep learning utilizes a deep neural network to fuse low-level image features into high-level features with semantic information, and can well solve the problem of image noise of a two-dimensional image in the process of identifying a three-dimensional target. Therefore, it is desirable to provide an automatic three-dimensional target identification method robust to image noise problem in images with different viewing angles.
Disclosure of Invention
The invention aims to more effectively depict the characteristics of a three-dimensional target under different visual angles, reduce the sensitivity of the characteristic extraction process to image noise and improve the identification accuracy of the three-dimensional target.
The technical scheme adopted by the invention for realizing the purpose is as follows: a three-dimensional target identification method based on curvature characteristic recurrent neural network comprises the following steps:
step 1: calculating joint curvature of a three-dimensional model of an object
Figure BDA0001169544040000021
Extracting combined curvatures
Figure BDA0001169544040000022
The local maximum values form a curvature sketch R of the three-dimensional modelSketch(ii) a Then, a curvature sketch R of the three-dimensional model is conductedSketchGeneration of a 360 DEG two-dimensional image P using transmission projective transformationmWherein m is 1, 2.., 360;
step 2: inputting a 360-degree two-dimensional image into the BRNN, and utilizing multi-angle characteristics to learn and calculate sequence attributes of the image under multiple visual angles; obtaining the identification category when the correct probability of the sequence attribute is maximum by utilizing a softmax function in a softmax layer; the BRNN is a bidirectional recurrent neural network.
The joint curvature of the three-dimensional model of the calculation target
Figure BDA0001169544040000023
The method comprises the following steps:
is provided with
Figure BDA0001169544040000024
Is a normal vector of a given point (x, y, z) on the target three-dimensional model R; order to
Figure BDA0001169544040000025
Then p isx,py,qx,qyIs defined as
Figure BDA0001169544040000026
Calculating the mean Gaussian curvature in a 3 × 3 neighborhood around the normal vector of each point on the three-dimensional model R
Figure BDA0001169544040000027
And mean curvature
Figure BDA0001169544040000028
Figure BDA0001169544040000029
Figure BDA00011695440400000210
Wherein the content of the first and second substances,
Figure BDA00011695440400000211
being the mean curvature matrix, trace (-) is the trace of the matrix,
Figure BDA00011695440400000212
are respectively p, q, px,py,qx,qyAverage in the 3 × 3 neighborhood;
defining a joint curvature of a three-dimensional model R of an object
Figure BDA00011695440400000213
Comprises the following steps:
Figure BDA00011695440400000214
the method comprises the following steps of inputting a 360-degree two-dimensional image into the BRNN, and utilizing multi-angle features to learn and calculate the sequence attributes of the image under multiple visual angles, wherein the method comprises the following steps:
one-dimensional characteristic sequence T for acquiring 360-degree two-dimensional imageSS 1,2, 360, then the signature sequence TSOutput at the i-th layer of BRNN is divided into forward output
Figure BDA0001169544040000031
And reverse output
Figure BDA0001169544040000032
And respectively output with a sequence on the BRNN of the local layer in the forward direction
Figure BDA0001169544040000033
Reverse output of BRNN next sequence at this layer
Figure BDA0001169544040000034
And the forward output of the upper layer BRNN
Figure BDA0001169544040000035
And reverse output
Figure BDA0001169544040000036
The following relationships exist:
Figure BDA0001169544040000037
Figure BDA0001169544040000038
wherein the content of the first and second substances,
Figure BDA0001169544040000039
b is a bias, and tanh is a neuron activation function;
then the characteristic sequence TSTotal output O at BRNNsI.e. input I of full connection level fcfcComprises the following steps:
Figure BDA00011695440400000310
wherein the content of the first and second substances,
Figure BDA00011695440400000311
respectively is the connection weight of the forward output and the reverse output on the full connection layer;
thus, the signature sequence TSThe cumulative output at full connection level fc is
Figure BDA00011695440400000312
I.e. the sequence property.
The identification category when the correct probability of the sequence attribute is maximum is obtained by utilizing a softmax function at a softmax layer, and the method comprises the following steps:
calculating the correct probability p (C) of the recognition result being the kth class by utilizing a softmax function at a softmax layerk)
Figure BDA00011695440400000313
Wherein C is the total number of identification categories, AkAccumulating and outputting a result of the sequence attribute of the kth three-dimensional target at the full connection layer fc;
then, the maximum likelihood estimation method is used to obtain the minimum value of the loss function, i.e. the correct probability p (C)k) Maximum recognition category k:
Figure BDA0001169544040000041
wherein δ (·) is a kronecker function
Figure BDA0001169544040000042
r represents a characteristic sequence TSCorrectly identify the category.
