The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, d...The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.展开更多
This paper presents a new method for robust and accurate optical flow estimation.The sig- nificance of this work is twofold.Firstly,the idea of bi-directional scheme is adopted to reduce the model error of optical flo...This paper presents a new method for robust and accurate optical flow estimation.The sig- nificance of this work is twofold.Firstly,the idea of bi-directional scheme is adopted to reduce the model error of optical flow equation,which allows the second order Taylor's expansion of optical flow equation for accurate solution without much extra computational burden;Secondly,this paper establishs a new optical flow equation based on LSCM (Local Structure Constancy Model) instead of BCM (Brightness Constancy Model),namely the optical flow equation does not act on scalar but on tensor-valued (ma- trix-valued) field,due to the two reason:(1) structure tensor-value contains local spatial structure information,which provides us more useable cues for computation than scalar;(2) local image structure is less sensitive to illumination variation than intensity,which weakens the disturbance of non-uniform illumination in real sequences.Qualitative and quantitative results for synthetic and real-world scenes show that the new method can produce an accurate and robust results.展开更多
A novel filter for image restoration is proposed in this paper. The filter estimates histogram of original image via input image. It gets a membership function through the histogram, and the membership function contai...A novel filter for image restoration is proposed in this paper. The filter estimates histogram of original image via input image. It gets a membership function through the histogram, and the membership function contains a lot of information of original image. Then a weighted fuzzy mean filter is established based on this membership function; meanwhile, the filter adaptively adopts different filter scale according to the character divergence of image region and intensity of impulsive noise. Experimental result shows that new filter gives superior performance to conventional filters and currently used fuzzy filter.展开更多
文摘The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
基金Supported by the National Natural Science Foundation of China(No.60672153)the Shenzhen Science & Technology Project (No.200424).
文摘This paper presents a new method for robust and accurate optical flow estimation.The sig- nificance of this work is twofold.Firstly,the idea of bi-directional scheme is adopted to reduce the model error of optical flow equation,which allows the second order Taylor's expansion of optical flow equation for accurate solution without much extra computational burden;Secondly,this paper establishs a new optical flow equation based on LSCM (Local Structure Constancy Model) instead of BCM (Brightness Constancy Model),namely the optical flow equation does not act on scalar but on tensor-valued (ma- trix-valued) field,due to the two reason:(1) structure tensor-value contains local spatial structure information,which provides us more useable cues for computation than scalar;(2) local image structure is less sensitive to illumination variation than intensity,which weakens the disturbance of non-uniform illumination in real sequences.Qualitative and quantitative results for synthetic and real-world scenes show that the new method can produce an accurate and robust results.
基金the National Natural Science Foundation of China(No.69972041)
文摘A novel filter for image restoration is proposed in this paper. The filter estimates histogram of original image via input image. It gets a membership function through the histogram, and the membership function contains a lot of information of original image. Then a weighted fuzzy mean filter is established based on this membership function; meanwhile, the filter adaptively adopts different filter scale according to the character divergence of image region and intensity of impulsive noise. Experimental result shows that new filter gives superior performance to conventional filters and currently used fuzzy filter.
