FAST WAY FOR MOVING OBJECT TRACKING BASED ON BALLOON SNAKE WITH REGION INFORMATION

A novel Snake model with region information is proposed to detect and track moving objects. Generally, the region-information-based approach is sensitive to illumination changes and small movement in the background, while the edge-information-based approach often obtains incorrect results for ambiguous images. The two types of information are introduced in computing the image force. Edge-information-based features make the algorithm fast and robust, and region information makes the active confour energy function obtains correct results for ambiguous images. Furthermore, an automatic contour initialization method using double difference images is given to meet the requirement of video sequence tracking. Meanwhile, a simple forecast section is added to estimate the position of the contour in the algorithm so that it can improve the convergence speed of the active contour. Experimental results show that the computation time of the algorithm is less than 0.1 s/frame. And it can be applied to a real-time system.

Side Information Generation Based on Hierarchical Motion Estimation in Distributed Video Coding

The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

An improved partial SPIHT with classified weighted rate-distortion optimization for interferential multispectral image compression

Based on the property analysis of interferential multispectral images, a novel compression algorithm of partial set partitioning in hierarchical trees （SPIHT） with classified weighted rate-distortion optimization is presented. After wavelet decomposition, partial SPIHT is applied to each zero tree independently by adaptively selecting one of three coding modes according to the probability of the significant coefficients in each bitplane. Meanwhile the interferential multispectral image is partitioned into two kinds of regions in terms of luminous intensity, and the rate-distortion slopes of zero trees are then lifted with classified weights according to their distortion contribution to the constructed spectrum. Finally a global rate- distortion optimization truncation is performed. Compared with the conventional methods, the proposed algorithm not only improves the performance in spatial domain but also reduces the distortion in spectral domain.