Block-based Community in China's Social Housing Development:A Case Study on Old City Renovation of Kashgar,Xinjiang Uygur Autonomous Region

Through the analysis of the international definition and classification of slums,this paper explores the development of China's social housing system and the renovation of the Old City of Kashgar.It argues that one of the issues in China's social housing system is to solve the problems of the scarcity of spatial elements and the lack of developmental driving force in large mixed communities of the Han and minority nationalities.Then it examines the elements of renovation and approaches based on a survey of the local residents in different parts of Kashgar City.Comparing the international development of traditional residential quarters and block-based communities,the paper points out that the block-based community is preferred for its impartiality and sustainability,and applies this mode to the renovation of the Old City of Kashgar in the form of design guidelines.

Novel algorithm for pose-invariant face recognition

By combining the AdaBoost modular locality preserving projection （AMLPP） algorithm and the locally linear regression （LLR） algorithm, a novel pose-invariant algorithm is proposed to realize high-accuracy face recognition under different poses. In the training stage of this algorithm, the AMLPP is employed to select the crucial frontal blocks and construct effective strong classifier. According to the selected frontal blocks and the corresponding non-frontal blocks, LLR is then applied to learn the linear mappings which will be used to convert the non-frontal blocks to visual frontal blocks. During the testing of the learned linear mappings, when a non-frontal face image is inputted, the non-frontal blocks corresponding to the selected frontal blocks are extracted and converted to the visual frontal blocks. The generated virtual frontal blocks are finally fed into the strong classifier constructed by AMLPP to realize accurate and efficient face recognition. Our algorithm is experimentally compared with other pose-invariant face recognition algorithms based on the Bosphorus database. The results show a significant improvement with our proposed algorithm.

Novel Efficient De-blocking Method for Highly Compressed Images

Due to coarse quantization, block-based discrete cosine transform(BDCT) compression methods usually suffer from visible blocking artifacts at the block boundaries. A novel efficient de-blocking method in DCT domain is proposed. A specific criterion for edge detection is given, one-dimensional DCT is applied on each row of the adjacent blocks and the shifted block in smooth region, and the transform coefficients of the shifted block are modified by weighting the average of three coefficients of the block. Mean square difference of slope criterion is used to judge the efficiency of the proposed algorithm. Simulation results show that the new method not only obtains satisfactory image quality, but also maintains high frequency information.

Compression of MR Images Using DWT by Comparing RGB and YCbCr Color Spaces

This paper consists of a lossy image compression algorithm dedicated to the medical images doing comparison of RGB and YCbCr color space. Several lossy/lossless transform coding techniques are used for medical image compression. Discrete Wavelet Transform (DWT) is one such widely used technique. After a preprocessing step (remove the mean and RGB to YCbCr transformation), the DWT is applied and followed by the bisection method including thresholding, the quantization, dequantization, the Inverse Discrete Wavelet Transform (IDWT), YCbCr to RGB transform of mean recovering. To obtain the best compression ratio (CR), the next step encoding algorithm is used for compressing the input medical image into three matrices and forward to DWT block a corresponding containing the maximum possible of run of zeros at its end. The last step decoding algorithm is used to decompress the image using IDWT that is applied to get three matrices of medical image.

Learning Structure Models with Context Information for Visual Tracking

Tracking objects that undergo abrupt appearance changes and heavy occlusions is a challenging problem which conventional tracking methods can barely handle. To address the problem, we propose an online structure learning algorithm that contains three layers： an object is represented by a mixture of online structure models （OSMs） which are learnt from block-based online random forest classifiers （BORFs）. BORFs are able ~o handle occlusion problems since they model local appearances of the target. To further improve the tracking accuracy and reliability, the algorithm utilizes mixture relational models （MRMs） as multi-mode context information to integrate BORFs into OSMs. Furthermore, the mixture construction of OSMs can avoid over-fitting effectively and is more flexible to describe targets. Fusing BORFs with MRMs, OSMs capture the discriminative parts of the target, which guarantees the reliability and robustness of our tracker. In addition, OSMs incorporate with block occlusion reasoning to update our BORFs and MRMs, which can deal with appearance changes and drifting problems effectively. Experiments on challenging videos show that the proposed tracker performs better than several state-of-the-art algorithms.

Steganalysis of MSU Stego Video Based on Block Matching of Interframe Collusion and Motion Detection

MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis method against MSU, which uses the chessboard character of MSU embedded video, proposes a down-sample block-based collusion method to estimate the original frame and checks the chessboard mode of the different frame between tested frame and estimated frame to detect MSU steganographic evidences. To reduce the error introduced by severe movement of the video content, a method that abandons severe motion blocks from detecting is proposed. The experiment results show that the false negative rate of the proposed algorithm is lower than 5%, and the false positive rate is lower than 2%. Our algorithm has significantly better performance than existing algorithms. Especially to the video that has fast motion, the algorithm has more remarkable performance.