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Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improvement is still limited by low-level feature representation. Therefore, this paper mainly studies the description of object structure from both(More)
Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain(More)
Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust(More)
Real scene video surveillance always involves low resolutions, lack of illumination or cluttered environments, which leads to insufficiency of discriminative details for the target. In this situation, discrimination based tracking methods could fail. To address this problem, this paper presents an adaptive multi-feature integration method in terms of(More)
This paper presents a system of data decomposition and spatial mixture modeling for part based models. Recently, many enhanced part based models (with e.g., multiple features, more components or parts) have been proposed. Nevertheless, those enhanced models bring high computation cost together with the risk of over-fitting. To tackle this problem, we(More)
The European Pharmacopoeia (Ph. Eur.) monograph for Erythropoietin Concentrated Solution describes a capillary zone electrophoresis method for identification of recombinant human erythropoietin. However, this method has shown poor reproducibility due to inadequate capillary conditioning. We have modified the Ph. Eur. method to make it more robust and(More)
Deformable object matching, which is also called elastic matching or deformation matching, is an important and challenging problem in computer vision. Although numerous deformation models have been proposed in different matching tasks, not many of them investigate the intrinsic physics underlying deformation. Due to the lack of physical analysis, these(More)
—Object detection is a fundamental task in the area of computer vision. Deformable part based model obtains great success in the past several years, demonstrating very promising performance. A lot of papers emerge on part based model such as structure learning, learning more discriminative features. To help researchers better understand the existing visual(More)