Sujoy Kumar Biswas

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—In this paper we propose a graph theoretic technique for recognizing human actions at a distance in a video by modeling the visual senses associated with poses. The proposed methodology follows a bag-of-word approach that starts with a large vocabulary of poses (visual words) and derives a refined and compact codebook of key poses using centrality measure(More)
In this paper, we propose a graph theoretic approach for recognizing interactions between two human performers present in a video clip. We watch primarily the human poses of each performer and derive descriptors that capture the motion patterns of the poses. From an initial dictionary of poses (visual words), we extract key poses (or key words) by ranking(More)
We propose a methodology for recognizing actions at a distance by watching the human poses and deriving descriptors that capture the motion patterns of the poses. Human poses often carry a strong visual sense (intended meaning) which describes the related action unambigu-ously. But identifying the intended meaning of poses is a challenging task because of(More)
We propose a generative model for constructing an efficient set of distinctive textures for recognizing architectural distortion in digital mammograms. In the first layer of the proposed two-layer architecture, the mammogram is analyzed by a multiscale oriented filter bank to form texture descriptor of vectorized filter responses. Our model presumes that(More)
One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among(More)
We propose a graph theoretic technique for recognizing actions at a distance by modeling the visual senses associated with human poses. Identifying the intended meaning of poses is a challenging task because of their variability and such variations in poses lead to visual sense ambiguity. Our methodology follows a bag-of-words approach. Here "word" refers(More)
A graph theoretic approach is proposed to recognize interactions (e.g., handshaking, punching, etc.) between two human performers in a video. Pose descriptors corresponding to each performer in the video are generated and clustered to form initial codebooks of human poses. Compact codebooks of dominating poses for each of the two performers are created by(More)
—Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here we propose a mid-level attribute in the form of multidimensional template, or tensor, using Local Steering Kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically(More)
The pseudorapidity density of charged particles, dN_{ch}/dη, at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. For the 5% most central collisions, we measure a value of 1943±54. The rise in dN_{ch}/dη as a function of sqrt[s_{NN}] is steeper than that observed in proton-proton(More)