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This paper concentrates on the development of an efficient and robust backward solution for the forward sparse greedy algorithms and applies this solution in the field of speech compression. All existing backward solutions are based on constraining more and more weights to zero while re-optimizing the remaining nonzero weights to compensate. Our approach is termed Backward Replacement (BRe) algorithm and its idea is to replace the k − sparse weights vector with a k − sparse symmetric matrix. The key result of this paper showed that, the replacement approach has demonstrated successfully the superiority over existing backward elimination algorithms in both enhancing the compression capabilities of the forward greedy algorithms, and reducing the time complexity. [ABSTRACT FROM AUTHOR]

Abstract: A feature extraction algorithm for palm-print recognition based on two dimensional discrete wavelet transform is proposed in this paper, which efficiently exploits the local spatial variations in a palm-print image. The palm-image is segmented into several spatial modules and a palm-print recognition scheme is developed, which extracts histogram-based dominant wavelet features from each of these local modules. The effect of modularization in terms of the entropy content of the palm-print images has been analyzed. The selection of dominant features for the purpose of recognition not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, the modularization of the palm-print image enhances the discriminating capabilities of the proposed features and thereby results in high within-class compactness and between-class separability of the extracted features. Different types of Daubechies wavelets (in terms of use of number of vanishing moments, i.e., db1–db10) have been utilized for the purpose of feature extraction and the effect upon the recognition performance has been also investigated. In order to further reduce the feature dimension, a principal component analysis is performed. It is found from our extensive experimentations on different palm-print databases that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods. [Copyright &y& Elsevier]

Abstract: Multi-chip module (MCM) substrates are designed for packing two or more semiconductor chips. On these substrates, there are open faults in the wiring, which are electrical disconnections. We must therefore test the substrates to detect open faults, and it is essential to establish an efficient method of testing them. One type of test method uses two probes. Two probes, each touching one edge (end) of an inter-chip wiring, are used to check for the presence of faults. Testing is complete when we have confirmed that no faults exist on the MCM substrate. The objective is to minimize the time to complete testing, that is, our aim is to design efficient routes for the two probes. In this paper, we propose a novel approach of formulating the routing problem as a shortest path problem with covering constraints (SPCC) and we also propose three algorithms for the SPCC. In computational experiments, we show that our formulation and algorithms outperform the existing method. [Copyright &y& Elsevier]

Abstract: This paper proposes a probability formulation that unifies both single-image deblurring and multi-image denoising using variational inference. The proposed formulation is based on a theoretical analysis that compares denoising and deblurring in the same probabilistic framework, and supported by a practical approach that deal with general motion that creates HDR images in the presence of spatially varying motion. Based on this formulation, a new algorithm for deblurring a noisy and blurry image pair is presented. Besides, we provide also an approach that combines existing optical flow and image denoising techniques for High Dynamic Range imaging. [Copyright &y& Elsevier]

With the development of big data technology, privacy protection of computer vision algorithm has become a hot research field. To protect privacy of both terminal and cloud in the object detection algorithm, Jin et al. proposed a method to represent an image with multiple random sub-images. The cloud detects objects from these random sub-images instead of the original image. However, there is a fatal flaw in this method that the cloud can recover the original image according to some rules of random sub-images. In this paper, a more secure object detection algorithm based on the random matrix was proposed. The original image belonging to terminal and parameters of detection algorithm belonging to cloud were hidden in irreversible matrices, which is the key to protect privacy of both sides. In order to further reduce the computational complexity, this algorithm was improved by replacing irreversible matrices with random vectors. [Display omitted] • Perform object detection while protecting the privacy of both the terminal and the cloud. • Combine Viola-Jones object detector with random matrix/vector description method. • Achieve double-blind object detection without losing any detection accuracy. [ABSTRACT FROM AUTHOR]