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Fruit recognition based on multi-feature and multi-decision

Fruit recognition based on multi-feature and multi-decision

引用

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Xiaohua Wang Wei Huang Chao Jin Min Hu Fuji Ren School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory The University of Tokushima Japan

ISBN: (纸本)9781467369541

In order to overcome the existing fruit recognition method only for single feature recognition which leads the problem of lower recognition rate, this paper proposes a recognition method based on multi-feature and multi-decision. Firstly, we preprocess the fruit image which is to be classified, separating foreground and background, and then we divide the target area. Secondly, in order to take full advantage of multi-independence and complementarity between features, we extract the color, shape and texture of fruits image and put these features into the BP neural network to classify. Finally, the results of different features are put into the decision-making mechanisms to obtain a final recognition result. The experiment results on object library and self-built library that is built by ourselves show that the method has good identification ability to fruit recognition and achieve the goal to identify different fruit.

关键词： Machine vision Image recognition Image segmentation Image color analysis Shape Image resolution

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ADAPTIVE FACIAL EXPRESSION RECOGNITION METHOD BASED ON MBP AND HMOG FEATURE

ADAPTIVE FACIAL EXPRESSION RECOGNITION METHOD BASED ON MBP A...

引用

2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Min Hu Xiaoyin Huang Xiaohua Wang Kun Li Fuji Ren Liangfeng Xu Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

Because of the limitation of Monogenic waves’ extraction from expression texture,this thesis proposes an adaptive facial expression feature fusion method based on MBP（monogenic binary pattern） and Histogram of HMOG（monogenic orientated gradients）.To begin with our method,we get information of orientation,phase and magnitude component from filtered image by monogenic waves,besides,extract and fuse texture information from orientation and magnitude component to recognize facial *** single texture feature have some inadequacies for facial expression *** of all,this paper describes a new kind of HMOG feature inspired by thoughts of HOG（histogram of oriented gradients） to indicate shape information of facial ***,acquiring best adaptive weights of expression block through information ***,we classify the expression image using Nearest Neighbor to get the final result of effect of proposed *** order to verify the efficiency of the methods above,we do a large number of experiments and it turns out our methods have great ability to explain expression feature.

关键词： Monogenic signal Histogram of Monogenic Oriented Gradients Adaptive weights by information entropy Nearest Neighbor

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Age estimation and gender classification of facial images based on Local Directional Pattern

Age estimation and gender classification of facial images ba...

引用

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Min Hu Yaona Zheng Fuji Ren He Jiang School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory The University of Tokushima Japan

ISBN: (纸本)9781467369541

In order to improve shortage of Local Binary Pattern (LBP) algorithm in the field of facial feature extraction, this paper uses Local Directional Pattern (LDP) algorithm to extract the facial features about age estimation and gender classification. Because gradient information is more stable than gray information, LDP algorithm is not affected easily by noise. After preprocessing images, we use block strategy to represent facial features more fully. And then, we extract features of each block with LDP algorithm, storing the LDP histogram. Finally, we obtain age and gender identification results by Support Vector Machine (SVM). Experimental results on FG-NET face database show that this method has a good effect on age estimation and gender classification.

关键词： Face Feature extraction Estimation Interference Principal component analysis

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Adaptive facial expression recognition method based on MBP and HMOG feature

Adaptive facial expression recognition method based on MBP a...

引用

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Min Hu Xiaoyin Huang Xiaohua Wang Kun Li Fuji Ren Liangfeng Xu School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory The University of Tokushima Japan

ISBN: (纸本)9781467369541

Because of the limitation of Monogenic waves' extraction from expression texture, this thesis proposes an adaptive facial expression feature fusion method based on MBP (monogenic binary pattern) and Histogram of HMOG (monogenic orientated gradients). To begin with our method, we get information of orientation, phase and magnitude component from filtered image by monogenic waves, besides, extract and fuse texture information from orientation and magnitude component to recognize facial expression. However single texture feature have some inadequacies for facial expression recognition. First of all, this paper describes a new kind of HMOG feature inspired by thoughts of HOG (histogram of oriented gradients) to indicate shape information of facial image. Secondly, acquiring best adaptive weights of expression block through information entropy. Finally, we classify the expression image using Nearest Neighbor to get the final result of effect of proposed method. In order to verify the efficiency of the methods above, we do a large number of experiments and it turns out our methods have great ability to explain expression feature.

关键词： Feature extraction Face Entropy Information filters Databases Computers

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FACE RECOGNITION BASED ON ADAPTIVE WEIGHTING AND FUZZY FUSION WITH SINGLE TRAINING SAMPLE

FACE RECOGNITION BASED ON ADAPTIVE WEIGHTING AND FUZZY FUSIO...

引用

2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiaohua Wang Chao Jin Wei Liu Min Hu Fuji Ren Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

In order to solve the robustness issues for single training sample face recognition under occlusion conditions,this paper presents a recognition method based on adaptive weighting and fuzzy *** our method,the information entropy expansion mode is introduced in sub-mode method and via adaptively assigning the weights corresponding to each sub-model can reduce the impact of occluded *** addition,through summing the similar blocks in face images can make up for the defect of the sub-model which ignores the integrity of ***,the method based on fuzzy comprehensive evaluation is utilized for decision-level fusion to the outputs by these two ways of *** results on AR face database show that this method has state-of-the-art classification accuracy and also robustness to occlusion.

