D. Egfin Nirmala

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In this paper, we present a novel method for adaptive fusion of multimodal surveillance images, based on Non-Subsampled Contourlet Transform (NSCT), which has an improved performance over Visual Sensor Networks (VSN). In sensor networks, energy consumption and bandwidth are the main factors that determine the lifetime of the sensors. In order to reduce the(More)
In this paper, a multimodal image fusion technique using Shift invariant Discrete Wavelet Transform (SIDWT) and Support Vector Machines (SVM) suitable for surveillance applications is proposed. This technique uses SIDWT for multiresolution decomposition as it is translation invariant. A Support Vector Machine is trained to select the coefficient blocks with(More)
With the recent developments in the field of visual sensor technology, multiple imaging sensors are used in several applications such as surveillance, medical imaging and machine vision, in order to improve their capabilities. The goal of any efficient image fusion algorithm is to combine the visual information, obtained from a number of disparate imaging(More)
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