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Salient object detection aims to detect the attractive objects on images and videos. In this paper, we propose a novel salient object detection method for videos based on cross-frame cellular automata. Given a video, we first represent the video frames with super-pixels, and construct a saliency propagation network among super-pixels within a frame and between adjacent frames based on their appearance...
In this paper, with the help of controllable active near-infrared (NIR) lights, we construct near-infrared differential (NIRD) images. Based on reflection model, NIRD image is believed to contain the lighting difference between images with and without active NIR lights. Two main characteristics based on NIRD images are exploited to conduct spoofing detection. Firstly, there exist obviously spoofing...
Recent advances in salient object detection in images have achieved obvious performance in various multimedia applications, but efficient salient object detection in videos is still a challenging problem. In this paper, we propose a novel salient object detection method based on spatio-temporal difference and coherence of video content. Firstly, we initialize the saliency map for each keyframe based...
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 presents a novel algorithm for face recognition based on a single image and a new LBP (Local Binary Pattern) descriptor. The algorithm can be divided into three steps: firstly, calculating both the horizontal and vertical edge maps from the gray image; then extracting LBP histograms from those two edge images; finally, adopting elastic matching for classification. In addition, in order...
Crowd estimation is crucial for crowd monitoring and control. It differs from pedestrian detection or people counting in that no individual pedestrian can be properly segmented in the image. This paper describes a novel and efficient system for crowd density estimation, based on local image texture analysis. A novel indication of local binary pattern feature vector called Advanced LBP is proposed...
This paper proposes an approach for object tracking using particle filter based on an improved color correlogram. The improved color correlogram for representation object contains not only color information but also spatial information, which makes feature more distinctive. Instead of using the whole correlogram matrix as feature, we construct feature vector based on elements of the upper triangular...
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...
In this paper, a novel framework for face recognition based on discriminatively trained orthogonal rank-one tensor projections (ORO) and local binary pattern (LBP) is proposed. LBP is an efficient method for extracting shape and texture information and it is robustness to illumination and expression, while ORO has been successful in appearance based face recognition by finding orthogonal tensors....
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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