The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the origin...The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.展开更多
The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides.In this paper,we proposed an image processing method to identify the wormholes in the image of...The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides.In this paper,we proposed an image processing method to identify the wormholes in the image of broccoli seedlings,and then to evaluate the damage of the broccoli seedlings by pests.The broccoli seedlings were taken as the research object.The ratio of wormhole areas to broccoli seedling leaves areas(Rw)was used to describe the pest damage degree.An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas.Firstly,broccoli seedling leaves were segmented from the background and the area of the leaves was obtained.There were some holes in segmentation results due to pest damage and other reasons.Then,a classifier based on machine learning was developed to classify the wormholes and other holes.Twenty-four features,including color features and shape features of the holes,were used to develop classifiers.After identifying wormholes from images,the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated.The determination coefficient(R2)between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85.The root-mean-square error(d)was 0.02.Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage.This method could provide references for precision spraying pesticides.展开更多
Due to the special geographical location and environment of Yuzhou and the convenient transportation conditions,the progress of political,economic,and cultural exchanges,has led to a wide variety of residential buildi...Due to the special geographical location and environment of Yuzhou and the convenient transportation conditions,the progress of political,economic,and cultural exchanges,has led to a wide variety of residential buildings and even prototypes of residential buildings in various parts of Henan and even in the middle reaches of the Yellow River are not uncommon in Yuzhou.Therefore,Yuzhou folk houses can be called one of the typical representatives of the traditional residential architectural culture in the Central Plains.Taking the Song Family Courtyard in Qianjing Village in Yuzhou Region as the research object,this paper obtains first-hand data and materials from fieldwork method and literature analysis methods.Besides,this paper not only comprehensively analyzes the shape system,architectural characteristics,and decorative art content of residential buildings through data measurement,on-site mapping,etc.,but also sorts out and demonstrates the characteristics of residential buildings under the influence of the traditional religious ritual law system and social hierarchy.This paper provides theoretical support for further improving and enriching the theoretical achievements of traditional residential dwellings in the Yuzhou area and also provides a theoretical basis for the protection,inheritance,and reuse of residential dwellings in the Yuzhou area,aiming to lay a foundation for subsequent research.展开更多
In the shape analysis community,decomposing a 3D shape intomeaningful parts has become a topic of interest.3D model segmentation is largely used in tasks such as shape deformation,shape partial matching,skeleton extra...In the shape analysis community,decomposing a 3D shape intomeaningful parts has become a topic of interest.3D model segmentation is largely used in tasks such as shape deformation,shape partial matching,skeleton extraction,shape correspondence,shape annotation and texture mapping.Numerous approaches have attempted to provide better segmentation solutions;however,the majority of the previous techniques used handcrafted features,which are usually focused on a particular attribute of 3Dobjects and so are difficult to generalize.In this paper,we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful sub-meshes.The first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different views.Contrasting viewpoints,on the other hand,might not have been associated,and a 3D region could correlate into totally distinct outcomes depending on the viewpoint.To address this,we ran each view through(shared weights)CNN and Bolster block in order to create a probability boundary map.The Bolster block simulates the area relationships between different views,which helps to improve and refine the data.In stage two,the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each view.Finally,a layer that is fully connected is used to return coherent edges,which are then back project to 3D objects to produce the final segmentation.Experiments on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.展开更多
Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover...Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.展开更多
High quality micro mould tools are critical for ensuring defect-free production of micro injection moulded products.The demoulding stage of the micro injection moulding can adversely affect the surface integrity due t...High quality micro mould tools are critical for ensuring defect-free production of micro injection moulded products.The demoulding stage of the micro injection moulding can adversely affect the surface integrity due to friction,adhesion and thermal stresses between the metallic mould and polymeric replicated part.In the present work,we propose the use of precision electropolishing(EP)as a shaping and polishing process to control the draft angle and fillet radius of micro features in order to ease demoulding.Typical defects that occur in replicated polymer parts include cracks,burrs and distorted features.A nickel mould having multiple linear ridges and star shape patterns was designed for the present investigation to have characteristic dimensions ranging from 10μm to 150μm and with various aspect ratios to study the effect of electropolishing on modifying the shape of micro features and surface morphology.A transient 2D computational analysis has been conducted to anticipate the effect of shaping on the Ni mould after electrochemical polishing with non-uniform material removal rates,based on the distribution of current density.The experimental results indicate that after shaping using EP,the draft angle of star-patterns and linear patterns can be effectively increased by approximately 3.6°,while the fillet radius increases by up to 5.0μm.By controlling the electropolishing process,the surface roughness can be maintained under 50 nm.This work uses a green and environmental friendly nickel sulfamate electrolyte which can be effective for shaping of nickel micro features without causing any surface deposition.展开更多
In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the ...In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree (MST) on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.展开更多
The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only addr...The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second one.Directly convolving kernels with irregular points will result in loss of shape information.This paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D points.BLNet optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular distribution.On the other hand,explicitly modeling point positions leads to a new feature encoding with increased structure-awareness.Then,an attention pooling unit selectively combines important features.This bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature learning.Experiments show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.展开更多
基金the National Natural Science Foundation of China (60303029)
文摘The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.
