Vol 6, No 2 (2011)
Pattern Recognition Theory
Interactive Fuzzy Model Based Recognition of Handwritten Numerals 154-165
Ramana Murthy, M Hanmandlu
In this fuzzy model based character recognition, the features extracted from a character are used to form the fuzzy sets. Assuming these fuzzy sets have the overlapping/interactive information, this paper develops an Interactive Fuzzy Model by devising new fuzzy rules for linking them.
Perceptual Color Representation of the Face: Extracting the Color of Skin, Hair and Eyes 166-174
Levente Sajo, Kornel Bertok, Attila Fazekas
A color representation of the face is presented to detect the color of the skin, eyes and hair in high resolution color images.
Bandwidth Selection for Mean-Shift Based Unsupervised Learning Techniques: A Unified Approach via Self-Coverage 175-192
Jochen Einbeck
The mean shift is a simple but powerful tool emerging from the computer science literature which shifts a point to the local center of mass around this point.
Online Adaptive Hierarchical Clustering in a Decision Tree Framework 201-229
Jayanta Basak
We present an online adaptive hierarchical clustering algorithm in a decision tree framework. Our model consists of an online adaptive binary tree and a code formation layer.
A Comperative Approach to Cluster Validation 230-243
Renata Avros, Mati Golani, Zeev Volkovich
The estimation of the appropriate number of clusters is a known problem in cluster analysis, that affects the clusters stability.
Multistage Handwritten Marathi Compound Character Recognition Using Neural Networks 253-268
Sushama Deepak Shelke, Shaila Apte
Compound character is a special feature of Marathi script, derived from Devanagari. It joins two or more characters in various ways forming a new character.
Evaluation of Different Feature Extractors and Classifiers for Offline Handwritten Devnagari Character Recognition 269-277
Brijmohan Singh, Ankush Mittal, Debashish Ghosh
Research on Optical Character Recognition (OCR) of Devnagari script is very challenging due to the complex structural properties of the script that are not observed in most other scripts.
Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case 298-306
Alok Sharma, Kuldip Paliwal
The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix.
Learning Factor Patterns in Exploratory Factor Analysis Using the Genetic Algorithm and Information Complexity as the Fitness Function 307-325
Hongwei Yang, Hamparsum Bozdogan
This paper presents a new, and novel data-adaptive expert approach to determining the best factor pattern structure in exploratory factor analysis (EFA) models using a clever genetic algorithm (GA) hybridized with information theoretic complexity (ICOMP) criterion as the fitness function.
Applications
Feature Dimensionality Reduction for Example-Based Image Super-Resolution 130-139
Liangjun Xie, Dalong Li, Steven J. Simske
Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution.
A Novel Region Based Multimodality Image Fusion Method 140-153
Tanish Hemalbhai Zaveri, Mukesh A. Zaveri
In recent years the region based image fusion algorithms has drawn the attention of researches because it is more meaningful to combine regions rather than pixels.
Multiphase Segmentation of 3D Flash Lidar Images 193-200
Frank Crosby, SungHa Kang
Three-dimensional Flash Lidar camera is a new technology which allows a single camera to take multiple images in a fast succession.
Assessment in Subsets of MNIST Handwritten Digits and Their Effect in the Recognition Rate 244-252
Gerardo Miramontes De León, Arturo Moreno-Báez, Rafael Magallanes-Quintanar, Ricardo David Valdez-Cepeda
This paper reports the performance of a character recognition test using MNIST handwritten-digit database. The presented assessment is twofold, first it shows the performance of a recognition test based on a very simple feature extraction method, secondly it shows a disparity within the database that may be important when recognition algorithms are compared.
Error Level Fusion of Multimodal Biometrics 278-297
Madasu Hanmandlu, Jyotsana Grover, Shantaram Vasikarla, Hari Mohan Gupta
This paper presents a multimodal biometric system based on error level fusion. Two error level fusion strategies, one involving the Choquet integral and another involving the t-norms are proposed.