Muhammad Rizwan Khokher

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This article presents a new method for violent scene detection using super descriptor tensor decomposition. Multi-modal local features comprising auditory and visual features are extracted from Mel-frequency cepstral coefficients (including first and second order derivatives) and refined dense trajectories. There is usually a large number of dense(More)
This article presents a new method for crowd behavior recognition, using dynamic features extracted from dense trajectories. The histogram of oriented gradient and motion boundary histogram descriptors are computed at dense points along motion trajectories, and tracked using median filtering and displacement information obtained from a dense optical flow(More)
A non-blind color image watermarking scheme using principle component analysis, discrete wavelet transform and singular value decomposition is proposed. The color components are uncorrelated using principle component analysis. The watermark is embedded into the singular values of discrete wavelet transformed sub-band associated with principle component(More)
Audio-visual recognition systems rely on efficient feature extraction. Many spatio-temporal interest point detectors for visual feature extraction are either too sparse, leading to loss of information, or too dense resulting in noisy and redundant information. Furthermore, interest point detectors designed for a controlled environment can be affected by(More)
This work deals with segmentation of the gray scale, color and texture images using graph cuts. From the input image, a graph is constructed using intensity, color and texture profiles of the image simultaneously. Based on nature of the image, a fuzzy rule based system is designed to find the weight that should be given to a specific image feature during(More)
This work deals with segmentation of the gray scale, color and texture images using graph cuts. From an input image, a graph is constructed using intensity, color and texture profiles of the image simultaneously (i.e., intensity and texture for gray scale images and color and texture for color images). Based on the nature of image, a fuzzy rule based system(More)
This work deals with the graph cuts based image segmentation of gray scale, color and texture images. Multilevel graph partitioning approach is used along with the normalized cuts framework. From the input image, an optimized graph is constructed using intensity, color and texture profiles of the image simultaneously. Based on nature of the image, a fuzzy(More)
A fuzzy logic based active contour model for medical image segmentation is proposed. Image local features are incorporated in active contour model. Fuzzy logic is used to assign weights to pixels. Higher weights are assigned to pixels having less entropy and local variance whereas Lower weights are assigned to pixels having high entropy and local variance.(More)
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