The invention has the following beneficial effects and advantages:
1. the method for extracting the features of the combined curvature sketch designed by the invention can automatically extract the common features of the three-dimensional model and the two-dimensional image, and the problem of image noise can be effectively solved by the local mean Gaussian curvature and the local mean curvature used by the combined curvature.
2. The invention designs a multi-angle feature learning bidirectional recurrent neural network, can simultaneously consider the feature sequence of the three-dimensional model under multiple angles, and can accurately identify the three-dimensional target in the two-dimensional image at any angle.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a multi-angle feature learning bi-directional recurrent neural network framework in the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention is mainly divided into two parts, as shown in fig. 1, the method flow chart of the invention is shown, and the specific implementation process is as follows.
Step 1: calculating the joint curvature of the target three-dimensional model, forming a curvature sketch of the three-dimensional model by extracting local maximum values of the joint curvature, and generating a 360-degree two-dimensional image by utilizing transmission projection transformation as input of a training recurrent neural network;
step 1.1: is provided with
Figure BDA0001169544040000043
Is the normal vector for a given point (x, y, z) on the three-dimensional model. Order to
Figure BDA0001169544040000051
Then p isx,py,qx,qyIs defined as
Figure BDA0001169544040000052
The gaussian curvature G of the three-dimensional modelKIs composed of
GK＝|C|，
Wherein the curvature matrix
Figure BDA0001169544040000053
Mean curvature M of three-dimensional modelKIs composed of
Figure BDA0001169544040000054
trace (·) is the trace of the matrix. In order to eliminate noise influence, the invention calculates the average Gaussian curvature in a 3 multiplied by 3 neighborhood around the normal vector of each point on the three-dimensional model
Figure BDA0001169544040000055
And mean curvature
Figure BDA0001169544040000056
Figure BDA0001169544040000057
Figure BDA0001169544040000058
Wherein
Figure BDA0001169544040000059
Is the average curvature matrix and is,
Figure BDA00011695440400000510
are respectively p, q, px,py,qx,qyAverage in the 3 × 3 neighborhood. Thus, we define the joint curvature of the three-dimensional model
Figure BDA00011695440400000511
Is composed of
Figure BDA00011695440400000512
Step 1.2: extracting combined curvatures
Figure BDA00011695440400000513
The local maximum points form a curvature sketch R of the three-dimensional model RSketch. Generating a three-dimensional curvature sketch R through perspective projection transformationSketch360 deg. two-dimensional projection image PmM =1, 2.., 360, as an input to the BRNN.
Step 2: the invention adopts a Deep Recurrent Neural Network (DRNN) as a curvature characteristic identification method, and a DRNN frame is shown as figure 2. And (3) utilizing multi-angle feature learning BRNN to depict sequence attributes of the three-dimensional model under multiple visual angles, and utilizing a softmax function to obtain the identification category with the maximum correct probability in a softmax layer.
Step 2.1: in order to depict the characteristic sequence of the three-dimensional model under different visual angles, the one-dimensional characteristic sequence of the three-dimensional model under multiple visual angles is defined as TSS 1,2, 360, then the signature sequence TSOutput at the i-th layer of BRNN is divided into forward output
Figure BDA0001169544040000061
And reverse output
Figure BDA0001169544040000062
Respectively output with a sequence on the BRNN of the layer
Figure BDA0001169544040000063
Reverse output of BRNN next sequence at this layer
Figure BDA0001169544040000064
And the forward output of the upper layer BRNN
Figure BDA0001169544040000065
And reverse output
Figure BDA0001169544040000066
The following relationships exist:
Figure BDA0001169544040000067
Figure BDA0001169544040000068
wherein
Figure BDA0001169544040000069
B is bias, and tanh is neuron activation function; then the characteristic sequence TSTotal output O at BRNNsI.e. input I of full connection level fcfcIs composed of
Figure BDA00011695440400000610
Wherein the content of the first and second substances,
Figure BDA00011695440400000611
the connection weights of the forward output and the reverse output at the full connection layer are respectively.
Step 2.2: characteristic sequence TSThe cumulative output at full connection level fc is
Figure BDA00011695440400000612
I.e. the sequence property. Calculating the correct probability p (C) of the recognition result being the kth class by utilizing a softmax function at a softmax layerk)
Figure BDA00011695440400000613
Wherein C is the total number of identification classes, AkAnd outputting the result of the sequence attribute of the kth three-dimensional target in the full connection layer fc. Then, the maximum likelihood estimation method is used to obtain the minimum value of the loss function, i.e. the correct probability p (C)k) Maximum recognition category k:
Figure BDA00011695440400000614
where δ (-) is a kronecker function
Figure BDA0001169544040000071
r represents a characteristic sequence TSCorrectly identify the category.

