Image Edge Detection and Segmentation by using Histogram Thresholding method
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 7
Abstract
A new approach used for image edge deduction, segmentation and normalization illumination under varying lighting conditions are presented. Edge detection refers to the process of identifying and locating sharp by applying smooth and noisy clinical technic in an image. It has favorable applications in the fields such as machine vision, pattern recognition, object recognition, motion analysis, pattern recognition, medical image processing & biomedical imaging. Segmentation refers to the process of partitioning a digital image into the multiple segments using set of pixels as to simplify and change the representation of an image and easier to analyze. Edge detection highlights high frequency components in the image. Edge detection is becomes more arduous when it comes to noisy images. The study focuses on fuzzy concepts based edge detection in smooth and noisy clinical images. Traditional method of edge detection involves convolving the image with an operator (2-D filter) changes in pixel intensity scene which is constructed to be sensitive to large gradients. Edge detectors form a collection of different images and applying local image processing method to locate sharp changes in the intensity function. In this paper, histogram thresholding is proposed in order to help the edge detection and segmentation of image to found robust way regardless of the segmentation approach applying for histogram thresholding algorithm. This paper shows the comparison of edge detection & segmentation techniques under different conditions & variation of intensity pixel value of the selected algorithms. Index Terms: Edge deduction, Image Segmentation, Noise Histogram Equalization, Illumination Normalization, Fuzzy algorithm & Histogram Thresholding.
Authors and Affiliations
Dr. V. S. Manjula
Keywords
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- EP ID EP391854
- DOI 10.9790/9622-0707067683.
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