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Crowd density analysis is crucial for crowd monitoring and management. This paper proposes a novel method for crowd density analysis. According to the framework, input images are firstly divided into patches, and each patch is associated with a density label based on its texture features. Finally, local information is synthesized for global density estimation. Local image content is described by features...
A novel pedestrian detection method that integrates context information with slide window search is proposed. The method applies notions such as corner, motion, and appearance to localize pedestrians in far-field videos without performing brute-force-search. The corners direct attention to a set of conspicuous locations as the starting points for searching. And motion detection restricts the searching...
Based on the physical model of fogged images, a new method is proposed to enhance the visibility of a single image adaptively. In order to restrain the edge halation produced by virtual air light defogging method, the concept of neighboring pixel's relative depth information is brought up. Input images may have different extent of disturbance of fog or haze, and they need different strength of defogging...
This paper presents a new probabilistic local binary pattern (PLBP), an extension of existing local binary pattern (LBP), for face verification. Unlike LBP employing the sign of the difference to express the result of comparing two pixels, PLBP employs probability to express it. The advantage is that it can encode the magnitude of the difference, which is useful for face verification but is ignored...
This paper describes a video quality analysis system for inservice monitoring of streamed videos, particularly over mobile/wireless networks. The algorithm adopts the no-reference method, and enables real-time measurement of video quality at any point in the content production and delivery chain using any given video. The technologies developed include no-reference methods for measuring picture freeze,...
This paper proposes a method of adaptive kernel density estimation (KDE) for motion detection. The method selects an adaptive threshold by analyzing probability histogram, which is suitable for different scenes and different moving objects. Then a mechanism of updating background using probability is also provided. It can get relative good background and is useful for motion detection. Moreover it...
Local binary pattern (LBP) is a powerful texture descriptor that is gray-scale and rotation invariant. In this paper, an extension of the original LBP is proposed. LBP operator is adopted in multi-layer block domain, instead of pixel domain. Meanwhile, feature dimension is effectively reduced by dual-histogram LBP (DH-LBP). Combining merits of the two, we propose the advanced LBP (ALBP) and use that...
Global gray-level thresholding techniques such as Otsupsilas method, and local gray-level thresholding techniques such as adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. In this paper, we propose a double-edge...
This paper proposes an extension of marginal fisher analysis (EMFA) for dimensionality reduction and analyzes some properties of both EMFA and linear discriminant analysis (LDA), and finally suggests a synthesized discriminant projection (SDP). SDP takes both global class relationship and local geometry structure into account, which maximizes the distance between marginal points and the distance between...
The main objective of medical image segmentation is to extract and characterize anatomical structures with respect to some input features or expert knowledge. Traditional two-dimensional Otsu method for medical image segmentation is time-consuming computation and become an obstacle in real time application systems. This paper describes a way of medical image segmentation using optimized two-dimensional...
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