Claims (3)

1. A three-dimensional target identification method based on curvature characteristic recurrent neural network is characterized by comprising the following steps:
step 1: calculating joint curvature of a three-dimensional model of an object
Figure FDA0002972861850000011
Extracting combined curvatures
Figure FDA0002972861850000012
The local maximum values form a curvature sketch R of the three-dimensional modelSketch(ii) a Then, a curvature sketch R of the three-dimensional model is conductedSketchGeneration of a 360 DEG two-dimensional image P using transmission projective transformationmWherein m is 1, 2.., 360;
step 2: inputting a 360-degree two-dimensional image into the BRNN, and utilizing multi-angle characteristics to learn and calculate sequence attributes of the image under multiple visual angles; obtaining the identification category when the correct probability of the sequence attribute is maximum by utilizing a softmax function in a softmax layer; the BRNN is a bidirectional recurrent neural network;
the joint curvature of the three-dimensional model of the calculation target
Figure FDA0002972861850000013
The method comprises the following steps:
is provided with
Figure FDA0002972861850000014
Is a normal vector of a given point (x, y, z) on the target three-dimensional model R; order to
Figure FDA0002972861850000015
Then p isx,py,qx,qyIs defined as
Figure FDA0002972861850000016
Calculating the mean Gaussian curvature in a 3 × 3 neighborhood around the normal vector of each point on the three-dimensional model R
Figure FDA0002972861850000017
And mean curvature
Figure FDA0002972861850000018
Figure FDA0002972861850000019
Figure FDA00029728618500000110
Wherein the content of the first and second substances,
Figure FDA00029728618500000111
being the mean curvature matrix, trace (-) is the trace of the matrix,
Figure FDA00029728618500000112
are respectively p, q, px,py,qx,qyAverage in the 3 × 3 neighborhood;
defining a joint curvature of a three-dimensional model R of an object
Figure FDA00029728618500000113
Comprises the following steps:
Figure FDA00029728618500000114
2. the method for identifying three-dimensional objects based on curvature feature recurrent neural network as claimed in claim 1, wherein said inputting 360 ° two-dimensional image into BRNN, using multi-angle feature to learn and calculate its sequence attribute under multi-view, comprises the following steps:
one-dimensional characteristic sequence T for acquiring 360-degree two-dimensional imageSS 1,2, 360, then the signature sequence TSOutput at the i-th layer of BRNN is divided into forward output
Figure FDA0002972861850000021
And reverse output
Figure FDA0002972861850000022
And respectively output with a sequence on the BRNN of the local layer in the forward direction
Figure FDA0002972861850000023
Reverse output of BRNN next sequence at this layer
Figure FDA0002972861850000024
And the forward output of the upper layer BRNN
Figure FDA0002972861850000025
And reverse output
Figure FDA0002972861850000026
The following relationships exist:
Figure FDA0002972861850000027
Figure FDA0002972861850000028
wherein the content of the first and second substances,
Figure FDA0002972861850000029
b is a bias, and tanh is a neuron activation function;
then the characteristic sequence TSTotal output O at BRNNsI.e. input I of full connection level fcfcComprises the following steps:
Figure FDA00029728618500000210
wherein the content of the first and second substances,
Figure FDA00029728618500000211
respectively is the connection weight of the forward output and the reverse output on the full connection layer;
thus, the signature sequence TSThe cumulative output at full connection level fc is
Figure FDA00029728618500000212
I.e. the sequence property.
3. The three-dimensional object recognition method based on the curvature feature recurrent neural network as claimed in claim 1, wherein the recognition class when the correct probability of the sequence attribute is maximum is found by the softmax layer by using the softmax function, comprising the following steps:
calculating the correct probability p (C) of the recognition result being the kth class by utilizing a softmax function at a softmax layerk)
Figure FDA00029728618500000213
Wherein C is the total number of identification categories, AkAccumulating and outputting a result of the sequence attribute of the kth three-dimensional target at the full connection layer fc;
then, the maximum likelihood estimation method is used to obtain the minimum value of the loss function, i.e. the correct probability p (C)k) Maximum recognition category k:
Figure FDA0002972861850000031
wherein δ (·) is a kronecker function
Figure FDA0002972861850000032
r represents a characteristic sequence TSCorrect identification category of; t isSTo acquire a one-dimensional sequence of features for a 360 ° two-dimensional image, s 1, 2.
CN201611096314.1A 2016-12-02 2016-12-02 Three-dimensional target identification method based on curvature characteristic recurrent neural network Active CN108154066B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201611096314.1A CN108154066B (en) 2016-12-02 2016-12-02 Three-dimensional target identification method based on curvature characteristic recurrent neural network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201611096314.1A CN108154066B (en) 2016-12-02 2016-12-02 Three-dimensional target identification method based on curvature characteristic recurrent neural network

