A complete factorization of paraunitary matrices with pairwise mirror-image symmetry in the frequency domain

The problem of designing orthonormal (paraunitary) filter banks has been addressed in the past. Several structures have been reported for implementing such systems. One of the structures reported imposes a pairwise mirror-image symmetry constraint on the frequency responses of the analysis (and synthesis) filters around π/2. This structure requires fewer multipliers, and the design time is correspondingly less than most other structures. The filters designed also have much better attenuation. In this correspondence, we characterize the polyphase matrix of the above filters in terms of a matrix equation. We then prove that the structure reported in a paper by Nguyen and Vaidyanathan, with minor modifications, is complete. This means that every polyphase matrix whose filters satisfy the mirror-image property can be factorized in terms of the proposed structure

Coding gain in paraunitary analysis/synthesis systems

A formal proof that bit allocation results hold for the entire class of paraunitary subband coders is presented. The problem of finding an optimal paraunitary subband coder, so as to maximize the coding gain of the system, is discussed. The bit allocation problem is analyzed for the case of the paraunitary tree-structured filter banks, such as those used for generating orthonormal wavelets. The even more general case of nonuniform filter banks is also considered. In all cases it is shown that under optimal bit allocation, the variances of the errors introduced by each of the quantizers have to be equal. Expressions for coding gains for these systems are derived

Generalized polyphase representation and application to coding gain enhancement

Generalized polyphase representations (GPP) have been mentioned in literature in the context of several applications. In this paper, we provide a characterization for what constitutes a valid GPP. Then, we study an application of GPP, namely in improving the coding gains of transform coding systems. We also prove several properties of the GPP

Linear phase paraunitary filter banks: theory, factorizations and designs

M channel maximally decimated filter banks have been used in the past to decompose signals into subbands. The theory of perfect-reconstruction filter banks has also been studied extensively. Nonparaunitary systems with linear phase filters have also been designed. In this paper, we study paraunitary systems in which each individual filter in the analysis synthesis banks has linear phase. Specific instances of this problem have been addressed by other authors, and linear phase paraunitary systems have been shown to exist. This property is often desirable for several applications, particularly in image processing. We begin by answering several theoretical questions pertaining to linear phase paraunitary systems. Next, we develop a minimal factorizdion for a large class of such systems. This factorization will be proved to be complete for even M. Further, we structurally impose the additional condition that the filters satisfy pairwise mirror-image symmetry in the frequency domain. This significantly reduces the number of parameters to be optimized in the design process. We then demonstrate the use of these filter banks in the generation of M-band orthonormal wavelets. Several design examples are also given to validate the theory

IMPLEMENTATION OF REJECTION STRATEGIES INSIDE MALAYALAM CHARACTER RECOGNITION SYSTEM BASED ON RANDOM FOURIER FEATURES AND REGULARIZED LEAST SQUARE CLASSIFIER

Robust and reliable recognition are indeed necessary requirements for optical character recognition systems. Distortions present in the document image and the pre-processing errors cause the optical character recognition system to apply rejection policies to achieve reliable recognition in computer assisted applications. The objective of this paper is to implement a robust and reliable character recognition system for Malayalam language. Random Fourier features classified with Regularized Least Square loss function based Regression classifier can approximate the non-linear kernel machines. Baseline Malayalam character recognition based on Random Fourier features and Regularized Least Square regression classifier is implemented in this paper. Up on this baseline character recognition system, rejection strategies are applied and are experimented with real world document images. An improvement in recognition accuracy is achieved with the simulated Malayalam character recognition system at the cost of rejecting character images having low classification score

On developing handwritten character image database for Malayalam language script

The objective of this paper is to build a handwritten character image database for Malayalam language script. Standard handwritten document image databases are an essential requirement for the development and objective evaluation of different handwritten text recognition systems for any language script. Considerable research efforts for handwritten Malayalam character recognition are present in literature. Still, no public domain handwritten image database is available for the Malayalam language. The present work focuses on building an open source handwritten character image database for Malayalam language script. The unique orthographic representation of the Malayalam characters forms the different character classes, and the current version of the database contains 85 character classes frequently used in writing Malayalam text. Handwritten data samples collected from 77 native Malayalam writers. For extracting the character images from the handwritten data sheets, active contour model-based image segmentation algorithm utilized. Recognition experiments conducted on the created character image database by employing different feature extraction techniques. Among the considered feature descriptors, scattering convolutional network-based feature descriptors attain the highest recognition accuracy of 91.05%. Keywords: Malayalam language, Handwritten character recognition, Handwritten character image database, Active contour minimization, Optical character recognition, Feature extractio