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Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction...
Handwriting recognition has always been a challenging task in image processing and pattern recognition. India is a multi-lingual, multi-script country, where eighteen official scripts are accepted and there are over a hundred regional languages. The feature extraction method is probably the most effective method in achieving high recognition performance. In this study we proposed a zone-based feature...
This paper presents a novel approach for recognition of unconstrained handwritten Marathi characters. The recognition is carried out using multistage feature extraction and classification scheme. The initial stages of feature extraction are based upon the structural features and the classification of the characters is done according to their parameters. The final stage of feature extraction employs...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
In this paper we propose an automatic system that recognizes continuous Arabic-Urdu Alphabet scripts through mouse in real- time based on Artificial Neural Network (ANN). The proposed neural network is trained using traditional back-propagation algorithm for self supervised neural network which provides the system with great learning ability and thus has proven highly successful in training for feed-forward...
In the last decade significant progress in computer vision based control of unmanned ground vehicles (UGV) has been achieved. However, until now textural information has been somewhat less effective than color or laser range information. In this paper we propose a computer vision based cross country segmentation system that is capable of distinguishing cross-country road, grass and trees during day-time...
A novel method of flame color image segmentation based on multilayer feedforward network is proposed. The training sample sets select the color and location information of the flame image in HSV color model as features. After preprocessing the training samples are normalized and input to multilayer feedforward network. By training with Levenberg-Marquardt algorithm the segmentation result is presented...
In this paper, a particle swarm optimization (PSO) based camera calibration approach is presented to determine the external and internal calibration parameters from the knowledge of a given set of points in object space. First, the image formation model for a pinhole camera is formulated in terms of a feed-forward neural network (NN) and then this neural network is trained using particle swarm optimization...
This paper discusses a method for abnormal motion detection and its real-time implementation on a smart camera. Abnormal motion detection is a surveillance technique that only allows unfamiliar motion patterns to result in alarms. Our approach has two phases. First, normal motion is detected and the motion paths are trained, building up a model of normal behaviour. Feed-forward neural networks are...
Urdu compound character recognition is a scarcely developed area and requires robust techniques to develop as Urdu being a family of Arabic script is cursive, right to left in nature and characters change their shapes and sizes when they are placed at initial, middle or at the end of a word. The developed system consists of two main modules segmentation and classification. In the segmentation phase...
The problem of identifying cosmic gamma ray events out of charged cosmic ray background in Cherenkov telescopes is one of the key problems in very high energy gamma ray astronomy. Separation between gamma-like and hadron-like events is performed by a Bayesian ensemble of neural networks and Markov chain Monte Carlo methods for model parameters optimization. The results are discussed in terms of the...
Fingerprint feature detection is one of the key techniques of automatic fingerprint identification. There are some problems of rotation and shift in the present fingerprint feature detection. This paper has given an algorithm of bifurcation detection based on neural network template matching. Correlative matching is to calculate the correlative value between template and target image according to...
In general, there is small perturbation between a pattern obtained by a certain acquisition way and the corresponding actual pattern in real word. Such small perturbation may cause disadvantage to several performance of a fuzzy neural network. Thus, a new concept is established in the paper that the robustness of a feed-forward fuzzy associative memory to perturbations of training pattern pair. Then...
The PCB traces produce electromagnetic field when excited by a signal source. The trace acts as a source of emission when impedance mismatch or a discontinuity along the length of the trace occurs which may interfere with other equipments placed in its closer proximity. In this paper a two stage feed forward neural network using Back Propagation algorithm is used to identify the radiating trace. The...
Palmprint identification is the means of recognizing an individual from the database using his/ her palmprint features. Palmprint is easy to capture, requires cheaper equipment and is more acceptable by the public. Moreover, palmprint is also rich in features. Wavelet transform is a multi-resolution analysis tool that can extract palm lines in different resolution levels. In low-resolution level,...
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