Dictionary learning algorithms have received widespread ac ceptance when it comes to data analysis and signal represen tations problems. These algorithms alternate between two stages: the sparse coding stage and dictionary update stage. In all existing dictionary learning algorithms the use of spar sity has been limited to the sparse coding stage while pre senting differences in the dictionary update stage which can be achieved sequentially or in parallel. The singular value decomposition (SVD) has been successfully used for sequen tial dictionary update. In this paper we propose a dictionary learning algorithm that include a sparsity constraint also in the dictionary update stage. The cost function used to include sparsity in the dictionary update stage is derived using the link between SVD and rank one matrix approximation. The effec tiveness of the proposed dictionary learning method is tested on synthetic data and an image processing application. The results reveal that including a sparsity constraint in the dictio nary update stage is not a bad idea.Index Terms-Dictionary learning, sparsity, SVD, se quential update, penalized rank one approximation. 1. IN TRODUCTION Dictionary learning methods have been successfully used in a number of signal and image processing applications among them image denoising [1][2], face recognition [3], compres sion [4] and fMRI data analysis [S][6][7]. These methods seek to uncover a linear multivariable latent structure in the observed data by imposing the reasonable constraint that the estimates of the observed variables are sparse linear func tions of some unknown regressors (atoms of the dictionary). The other particularity of these methods is that this set of regressors or the dictionary is also tuned iteratively to find the optimal linear multivariable latent model. Dictionary learning algorithms consist of two stages: a sparse coding stage and a dictionary update stage. In the first stage the dictionary is kept constant and the sparsity assumption is used to produce sparse linear approximations of the ob served data. In the second stage, the coefficients of the linear combination are kept constant and the dictionary is updated to minimize a certain cost function. The dictionary learning methods iterate between these two stages until convergence. The perfonnance of these methods strongly depend on the dictionary update stage since most of these methods share a similar sparse coding stage. Dictionary learning through the dictionary update stage can also be made sequential or paral lel. For a specific strategy or goal (characterized by the cost function used to update the dictionary), the parallel approach will generally require a lower computational cost than the sequential approach that uses the same strategy. While the parallel update approach may be preferred for its advantage in computational complexity, the sequential approach generally offers better results because it generates finer tuned dictionary atoms. Probabilistic and non-probabilistic approaches have been adop...
Binarkation of document images is 1, JntroductionImage binarization or thrcsholding is an important tool in image processing and computer vision, to extract the object pixels in an image from the background pixels. Image binarization is central to many applications including document image analysis (printed characters, logos, graphical content, and musical scores are important as objects), map processing (fines, lzgends and characters necd to be cxtracted), scene processing, quality inspection of materials, cell images, segmentation of various image modalities for nondestructive testing (NDT) applications (ultrasonic images, eddy cumnt images, thermal images, X-ray computed tomography, laser scanning confocal microscopy, extraction of edge field and spatioteinporal segmentation of video images). A number of methods have already been proposed for image binarization but unfortunately, most of them are very much specific for a few applications. Thus, it can be said that a binarization (thresholding) inethod may work well for one application but its performance can be unsatisfactory for another application.Bi-level image is used as a pre-processing unit in several applications. The use of binary images decreases computational load for the overall application. These applications include document analysis, optical character recognition system, scene matching, quality inspection of materials etc. The binarization process computes the threshold value that differentiate object and background pixels. Under varying illumination and noise, the binarization can become a challenging job. A number of factors contribute to complicate the thresholding scheme inchding ambient illumination, variance of gray levels within the object and the background, inadequate contrast, object shape and size non-commensurate with the scene. A wrong selection of threshold value may misinterpret thc background pixel and can classify it as object and vice versa, resulting in overall degradation of system performance. The determination of a threshold itself i s application dependent since one threshold may work with one application and may not work with other one. In document analysis, binarization is sensitive to noisc, surrounding illumination, gray Ievel distribution, local shading effects? inadequate contrast, the presence o f dense nontext components such as photographs, etc. while at the same time, the merges,' fractures and other deformations in the character shapes affects the threshold value io OCR system. On the other hand, the simplification is needed that benefits the overall system processing characteristics (computational load, algorithm complexity, real-time requirements etc). So all these challenges and problems makes binarization a difficult task.There are a number of important performance requirements that need to be considered while binarizing gray Ievel images. With reference to the proposed algorithm, following sections describe all the rehtsd concepts briefly along with the rclatcd work done in the past. The algorithmi...
-Data fusion of thermal and visual images is a solution to overcome the drawbacks present in individual thermal and visual images. Data fusion using different approach is discussed and results are presented in this paper. Traditional fusion approaches don't produce useful results for face recognition. An optimized approach for face data fusion is developed which works for face data fusion equally well as for non-face images. This paper presents the implementation of Human face recognition system using proposed optimized data fusion of visual and thermal images. Gabor filtering technique, which extracts facial features, is used as a face recognition technique to test the effectiveness of the fusion techniques. It has been found that by using the proposed fusion technique Gabor filter can recognize face even with variable expressions and light intensities.
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