Given a digital video clip, this invention describes how to efficiently compute the motion vectors and motion trajectory of each identified video object for facilitating various commonly encountered visual applications, such as video compression for transmission and archiving, security and surveillance monitoring, and search-by-query required in the Internet search engine or digital library.

System and method for motion vector generation and analysis of digital video clips

In this paper, a new image-denoising filter that is based on the standard median (SM) filter is proposed. In our method, a threshold and the standard median is used to detect noise and change the original pixel value to a newer that is closer to or the same as the standard median. We also incorporate the center weighted median (CWM) filter in our method. With our experimental results, we have made a comparison among our method, the standard median (SM) filter, the center weighted median (CWM) filter, and the tri-state median (TSM) filter, in which our method proves to be superior.

An Adaptive Median Filter for Image Denoising

We propose an approach to unsupervised segmentation of moving video objects (VOs) over the MPEG compressed domain. The proposed algorithm utilizes the homogeneity property of the spatiotemporally localized VO's information (macroblock motion vectors (MVs) and DCT's DC coefficients) in order to achieve segmentation with an accuracy of 8/spl times/8 DCT block size. First, macroblock MVs are utilized to identify the locations of moving VOs. DC coefficients are then exploited to achieve finer boundary segmentation. For achieving both objectives, a maximum entropy fuzzy clustering algorithm is proposed to classify MVs and DC coefficients into homogeneous regions, respectively. Experimental results show that the developed algorithm can accurately segment VOs with an accuracy of 8/spl times/8 DCT block size without any user intervention.

Spatiotemporal segmentation of moving video objects over MPEG compressed domain

With the development of information technology, people access information more and more rely on the network, and more than 80% of the information in the network is replaced by multimedia technology represented by images. Therefore, the research on image processing technology is very important, but most of the research on image processing technology is focused on a certain aspect. The research results of unified modeling on various aspects of image processing technology are still rare. To this end, this paper uses image denoising, watermarking, encryption and decryption, and image compression in the process of image processing technology to carry out unified modeling, using wavelet transform as a method to simulate 300 photos from life. The results show that unified modeling has achieved good results in all aspects of image processing.

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Application research of digital media image processing technology based on wavelet transform

In this paper, a novel random-valued impulse noise (RVIN) detection based-on neural network is proposed. In order to precise noise detection, the feed-forward neural network with back-propagation training algorithm is applied. Five features of noisy images are extracted and used as input of the proposed network. Thus, the uncorrupted and corrupted pixels can be precisely classified. Experimental results confirm the superiority of the method in contrast to similar techniques in the noise detection. Moreover, we find the best compromise between RVIN undetection and misdetection.

A novel random-valued impulse noise detector based on MLP neural network classifier

We observed that certain fundamental concerns commonly exist in some state-of-the-art switching-based median filters: (i) fixed thresholding for the pre-assumed noise density, (ii) the noise decision accuracy at high density impulse noise, and (iii) the filtering scheme adopted in response to pixel characteristic type identified. In this paper, we propose a novel noise adaptive soft-switching median (NASM) filter to effectively address the above-mentioned issues and achieve much improved filtering performance in terms of efficiency in removing impulse noise and robustness against noise density variations. Experimental results also reveal that the performance of our NASM filter is fairly close to that of ideal-switching median filter.

How-Lung Eng

Papers

Noise adaptive soft-switching median filter for image denoising