In this paper authors describe the Curvelet representation of image (object) with dominant angular subbands in Curvelet domain and analyse about the energy distribution for each subbands at different angles. Curvelet transform is localized not only in position (the spatial domain) and scale (the frequency domain), but also in orientation. Here energy of dominant orientations (angles) in a given...

Sparse recovery aims to reconstruct signals that are sparse in a linear transform domain from a heavily underdetermined set of measurements. The success of sparse recovery relies critically on the knowledge of transform domains that give compressible representations of the signal of interest. Here we consider twoand three-dimensional images, and investigate various multi-dimensional transforms ...

Crops are highly cultivating all around the world and vary depending upon the quality of the land in each country. Crop classification is necessary to tell what crops they are and if they are good for that specific area. This classification will help agronomists to decide crop pattern and cultivation practice. Plants are a major source of food stuff, medicine and industry. However it is an impo...

Many signal and image processing applications have benefited remarkably from the theory of sparse representations. In the classical synthesis model, the signal is assumed to have a sparse representation under a given known dictionary. The algorithms developed for this framework mainly operate in the representation domain. Recently, a new model has been introduced, the cosparse analysis one, in ...

Abelian codes constitute a class of codes that includes cyclic codes as a special case. It is shown that the general class of abelian codes can be characterized in the transform domain using discrete Fourier transform (DFT) over finite fields with appropriate mixed radix Manuscript received July 31, 1991. B. S. Rajan is with the Electrical Engineering Department, Indian Institute of Technology,...

The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last thirty years many transforms such as wavelets, curvelets, contourlets, surfacelets, shearlets, and many other types of ‘x-lets’ have been developed. Such transform were leveraged to resolve this issue of sparse representations. In this work we compare the pro...

Distributed video coding depends heavily on the virtual channel model. Due to the limitations of the side information estimation one stationary model does not properly describe the virtual channel. In this work the correlation noise is modelled per slice to obtain location-specific correlation noise model. The resulting delay from the lengthy Slepian-Wolf (SW) codec input is also reduced by red...

In this paper, a new Transform Domain implementation of the well known Multilayer Perceptron Neural Network is presented. With the Transform Domain implementation, the input of the Neural Network can be represented in a more compact manner and the elements of the input vector become uncorrelated. The new Transform Domain Multilayer Perceptron (TDMLP) Neural Network is applied for the problem of...

This paper shows that members of the fourier transform family are the only linear transforms that have a convolution theorem, that is, that can replace O(N 2) operations of a convolution in a time domain by O(N) operations in a transform domain. Generally, there is an additional cost to compute the transform itself. Our observation is motivated by recent activity in wavelet and subband decompos...

The Fractional Fourier Transform (FRFT) is a generalization of the classical Fourier transform and has many applications in several areas including signal processing, optics and quantum mechanics. This paper presents a low pass filter, designed by using convolution theorem for FRFT. In the design of filter, Blackman window function is used and it has been observed that proposed FRFT domain filt...