Edge detection research paper

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Edge Detection

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Using Canny's criteria to derive a recursively implemented optimal edge detector | SpringerLink

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Using Canny's criteria to derive a recursively implemented optimal edge detector

Thermal images are used for detection and identification of edges in any object due to varying atmospheric conditions. In this paper, five algorithms, viz, Robert, Sobal, Prewitt, laplacian and Canny edge detection algorithm are discussed and their performances are compared for optimal edge detection. Edge detection play a linchpin role in image analysis and able to solve complex problems, it is an important step to identify object segmentation and help in feature extraction. It basically works on grey level of pixels, it is used to track targets, compress data, recognition of object etc. Performance of algorithms is decided in considering accuracy and speed.
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Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing , machine vision and computer vision , particularly in the areas of feature detection and feature extraction.
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