Siva Chandra

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A 2D function, representing a digital image, is a surface in 3D space. Curvature of such a surface can be exploited to detect ridge and valley like features from images. In this paper, we present an analysis of such curvature based ridge and valley detection techniques and come up with a description for the different classes of ridge and valley profiles(More)
The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D image function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from images. One such property, the curvature of the image surface, can be used to detect features characterised by a sharp bend in the surface.(More)
Unsupervised methods for automatic vessel segmentation from retinal images are attractive when only small datasets, with associated ground truth markings, are available. We present an unsupervised, curvature-based method for segmenting the complete vessel tree from colour retinal images. The vessels are modeled as trenches and the medial lines of the(More)
The growth or energy demand in response to industrialization, urbanization, and societal affluence has led to an extremely uneven global distribution of primary energy consumption. The sun, wind, waves and geothermal heat are renewable energy sources that will never run out. They are perpetual, or self-renewing. The rate of consumption does not exceed the(More)
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