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Due to many uncontrolled factors, pedestrian detection is one of the most challenging problems in computer vision. In this paper, a fast and accurate hierarchical method for pedestrian detection with random forests is proposed, which can combine holistic information and local information based on image pyramid model. Image pyramid can effectively realize multi-layer information fusion and hierarchical...
Contour detection is an important and fundamental problem in computer vision that finds numerous applications. In this paper, we propose a learning algorithm for contour detection via random forest. Visual cues that can be extracted easily and efficiently are integrated to learn a detector where the decision of an contour pixel is made independently via the random forest at each location in the image...
We consider the feature extraction problem based on compressive sampling for supervised image classification. Inspired by recently emerged 1D compressive sampling (1DCS) and 2DPCA techniques, a novel 2D compressive sampling method, called 2DCS, using two random underdetermined projections, is proposed. 2DCS data could be effectively used for pattern representation. Moreover, original data could be...
In order to eliminate the effect of the facial expression and illumination condition, as well as to speed up the recognition procedure, we propose a face recognition approach based on sparse representation. First, preprocessing and segmenting the face area from three dimensional (3D) face scans, we also apply coarse to fine registration to ensure the alignment of range images; second, mapping the...
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