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Object tracking is a critical task in surveillance and activity analysis. One main issue in tracking is illumination variation. We propose a method which is robust to illumination by incorporating a feature that is less variant to illumination. The proposed feature is a reflectance histogram obtained using sparsity constrained non-negative matrix factorization (NMFsc). Using NMFsc, illumination and...
Object tracking is a critical task in surveillance and activity analysis. Two main issues for tracking are appearance (illumination) and structural (size of a target) variations of the object. We propose a method which is robust and addresses these issues by incorporating features that are less variant to these changes. The proposed features are mean local binary pattern (mLBP), an illumination invariant...
Object tracking is critical to visual surveillance and activity analysis. The color based mean shift has been addressed as an effective and fast algorithm for tracking. But it fails in case of objects with low color intensity, clutter in background and total occlusion for several frames. We present a new scheme based on multiple feature integration for visual tracking. The proposed method integrates...
Visual tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variations in illumination. In this paper, we propose a novel algorithm based on discrete cosine transform (DCT) to handle illumination variations, since illumination variations are mainly reflected in the low-frequency band. For instance, low illumination in a frame leads...
Content Based Image Retrieval (CBIR) is a technique for retrieving images from a large database without using keyword annotation. Many researchers are developing various techniques for reducing the semantic gap between the results obtained and the user's needs. This paper provides an efficient two stage CBIR method along with a new classical weight allocation technique. These weights are asssigned...
Content-based image retrieval (CBIR) systems demonstrate excellent performance at computing low-level features from pixel representations. Its output does not reflect the overall desire of the user. The systems perform poorly in extracting high-level (semantic) features that include objects and their meanings, actions and feelings. This is, referred to as the semantic gap, and has necessitated current...
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