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Full-Text Articles in Physical Sciences and Mathematics
Kernel Pca Of Hog Features For Posture Detection, Peng Cheng, Wanqing Li, Philip Ogunbona
Kernel Pca Of Hog Features For Posture Detection, Peng Cheng, Wanqing Li, Philip Ogunbona
Professor Philip Ogunbona
Motivated by the non-linear manifold learning ability of the Kernel Principal Component Analysis (KPCA), we propose in this paper a method for detecting human postures from single images by employing KPCA to learn the manifold span of a set of HOG features that can effectively represent the postures. The main contribution of this paper is to apply the KPCA as a non-linear learning and open-set classification tool, which implicitly learns a smooth manifold from noisy data that scatter over the feature space. For a new instance of HOG feature, its distance to the manifold that is measured by its reconstruction …
Human Detection Based On Weighted Template Matching, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Human Detection Based On Weighted Template Matching, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
This paper proposes a new two-stage human detection method involving matching and verification. A Bayesian framework is developed to verify the matching score obtained from a weighted distance measure. Performance evaluation indicates that the proposed method is able to utilize the flexible matching scheme and produce superior true positive, true negative and low misclassification rates.
Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
Professor Philip Ogunbona
Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance features are extracted. We introduce a novel local binary pattern (LBP) descriptor, called Non-Redundant LBP (NRLBP), to encode local appearance of human. The proposed method was evaluated and compared with other state-of-the-art human detection methods on two commonly used datasets: …
A Novel Template Matching Method For Human Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
A Novel Template Matching Method For Human Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
Professor Philip Ogunbona
This paper proposes a novel weighted template matching method. It employs a generalized distance transform (GDT) and an orientation map (OM). The GDT allows us to weight the distance transform more on the strong edge points and the OM provides supplementary local orientation information for matching. Based on the matching method, a two-stage human detection method consisting of template matching and Bayesian verification is developed. Experimental results have shown that the proposed method can effectively reduce the false positive and false negative detection rates and perform superiorly in comparison to the conventional Chamfer matching method.
Object Detection Using Non-Redundant Local Binary Patterns, Duc Thanh Nguyen, Zhimin Zong, Philip Ogunbona, Wanqing Li
Object Detection Using Non-Redundant Local Binary Patterns, Duc Thanh Nguyen, Zhimin Zong, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
Local Binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant Local Binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object’s appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human’s appearance in a human detection task. …