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Automated learning systems used to extract useful information from musical scripts, play a major role in optical music recognition. Optical music recognition or OMR has been widely used to extract the musical notations and knowledge from old scripts and thus enclose lot of importance in retrieving historical data. The field of pattern recognition and knowledge representation has to be symmetrically...
The scanned text image is a non editable image though it has the text but one can not edit it or make any change, if required, to that scanned document. This provides a basis for the optical character recognition (OCR) theory. OCR is the process of recognizing a segmented part of the scanned image as a character. The overall OCR process consists of three major sub processes like pre processing, segmentation...
An abandoned object detection system is presented and evaluated using benchmark datasets. The detection is based on a simple mathematical model and works efficiently at QVGA resolution at which most CCTV cameras operate. The pre-processing involves a dual-time background subtraction algorithm which dynamically updates two sets of background, one after a very short interval (less than half a second)...
This paper proposes an effective background subtraction technique in still camera videos, to track objects with high degree of sensitivity, accuracy and low false detections. The method involves applying a Bayesian learning technique to update parameters of clusters formed by pixel observations at a particular spatial position. The proposed method also overcomes the limitation of having a heuristically...
Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. This uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection methods. Addition of a fidelity term facilitates the proposed scheme to remove the noise while preserving edges. This method is general in the sense that it can be incorporated into any of...
This paper presents a technique for performing unsupervised clustering of satellite images using a unique 'sampling-resampling' based Bayesian learning method. The multi-band pixel values of the satellite image are expected to form a certain number of clusters. The parameters of these clusters are learnt using a Bayesian approach. This technique is unsupervised in the sense that no separate training...
This paper presents multicore implementation of an adaptive correlation tracking technique to track aerial target. The principle novelty of the proposed algorithm is its adaptiveness to changes in target size, illumination and target occlusion, etc during tracking. Tracking window size is adjusted according to the target size and tracking window is displaced depending on the tracking error generated...
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