文摘The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides.In this paper,we proposed an image processing method to identify the wormholes in the image of broccoli seedlings,and then to evaluate the damage of the broccoli seedlings by pests.The broccoli seedlings were taken as the research object.The ratio of wormhole areas to broccoli seedling leaves areas(Rw)was used to describe the pest damage degree.An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas.Firstly,broccoli seedling leaves were segmented from the background and the area of the leaves was obtained.There were some holes in segmentation results due to pest damage and other reasons.Then,a classifier based on machine learning was developed to classify the wormholes and other holes.Twenty-four features,including color features and shape features of the holes,were used to develop classifiers.After identifying wormholes from images,the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated.The determination coefficient(R2)between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85.The root-mean-square error(d)was 0.02.Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage.This method could provide references for precision spraying pesticides.
基金funded by Fund projects:The National Social Science Foundation of the Arts Key Project“Research on the Architecture Art and Folk Culture of Chinese Traditional Houses on the Land“Silk Road”(Number:18AH008)”Project entrusted by the Ministry of Culture and Tourism:“Yellow River Culture and Chinese Civilization:Rescue Research on Shaanxi Traditional Residential Buildings and Residential Folk Culture”(No.21HH02)Shaanxi Province High-level Talents Special Support Program.
文摘Due to the special geographical location and environment of Yuzhou and the convenient transportation conditions,the progress of political,economic,and cultural exchanges,has led to a wide variety of residential buildings and even prototypes of residential buildings in various parts of Henan and even in the middle reaches of the Yellow River are not uncommon in Yuzhou.Therefore,Yuzhou folk houses can be called one of the typical representatives of the traditional residential architectural culture in the Central Plains.Taking the Song Family Courtyard in Qianjing Village in Yuzhou Region as the research object,this paper obtains first-hand data and materials from fieldwork method and literature analysis methods.Besides,this paper not only comprehensively analyzes the shape system,architectural characteristics,and decorative art content of residential buildings through data measurement,on-site mapping,etc.,but also sorts out and demonstrates the characteristics of residential buildings under the influence of the traditional religious ritual law system and social hierarchy.This paper provides theoretical support for further improving and enriching the theoretical achievements of traditional residential dwellings in the Yuzhou area and also provides a theoretical basis for the protection,inheritance,and reuse of residential dwellings in the Yuzhou area,aiming to lay a foundation for subsequent research.
基金supported by the National Natural Science Foundation of China (61671397).
文摘In the shape analysis community,decomposing a 3D shape intomeaningful parts has become a topic of interest.3D model segmentation is largely used in tasks such as shape deformation,shape partial matching,skeleton extraction,shape correspondence,shape annotation and texture mapping.Numerous approaches have attempted to provide better segmentation solutions;however,the majority of the previous techniques used handcrafted features,which are usually focused on a particular attribute of 3Dobjects and so are difficult to generalize.In this paper,we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful sub-meshes.The first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different views.Contrasting viewpoints,on the other hand,might not have been associated,and a 3D region could correlate into totally distinct outcomes depending on the viewpoint.To address this,we ran each view through(shared weights)CNN and Bolster block in order to create a probability boundary map.The Bolster block simulates the area relationships between different views,which helps to improve and refine the data.In stage two,the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each view.Finally,a layer that is fully connected is used to return coherent edges,which are then back project to 3D objects to produce the final segmentation.Experiments on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.
文摘Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.
基金support of the Science Foundation Ireland and I-Form(Grant No.16/RC/3872).
文摘High quality micro mould tools are critical for ensuring defect-free production of micro injection moulded products.The demoulding stage of the micro injection moulding can adversely affect the surface integrity due to friction,adhesion and thermal stresses between the metallic mould and polymeric replicated part.In the present work,we propose the use of precision electropolishing(EP)as a shaping and polishing process to control the draft angle and fillet radius of micro features in order to ease demoulding.Typical defects that occur in replicated polymer parts include cracks,burrs and distorted features.A nickel mould having multiple linear ridges and star shape patterns was designed for the present investigation to have characteristic dimensions ranging from 10μm to 150μm and with various aspect ratios to study the effect of electropolishing on modifying the shape of micro features and surface morphology.A transient 2D computational analysis has been conducted to anticipate the effect of shaping on the Ni mould after electrochemical polishing with non-uniform material removal rates,based on the distribution of current density.The experimental results indicate that after shaping using EP,the draft angle of star-patterns and linear patterns can be effectively increased by approximately 3.6°,while the fillet radius increases by up to 5.0μm.By controlling the electropolishing process,the surface roughness can be maintained under 50 nm.This work uses a green and environmental friendly nickel sulfamate electrolyte which can be effective for shaping of nickel micro features without causing any surface deposition.
基金It was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472170, 61170143, 60873110, and Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia under Grant No. ITSM201301. Acknowledgement The work presented in this paper was done during Xiao-Peng Sun's visit at the graphics group of Michigan State University. Thank University of North Dakota for the biometrics database, thank Dr. Yi-Ying Tong for helpful discussions and review, and thank the reviewers of CVM2015 for constructive comments.
文摘In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree (MST) on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.
基金supported by the National Natural Science Foundation of China(Grant No.62171393)National Key R&D Program of China(Grant No.2021YFF0704600).
文摘The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second one.Directly convolving kernels with irregular points will result in loss of shape information.This paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D points.BLNet optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular distribution.On the other hand,explicitly modeling point positions leads to a new feature encoding with increased structure-awareness.Then,an attention pooling unit selectively combines important features.This bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature learning.Experiments show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.