To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for...To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.展开更多
Basic block is the foundation of clothing construction design because it is the media between body and clothes and the fitness of clothes should be based on the accuracy of basic block. That needs us to recognize body...Basic block is the foundation of clothing construction design because it is the media between body and clothes and the fitness of clothes should be based on the accuracy of basic block. That needs us to recognize body not to record it. This paper reports the Algorithm of woman body fuzzy pattern recognition. It is organized in three sections:(i) extracting woman body feature; (ii) establishing membership functions of feature indexes;(iii) presenting an Algorithm for woman body fuzzy pattern recognition by example.展开更多
In this paper,we propose a novel approach to recognise human activities from a different view.Although appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views,the...In this paper,we propose a novel approach to recognise human activities from a different view.Although appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views,there must be some regularity among the same action sequences of different views.Selfsimilarity matrices appear to be relative stable across views.However,the ability to effectively realise this stability is a problem.In this paper,we extract the shape-flow descriptor as the low-level feature and then choose the same number of key frames from the action sequences.Self-similarity matrices are obtained by computing the similarity between any pair of the key frames.The diagonal features of the similarity matrices are extracted as the highlevel feature representation of the action sequence and Support Vector Machines(SVM) is employed for classification.We test our approach on the IXMAS multi-view data set.The proposed approach is simple but effective when compared with other algorithms.展开更多
Intravascular ultrasound can provide clear real-time cross-sectional images,including lumen and plaque.In practice,to identify the plaques tissues in different pathological changes is very important.However,the graysc...Intravascular ultrasound can provide clear real-time cross-sectional images,including lumen and plaque.In practice,to identify the plaques tissues in different pathological changes is very important.However,the grayscale differences of them are not so apparent.In this paper a new textural characteristic space vector was formed by the combination of Co-occurrence Matrix and fraction methods.The vector was projected to the new characteristic space after multiplied by a projective matrix which can best classify those plaques according to the Fisher linear discriminant.Then the classification was completed in the new vector space.Experimental results found that the veracity of this classification could reach up to 88%,which would be an accessorial tool for doctors to identify each plaque.展开更多
Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of conceal- ment strategies, disruptive coloration is thought to visually fragment an animal's' outline, thereby ...Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of conceal- ment strategies, disruptive coloration is thought to visually fragment an animal's' outline, thereby reducing its rate of discovery. Here, I propose two non-mutually exclusive hypotheses for how disruptive camouflage functions, and describe the visual me- chanisms that might underlie them. (1) The local edge disruption hypothesis states that camouflage is achieved by breaking up edge information. (2) The global feature disruption hypothesis states camouflage is achieved by breaking up the characteristic features of an animal (e.g., overall shape or facial features). Research clearly shows that putatively disruptive edge markings do increase concealment; however, few tests have been undertaken to determine whether this survival advantage is attributable to the distortion of features, so the global feature disruption hypothesis is under studied. In this review the evidence for global feature disruption is evaluated. Further, I address if object recognition processing provides a feasible mechanism for animals' features to influence concealment. This review concludes that additional studies are needed to test if disruptive camouflage operates through the global feature disruption and proposes future research directions [Current Zoology 61 (4): 708-717, 2015].展开更多
基金The National Natural Science Foundation of China(No.61273266,61231002,61301219,61375028)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092130004)the Natural Science Foundation of Shandong Province(No.ZR2014FQ016)
文摘To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.
文摘Basic block is the foundation of clothing construction design because it is the media between body and clothes and the fitness of clothes should be based on the accuracy of basic block. That needs us to recognize body not to record it. This paper reports the Algorithm of woman body fuzzy pattern recognition. It is organized in three sections:(i) extracting woman body feature; (ii) establishing membership functions of feature indexes;(iii) presenting an Algorithm for woman body fuzzy pattern recognition by example.
基金supported by a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(Information and Communication Engineering)the Natural Science Foundation of Jiangsu Province under Grant No.BK2010523+2 种基金the National Natural Science Foundation of China under Grants No.61172118,No.61001152the University Natural Science Research Project of Jiangsu Province under Grant No.11KJB510012the Scientific Research Foundation of Nanjing University of Posts and Telecommunications under Grant No.NY210073
文摘In this paper,we propose a novel approach to recognise human activities from a different view.Although appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views,there must be some regularity among the same action sequences of different views.Selfsimilarity matrices appear to be relative stable across views.However,the ability to effectively realise this stability is a problem.In this paper,we extract the shape-flow descriptor as the low-level feature and then choose the same number of key frames from the action sequences.Self-similarity matrices are obtained by computing the similarity between any pair of the key frames.The diagonal features of the similarity matrices are extracted as the highlevel feature representation of the action sequence and Support Vector Machines(SVM) is employed for classification.We test our approach on the IXMAS multi-view data set.The proposed approach is simple but effective when compared with other algorithms.
文摘Intravascular ultrasound can provide clear real-time cross-sectional images,including lumen and plaque.In practice,to identify the plaques tissues in different pathological changes is very important.However,the grayscale differences of them are not so apparent.In this paper a new textural characteristic space vector was formed by the combination of Co-occurrence Matrix and fraction methods.The vector was projected to the new characteristic space after multiplied by a projective matrix which can best classify those plaques according to the Fisher linear discriminant.Then the classification was completed in the new vector space.Experimental results found that the veracity of this classification could reach up to 88%,which would be an accessorial tool for doctors to identify each plaque.
文摘Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of conceal- ment strategies, disruptive coloration is thought to visually fragment an animal's' outline, thereby reducing its rate of discovery. Here, I propose two non-mutually exclusive hypotheses for how disruptive camouflage functions, and describe the visual me- chanisms that might underlie them. (1) The local edge disruption hypothesis states that camouflage is achieved by breaking up edge information. (2) The global feature disruption hypothesis states camouflage is achieved by breaking up the characteristic features of an animal (e.g., overall shape or facial features). Research clearly shows that putatively disruptive edge markings do increase concealment; however, few tests have been undertaken to determine whether this survival advantage is attributable to the distortion of features, so the global feature disruption hypothesis is under studied. In this review the evidence for global feature disruption is evaluated. Further, I address if object recognition processing provides a feasible mechanism for animals' features to influence concealment. This review concludes that additional studies are needed to test if disruptive camouflage operates through the global feature disruption and proposes future research directions [Current Zoology 61 (4): 708-717, 2015].