Publications (2)

Publication Number Publication Date
CN108154066A CN108154066A (en) 2018-06-12
CN108154066B true CN108154066B (en) 2021-04-27

Family

ID=62470160

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201611096314.1A Active CN108154066B (en) 2016-12-02 2016-12-02 Three-dimensional target identification method based on curvature characteristic recurrent neural network

Country Status (1)

Country Link
CN (1) CN108154066B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109166183B (en) * 2018-07-16 2023-04-07 中南大学 Anatomical landmark point identification method and identification equipment
CN109496316B (en) * 2018-07-28 2022-04-01 合刃科技（深圳）有限公司 Image recognition system
CN109242955B (en) * 2018-08-17 2023-03-24 山东师范大学 Workpiece manufacturing characteristic automatic identification method and device based on single image
CN109493354B (en) * 2018-10-10 2021-08-06 中国科学院上海技术物理研究所 Target two-dimensional geometric shape reconstruction method based on multi-view images
CN110287783A (en) * 2019-05-18 2019-09-27 天嗣智能信息科技(上海)有限公司 A kind of video monitoring image human figure identification method
CN117315397B (en) * 2023-10-11 2024-06-07 电子科技大学 Classification method for noise data containing labels based on class curvature

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101770566A (en) * 2008-12-30 2010-07-07 复旦大学 Quick three-dimensional human ear identification method
CN104166842A (en) * 2014-07-25 2014-11-26 同济大学 Three-dimensional palm print identification method based on partitioning statistical characteristic and combined expression
CN104463111A (en) * 2014-11-21 2015-03-25 天津工业大学 Three-dimensional face recognition method fused with multi-scale feature region curvatures
CN105205478A (en) * 2015-10-23 2015-12-30 天津工业大学 3-dimensional human face recognition method integrating anthropometry and curvelet transform
KR101592294B1 (en) * 2014-09-03 2016-02-05 배재대학교 산학협력단 Decimation Method For Complex Three Dimensional Polygonal Mesh Data
CN106097431A (en) * 2016-05-09 2016-11-09 王红军 A kind of object global recognition method based on 3 d grid map

