There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation...

We discuss the problem of nding sparse representations of a class of signals. We formalize the problem and prove it is NP-complete both in the case of a single signal and that of multiple ones. Next we develop a simple approximation method to the problem and we show experimental results using arti cially generated signals. Furthermore,we use our approximation method to nd sparse representations...

This paper introduces a novel data structure used to store sparse matrices optimally – minimizing the space of the matrix representation and the time complexity of an access to the matrix element. The size of our data structure is close to the information theoretic minimum – it differs in the second order term – and permits constant access to the matrix elements and a constant amortized time wi...

Recognition of people by means of their biometric characteristics is very popular among the society. There are various biometric techniques including fingerprint recognition, face recognition and eye detection that are used for the privacy and security purposes in different applications. Among all these techniques, fingerprint recognition has gain more popularity for personal identification due...

Sparse representation has attracted a great deal of attention in the past decade. Famous transforms such as discrete Fourier transform, wavelet transform and singular value decomposition are used to sparsely represent the signals. The aim of these transforms is to reveal certain structures of a signal and representation of these structures in a compact form. Therefore, sparse representation pro...

Sparse representation based classification (SRC) has been proved to be a simple, effective and robust solution to face recognition. As it gets popular, doubts on the necessity of enforcing sparsity starts coming up, and primary experimental results showed that simply changing the l1-norm based regularization to the computationally much more efficient l2-norm based non-sparse version would lead ...

In this paper, application of sparse representation (factorization) of signals over an overcomplete basis (dictionary) for signal classification is discussed. Searching for the sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that includes two terms: one that measures the signal reconstruction error and another that measures the s...

Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature s...

We study the representation number for various sparse graphs; in particular, we give an exact formula for graphs with a single edge and complete binary trees and an improved lower bound for the representation number of the hypercube. We also study the prime factorization of the representation number of graphs with a single edge.