Abhijeet V. Nandedkar

A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture

This paper proposes a fuzzy min-max neural network classifier with compensatory neurons (FMCNs). FMCN uses hyperbox fuzzy sets to represent the pattern classes. It is a supervised classification technique with new compensatory neuron architecture. The concept of compensatory neuron is inspired from the reflex system of human brain which takes over the control in hazardous conditions. Compensatory neurons (CNs) imitate this behavior by getting activated whenever a test sample falls in the overlapped regions amongst different classes. These neurons are capable to handle the hyperbox overlap and containment more efficiently. Simpson used contraction process based on the principle of minimal disturbance, to solve the problem of hyperbox overlaps. FMCN eliminates use of this process since it is found to be erroneous. FMCN is capable to learn the data online in a single pass through with reduced classification and gradation errors. One of the good features of FMCN is that its performance is less dependent on the initialization of expansion coefficient, i.e., maximum hyperbox size. The paper demonstrates the performance of FMCN by comparing it with fuzzy min-max neural network (FMNN) classifier and general fuzzy min-max neural network (GFMN) classifier, using several examples

A Granular Reflex Fuzzy Min–Max Neural Network for Classification

Granular data classification and clustering is an upcoming and important issue in the field of pattern recognition. Conventionally, computing is thought to be manipulation of numbers or symbols. However, human recognition capabilities are based on ability to process nonnumeric clumps of information (information granules) in addition to individual numeric values. This paper proposes a granular neural network (GNN) called granular reflex fuzzy min-max neural network (GrRFMN) which can learn and classify granular data. GrRFMN uses hyperbox fuzzy set to represent granular data. Its architecture consists of a reflex mechanism inspired from human brain to handle class overlaps. The network can be trained online using granular or point data. The neuron activation functions in GrRFMN are designed to tackle data of different granularity (size). This paper also addresses an issue to granulate the training data and learn from it. It is observed that such a preprocessing of data can improve performance of a classifier. Experimental results on real data sets show that the proposed GrRFMN can classify granules of different granularity more correctly. Results are compared with general fuzzy min-max neural network (GFMN) proposed by Gabrys and Bargiela and with some classical methods.

Object recognition using reflex fuzzy min-max neural network with floating neurons

This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy Min-Max Neural Network (RFMN) architecture with “Floating Neurons”. RFMN is capable to learn mixture of labeled and unlabeled data which enables training under partial supervision. Learning under partial supervision is of high importance for the practical implementation of pattern recognition systems, as it may not be always feasible to get a fully labeled dataset for training or cost to label all samples is not affordable. The proposed system is tested on shape data-base available online, Marathi and Bengali digits. Results are compared with “General Fuzzy Min-Max Neural Network” proposed by Gabrys and Bargiela.

A general fuzzy min max neural network with compensatory neuron architecture

This paper proposes “A General Fuzzy Min-max neural network with Compensatory Neurons architecture”(GFMCN) for pattern classification and clustering. The network is capable of handling labeled and unlabeled data simultaneously, on-line. The concept of compensatory neurons is inspired from reflex system of the human brain. Fuzzy min-max neural network based architectures use fuzzy hyperbox sets to represent the data cluster or classes. An important stage in the training phase of these techniques is to manage the hyperbox overlaps and containments. In case of GFMCN, compensatory neurons are trained to handle the hyperbox overlap and containment. Inclusion of these neurons with a new learning approach has improved the performance significantly for labeled as well as unlabeled data. Moreover accuracy is almost independent of the maximum hyperbox size. The advantage of GFMCN is that it can learn data in a single pass (on-line). The performance of GFMCN is compared with “General Fuzzy Min-max neural network” proposed by Gabrys and Bargiela on several datasets.

Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features

Offline handwritten character recognition is the process of recognizing given characters from the large set of characters. OCR system mainly focuses on the recognition of printed or handwritten characters of a scanned image. The proposed system extracts features that are based only on gradient of image which is helpful in exact recognition of characters. A technique to recognize handwritten Devanagari characters using combination of quadratic and SVM classifiers is presented in this paper. Features used are directional features that are strength, angle and histogram of gradient (SOG, AOG, HOG). Using a Gaussian filter, the strength and the angle features are down sampled to obtain a feature vector of 392 dimensions. These features are finally concatenated with HOG feature. Applying these to the combination of quadratic and SVM classifiers to obtain maximum accuracy of 95.81% using 3 fold cross validation.

Conversion and Recognition of Handwritten Devnagari Character String into Printed Character String Using KNN

This paper presents a system for the conversion of handwritten string of Devnagari character to printed character string by using character segmentation approach. 11 different statistical features of segmented characters are extracted which are compared with features extracted from printed string of characters available in training data for cross validation purpose using Knearest neighborhood (kNN) algorithm. Use of handwritten string of Devnagari characters written in different styles and converting it into printed string makes the system more prone to real life application. System mainly works on segmentation of characters using bounding box, after segmentation, features are extracted which is compared with training feature set. We have analyzed our system with existing Devnagari handwritten character recognition systems. In given framework, we have focused on a creating database in different styles and recognizing them as printed characters. Keywords— K-Nearest Neighborhood Algorithm, Connected Components Labeling, Bounding Box, Statistical Feature, Feature Extraction Technique, Handwritten Devnagari string segmentation, Object extraction, Printed String of Characters.

Computer vision based offset error computation for web printing machines using FPGA

The use of computer vision based approach has started to bring the intelligence to many of the modern machineries. Such kind of high performance image processing systems can be efficiently built using Field Programmable Gate Arrays (FPGAs). This paper presents the design and implementation of FPGA based Computer Vision System for offset error computation of a new proposed registration mark pattern in 4-color web offset printing machines. The color printing quality of offset machine degrades due to a genuine problem of registration error caused by improper alignment of the prints from each process color section. This system can be used in an automated registration control system for web printing press that will control the position of each of CMYK cylinders depending on offset error calculated which will improve printing quality.