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We propose a novel object tracking algorithm based on modeling the target appearance in a joint space. In contrast with traditional histogram-based trackers which discard all spatial information, the joint space takes both the photometric and spatial information into account. Within this joint space, the target is modeled in a Gaussian mixtures manner where a richer description of the target is captured...
There have been several stereo matching methods that perform well under the circumstance of color consistency. However, various factors including radiometric and device variations between images will drop color consistency and then seriously degrade the performance of those methods. In this paper, we propose a robust method to cover these situations. We use a measurement combining SIFT descriptor...
A robust object tracking algorithm is proposed in this paper based on an on-line discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration...
In natural image matting, various kinds of algorithms have been recently proposed. Moreover, alpha matting results have also been generated for comparison and composition into new backgrounds. However, all these methods have to make an alpha matte comparison to the ground truth so that one can get the final pixel-wised evaluation of these results. Nevertheless, while the input datasets are just used...
This paper designs a novel hiding strategy based on an equivalence relation, which can remarkably enhance the quality of stego image without sacrificing the security and capacity of original steganography schemes. According to a constructed equivalence relation based on the capacity of hiding units, all hiding units can be partitioned into equivalence classes. Following that, the hiding procedure...
Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force search to perform object tracking on the generated probability map, which is obtained from the classification results; however, performing probabilistic object tracking on the probability map is almost unexplored. This paper...
In this paper, a novel standard variance feature is proposed for background modeling in dynamic scenes involving waving trees and ripples in water. The standard variance feature is the standard variance of a set of pixels' feature values, which captures mainly co-occurrence statistics of neighboring pixels in an image patch. The background modeling method based on standard variance feature includes...
Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) challenge many traditional background subtraction methods. In this paper, we present a novel background subtraction approach for dynamic scenes, in which the background is modeled in a multi-resolution framework. First, for each level of the pyramid, we run an independent mixture of Gaussians models (GMM)...
We present a novel feature extraction framework, Neighboring Image Patches Embedding (NIPE), for robust and efficient background modeling. We divide image into patches and represent each image patch as a NIPE vector. Then, the background model of each image patch is constructed as a group of weighted adaptive NIPE vectors. The NIPE feature vector, whose components are similarities between current...
Traditional background subtraction methods model only temporal variation of each pixel. However, there is also spatial variation in real word due to dynamic background such as waving trees, spouting fountain and camera jitters, which causes the significant performance degradation of traditional methods. In this paper, a novel spatial-temporal nonparametric background subtraction approach (STNBS) is...
We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary...
Background subtraction in dynamic scenes is an important and challenging task. In this paper, we present a novel and effective method for dynamic background subtraction based on covariance matrix descriptor. The algorithm integrates two distinct levels: pixel level and region level. At the pixel level, spatial properties that are obtained from pixel coordinate values, and appearance properties, i...
Traditional background modeling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poorly in dynamic scenes. In this paper, we present a solution to this problem. We first extend the local binary patterns from spatial domain to spatio-temporal domain, and present a new online dynamic texture extraction operator,...
Two main restrictions exist in state-of-the-art text detection algorithms: 1. Illumination variance; 2. Text-background contrast variance. This paper presents a robust text characterization approach based on local Haar binary pattern (LHBP) to address these problems. Based on LHBP, a coarse-to-fine detection framework is presented to precisely locate text lines in scene images. Firstly, threshold-restricted...
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