Live Image Colour Segmentation Using Different Methods of ANN

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Rosan Patel, Seema Baghel, Sweta Patel, Rosy Mishra

Abstract

Machine learning is a new dimension of science since last 2 decade which motivates algorithms that can learn from data by building a model, based on inputs and using that to make predications or decisions, rather than following only explicitly programmed instructions. Machine learning is sometimes conflated with data mining, which focuses more on exploratory data analysis. Data mining is the extraction of interesting (non-trivial, implicit, previously unknow and potential useful) patterns of knowledge from huge amount of data In computer vision image segmentation is the process of partitioning a digital image into multiple segments (set of pixels, also known as super-pixels). The goals of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. After that by considering region co-ordinates it separates all color in different figure.

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How to Cite
, R. P. S. B. S. P. R. M. “Live Image Colour Segmentation Using Different Methods of ANN”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 1, Jan. 2018, pp. 21-26, doi:10.17762/ijritcc.v6i1.1374.
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