Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
In this paper, we propose a new variational model for image restoration by incorporating a nonlocal TV regularizer\nand a nonlocal Laplacian regularizer on the image. The two regularizing terms make use of nonlocal comparisons\nbetween pairs of patches in the image. The new model can be seen as a nonlocal version of the CEP-L2 model.\nSubsequently, an algorithm combining the alternating directional minimization and the split Bregman iteration is\npresented to solve the new model. Numerical results verified that the proposed method has better performance for\nimage restoration than CEP-L2 model, especially for low noised images....
The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific\nautoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended\nmethodology for detecting ANAs in clinic practice. However, the currently practiced manual detection system suffers\nfrom serious problems due to subjective evaluation. In this paper, we present an automated system for HEp-2 cells\nclassification. We adopt a bag-of-words (BoW) framework which has shown impressive performance in image\nclassification tasks because it can obtain discriminative and effective image representation. However, the information\nloss is inevitable in the coding process. Therefore, we propose a linear local distance coding (LLDC) method to capture\nmore discriminative information. Our LLDC method transforms original local feature to more discriminative local\ndistance vector by searching for local nearest few neighbors of the local feature in the class-specific manifolds. The\nobtained local distance vector is further encoded and pooled together to get salient image representation. The LLDC\nmethod is combined with the traditional coding methods to achieve higher classification accuracy. Incorporated with\na linear support vector machine classifier, our proposed method demonstrated its effectiveness on two public\ndatasets, namely, the International Conference on Pattern Recognition (ICPR) 2012 dataset and the International\nConference on Image Processing (ICIP) 2013 training dataset. Experimental results show that the LLDC framework can\nachieve superior performance to the state-of-the-art coding methods for staining pattern classification of HEp-2 cells....
In this paper, we extend the Richardson-Lucy (RL) method to block-iterative versions, separated BI-RL, and interlaced\nBI-RL, for image deblurring applications. We propose combining algorithms for separated BI-RL to form block\nartifact-free output images from separately deblurred block images. For interlaced BI-RL to accelerate the iteration,\nwe propose an interlaced block-iteration algorithm on down-sampled blocks of the observed image. Simulation\nstudies show that separated BI-RL and interlaced BI-RL achieve desired goals in Gaussian and diagonal deblurrings....
In this paper, we propose a new distributed video coding (DVC) method, with hierarchical group of picture (GOP)\nstructure. Coding gain of DVC can be significantly improved by enlarging GOP size for slow-moving frames. The\nproposed DVC decoder estimates a side information (SI) frame and transmits motion vectors (MVs) of the SI to the\nproposed encoder. Using the received MVs from the decoder, the proposed encoder can generate a predicted SI\n(PSI), which is the same as the SI in the decoder, and estimate the quality of PSI with minimal computational\ncomplexity. The proposed method decides the best coding mode among key, Wyner-Ziv (WZ), and skip modes, by\nestimating rate-distortion costs. Based on the selected best coding mode, the best GOP size can be automatically\ndetermined. As the GOP size is adaptively decided depending on the SI quality, entropy and parity bits can be\neffectively consumed. Experimental results show that the proposed algorithm is around 0.80 dB better in Bj�¸ntegaard\ndelta (BD) bitrate than an existing conventional DVC system....
In this paper, we propose a cascade classifier for high-performance on-road vehicle detection. The proposed system\ndeliberately selects constituent weak classifiers that are expected to show good performance in real detection\nenvironments. The weak classifiers selected at a cascade stage using AdaBoost are assessed for their effectiveness in\nvehicle detection. By applying the selected weak classifiers with their own confidence levels to another set of image\nsamples, the system observes the resultant weights of those samples to assess the biasing of the selected weak\nclassifiers. Once they are estimated as biased toward either positive or negative samples, the weak classifiers are\ndiscarded, and the selection process is restarted after adjusting the weights of the training samples. Experimental\nresults show that a cascade classifier using weak classifiers selected by the proposed method has a higher\ndetection performance....
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