关键词： Face recognition Single training sample Adaptive weighting Fuzzy comprehensive evaluation Sub-model

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AGE ESTIMATION AND GENDER CLASSIFICATION OF FACIAL IMAGES BASED ON LOCAL DIRECTIONAL PATTERN

AGE ESTIMATION AND GENDER CLASSIFICATION OF FACIAL IMAGES BA...

引用

2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Min Hu Yaona Zheng Fuji Ren He Jiang Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

In order to improve shortage of Lxical Binary Pattern(LBP) algorithm in the field of facial feature extraction,this paper uses Lxical Directional Pattern(LDP) algorithm to extract the facial features about age estimation and gender *** gradient information is more stable than gray information,LDP algorithm is not affected easily by *** preprocessing images,we use block strategy to represent facial features more *** then,we extract features of each block with LDP algorithm,storing the LDP histogram Finally,we obtain age and gender identification results by Support Vector Machine(SVM).Experimental results on FG-NET face database show that this method has a good effect on age estimation and gender classification.

关键词： feature extraction,LDP,age estimation,gender classification

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Face recognition based on adaptive weighting and fuzzy fusion with single training sample

Face recognition based on adaptive weighting and fuzzy fusio...

引用

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Xiaohua Wang Chao Jin Wei Liu Min Hu Fuji Ren School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory The University of Tokushima Japan

In order to solve the robustness issues for single training sample face recognition under occlusion conditions, this paper presents a recognition method based on adaptive weighting and fuzzy fusion. In our method, the information entropy expansion mode is introduced in sub-mode method and via adaptively assigning the weights corresponding to each sub-model can reduce the impact of occluded region. In addition, through summing the similar blocks in face images can make up for the defect of the sub-model which ignores the integrity of face. Finally, the method based on fuzzy comprehensive evaluation is utilized for decision-level fusion to the outputs by these two ways of classification. Experimental results on AR face database show that this method has state-of-the-art classification accuracy and also robustness to occlusion.

关键词： Robustness Testing

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Comparative study of two 3-CRU translational parallel manipulators

Comparative study of two 3-CRU translational parallel manipu...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Bin Li Yangmin Li Xinhua Zhao Weimin Ge Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin China Department of Electromechanical Engineering University of Macau Taipa Macao SAR China

ISBN: (纸本)9781479973989

Two types of 3-CRU translational parallel manipulators (TPM) are compared and investigated in this paper. The two 3-CRU TPMs have identical kinematics, but some differences exist in terms of the chain arrangement, one is in fully symmetrical chain arrangement, called symmetrical 3-CRU TPM, the other one is in asymmetrical chain arrangement, called asymmetrical 3-CRU TPM. This paper focuses on discussing the differences between the two 3-CRU TPMs in kinematics, workspace and performance. This study provides insights into parallel manipulators with identical kinematics arranged differently.

关键词： Manipulators Joints Kinematics Actuators Layout Rails Integrated circuits

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FACE RECOGNITION BASED ON DOUBLE COMPLEMENTARY SPACE AND MULTI-DECISION CLASSIFICATION

FACE RECOGNITION BASED ON DOUBLE COMPLEMENTARY SPACE AND MUL...

引用

2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Fuji Ren Yanqiu Li Liangfeng Xu Min Hu Xiaohua Wang Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology Faculty of Engineering The University of Tokushima770-8506TokushimaJapan

In order to overcome the one-sidedness and limitations of a single subspace in feature extraction and classification,we propose a face recognition method that extracts features in double complementary space and utilize multi-decision for *** the feature extraction stage,we use ICA and LPE algorithm as the first layer in complementary space to extract global and local features of the face ***,for the purpose of solving the problem that features extracted by ICA are lack of classification information,we further extract classification information from the independent features extracted by ICA on the condition that the FLDA and DCV algorithm are used as the second layer in the complementary *** the classification stage,the test samples are firstly projected to the independent *** that,projected the samples which are difficult to identify to the LPE space and reclassified ***,the results are been determined *** results on ORL database show that the proposed method can effectively improve the recognition rate.

关键词： algorithms complementary space multi-decision classification face recognition

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Face recognition based on double complementary space and multi-decision classification

Face recognition based on double complementary space and mul...

引用

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Fuji Ren Yanqiu Li Liangfeng Xu Min Hu Xiaohua Wang School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory Faculty of Engineering The University of Tokushima Tokushima Japan

ISBN: (纸本)9781467369541

In order to overcome the one-sidedness and limitations of a single subspace in feature extraction and classification, we propose a face recognition method that extracts features in double complementary space and utilize multi-decision for classification. In the feature extraction stage, we use ICA and LPE algorithm as the first layer in complementary space to extract global and local features of the face images. Then, for the purpose of solving the problem that features extracted by ICA are lack of classification information, we further extract classification information from the independent features extracted by ICA on the condition that the FLDA and DCV algorithm are used as the second layer in the complementary space. In the classification stage, the test samples are firstly projected to the independent subspace. After that, projected the samples which are difficult to identify to the LPE space and reclassified them. Finally, the results are been determined comprehensively. Experimental results on ORL database show that the proposed method can effectively improve the recognition rate.

关键词： Feature extraction Classification algorithms Face recognition Face Data mining Entropy Databases

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