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9984473B2 (en) * 2014-07-09 2018-05-29 Nant Holdings Ip, Llc Feature trackability ranking, systems and methods
US10019784B2 (en) * 2015-03-18 2018-07-10 Toshiba Medical Systems Corporation Medical image processing apparatus and method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101770566A (en) * 2008-12-30 2010-07-07 复旦大学 Quick three-dimensional human ear identification method
CN104166842A (en) * 2014-07-25 2014-11-26 同济大学 Three-dimensional palm print identification method based on partitioning statistical characteristic and combined expression
KR101592294B1 (en) * 2014-09-03 2016-02-05 배재대학교 산학협력단 Decimation Method For Complex Three Dimensional Polygonal Mesh Data
CN104463111A (en) * 2014-11-21 2015-03-25 天津工业大学 Three-dimensional face recognition method fused with multi-scale feature region curvatures
CN105205478A (en) * 2015-10-23 2015-12-30 天津工业大学 3-dimensional human face recognition method integrating anthropometry and curvelet transform
CN106097431A (en) * 2016-05-09 2016-11-09 王红军 A kind of object global recognition method based on 3 d grid map

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Study on novel Curvature Features for 3D fingerprint recognition";Feng Liu 等;《Neurocomputing》;20151130;第168卷;第599-608页 *
"基于模型的任意视点下三维目标识别研究";许俊峰;《中国优秀硕士学位论文全文数据库 信息科技辑》;20160115(第01期);全文 *

Also Published As

Publication number Publication date
CN108154066A (en) 2018-06-12

Similar Documents

Publication Publication Date Title
CN108154066B (en) Three-dimensional target identification method based on curvature characteristic recurrent neural network
CN110866953B (en) Map construction method and device, and positioning method and device
CN108898063B (en) Human body posture recognition device and method based on full convolution neural network
CN108537191B (en) Three-dimensional face recognition method based on structured light camera
Wei et al. Applications of structure from motion: a survey
CN107424161B (en) Coarse-to-fine indoor scene image layout estimation method
Tau et al. Dense correspondences across scenes and scales
CN108564120B (en) Feature point extraction method based on deep neural network
CN104077760A (en) Rapid splicing system for aerial photogrammetry and implementing method thereof
CN105740775A (en) Three-dimensional face living body recognition method and device
CN113361542B (en) Local feature extraction method based on deep learning
CN106919944A (en) A kind of wide-angle image method for quickly identifying based on ORB algorithms
CN111832484A (en) Loop detection method based on convolution perception hash algorithm
CN110751097B (en) Semi-supervised three-dimensional point cloud gesture key point detection method
CN105513094A (en) Stereo vision tracking method and stereo vision tracking system based on 3D Delaunay triangulation
CN114998566A (en) Interpretable multi-scale infrared small and weak target detection network design method
CN110120013A (en) A kind of cloud method and device
CN116385660A (en) Indoor single view scene semantic reconstruction method and system
He et al. A generative feature-to-image robotic vision framework for 6D pose measurement of metal parts
Feng Mask RCNN-based single shot multibox detector for gesture recognition in physical education
CN107330363A (en) A kind of quick Internet advertising board detection method
Li et al. Sparse-to-local-dense matching for geometry-guided correspondence estimation
CN104361573B (en) The SIFT feature matching algorithm of Fusion of Color information and global information
Konishi et al. Detection of target persons using deep learning and training data generation for Tsukuba challenge
CN117351078A (en) Target size and 6D gesture estimation method based on shape priori

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
GR01 Patent grant
GR01 Patent grant
CB03 Change of inventor or designer information

Inventor after: Liang Wei

Inventor after: Li Yang

Inventor after: Zheng Meng

Inventor after: Peng Shiwei

Inventor before: Liang Wei

Inventor before: Li Yang

Inventor before: Zheng Meng

Inventor before: Tan Jindong

Inventor before: Peng Shiwei

CB03 Change of inventor or designer information