Shiv Dutt Joshi

Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estimating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.

ADVERTISING DATA ANALYSIS USING ROUGH SETS MODEL

This study explores the use of rough-set methods for marketing decision support systems in the retail business. A tutorial presentation of Rough Set Data Analysis (RSDA) in the context of knowledge discovery from time series databases is given. We show how an RSDA model can be used to develop a marketing decision support system which can capture the complex relationships between marketing factors, such as advertising and promotion, and the total impact on sales levels in order to find influential advertising strategies. This information is used by the business manager to make faster and better strategy decisions for the business to survive in the rapidly changing and competitive environments. The data set used for RSDA application example contains weekly investments in different media categories: TV, radio, cinema, morning press, evening press, popular press, special interest press, and outdoor posters; for seven makes of cars in the Swedish market.

Some studies on nonpolynomial interpolation and error analysis

Abstract In this paper, we propose nonpolynomial and Hermite nonpolynomial interpolation with multiple parameters and present method to determine optimal value of parameters which generate minimum error in approximation. The generalized error analysis results for nonpolynomial and Hermite nonpolynomial interpolations are derived. We establish theoretical relationship among nonpolynomial, polynomial interpolation and the Fourier series, and propose solution to Runge’s phenomenon through nonpolynomial interpolation. The Hermite nonpolynomial cubic spline, nonpolynomial cubic spline interpolation methods and their error analysis are presented. Numerical simulations are carried out for the analysis of error in cubic spline interpolations. Proposed method is applied to the analysis of various time series to show comparison in errors between polynomial and nonpolynomial spline interpolations, and to Empirical Mode Decomposition (EMD) to illustrate practical usefulness of the results.

Locating text in images using matched wavelets

In this paper we have proposed a novel scheme for locating text regions in an image. The method is based on multiresolution wavelet analysis. We used matched wavelets to capture textural characteristics of image regions. A clustering based approach has been proposed for estimating globally matched wavelets (GMWs) for a given collection of images. Using these GMWs, we generate feature vectors for segmentation and identification of text regions in an image. Our method, unlike most of the other methods, does not require any a priori information about the font, font size, scripts, geometric transformation, distortion or background texture. We have tested our method on various categories of images like license plates, posters, hand written documents and document images etc. The results show proposed method to be a robust, versatile and effective tool for text extraction from images.

The Fourier decomposition method for nonlinear and non-stationary time series analysis

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of ‘Fourier intrinsic band functions’ (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

A model guided document image analysis scheme

This paper presents a new model-based document image segmentation scheme that uses XML-DTDs (eXtensible Markup Language Document Type Definitions). Given a document image, the algorithm has the ability to select the appropriate model. A new wavelet-based tool has been designed for distinguishing text from non-text regions and characterization of font sizes. Our model-based analysis scheme makes use of this tool for identifying the logical components of a document image.

Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms

In this paper, we propose a method for the analysis and classification of electroencephalogram (EEG) signals using EEG rhythms. The EEG rhythms capture the nonlinear complex dynamic behavior of the brain system and the nonstationary nature of the EEG signals. This method analyzes common frequency components in multichannel EEG recordings, using the filter bank signal processing. The mean frequency (MF) and RMS bandwidth of the signal are estimated by applying Fourier-transform-based filter bank processing on the EEG rhythms, which we refer intrinsic band functions, inherently present in the EEG signals. The MF and RMS bandwidth estimates, for the different classes (e.g., ictal and seizure-free, open eyes and closed eyes, inter-ictal and ictal, healthy volunteers and epileptic patients, inter-ictal epileptogenic and opposite to epileptogenic zone) of EEG recordings, are statistically different and hence used to distinguish and classify the two classes of signals using a least-squares support vector machine classifier. Experimental results, with 100xa0% classification accuracy, on a real-world EEG signals database analysis illustrate the effectiveness of the proposed method for EEG signal classification.

Enhancement of Old Manuscript Images

In this paper we address the issue of enhancement in the quality of scanned images of old manuscripts. Small portions of the text in these manuscripts have degraded with time and are not readable. We propose a segmentation based histogram matching scheme for enhancing these degraded text regions. To automatically identify the degraded text we use a matched wavelet based text extraction algorithm followed by MRF(Markov Random Field) post processing. Additionally we do background clearing to improve the quality of results. This method does not require any a priori information about the font, font size, background texture or geometric transformation. We have tested our method on a variety of manuscript images. The results show proposed method to be a robust, versatile and effective tool for enhancement of manuscript images.

A Novel Discrete Distribution and Process to Model Self-Similar Traffic

Network traffic is modeled as Poisson process for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. Packet arrivals deviate considerably from Poissonity and follow statistical self-similar and long-range dependent behavior (LRD). We propose a novel discrete distribution for the counting process and show that for such a process the interarrivals are Pareto distributed. This new process is self-similar and exhibits long-range dependent behavior.

Detection of splicing forgery using wavelet decomposition

Authenticity of an image is an important issue in many social areas such as Journalism, Forensic investigation, Criminal investigation and Security services etc. and digital images can be easily manipulated with the help of sophisticated photo editing software and high-resolution digital cameras. So there is a requirement for the implementation of new powerful and efficient algorithms for forgery detection of a tampered images. The splicing is the common forgery in which two images are combine and make a single composite and the duplicated region is retouched by performing operations like edge blurring to get the appearance of the authentic image. In this paper, we have proposed a new computationally efficient algorithm for splicing (copy-create) forgery detection of an image using block matching method. The proposed method achieve an accuracy of 87.75% within a small processing time by modeling the threshold.