Optimal Threshold Selection Based on Random Fuzzy Sets : for Image Segmentation and Analysis

R. Pradeep Kumar Reddy

ISBN 10: 620319574X / ISBN 13: 9786203195743

Published by LAP LAMBERT Academic Publishing, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The extended Canny edge detection technique is proposed to identify the edges efficiently using an automatic selection of optimal threshold values. The proposed work states that the detailed results reveal the superior performance over the traditional canny edge detection algorithm. However proposed algorithm fails for low contrast, multiresolution, and unevenly illuminated images. For the second objective, the gradient profile sharpness (GPS) algorithm is presented to emphasize the impact of illumination contrast on human visual perception. GPS is an edge sharpness metric, used for the description of various kinds of gradient profiles. This method focuses on the enhancement of low-resolution images using the triangle model and further the k-mean clustering is applied for object identification. However, it fails with multiresolution, and unevenly illuminated images. Finally, an optimal local thresholding technique based on random fuzzy sets and entropy measures is presented in this book for segmenting multiresolution and unevenly illuminated images. The results are shown in the form of tables and graphs for three methods.

Seller Inventory # 9786203195743

Bibliographic Details

• Title: Optimal Threshold Selection Based on Random Fuzzy Sets : for Image Segmentation and Analysis
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 620319574X
• ISBN 13: 9786203195743
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 209 grams

About this title

Synopsis

The extended Canny edge detection technique is proposed to identify the edges efficiently using an automatic selection of optimal threshold values. The proposed work states that the detailed results reveal the superior performance over the traditional canny edge detection algorithm. However proposed algorithm fails for low contrast, multiresolution, and unevenly illuminated images. For the second objective, the gradient profile sharpness (GPS) algorithm is presented to emphasize the impact of illumination contrast on human visual perception. GPS is an edge sharpness metric, used for the description of various kinds of gradient profiles. This method focuses on the enhancement of low-resolution images using the triangle model and further the k-mean clustering is applied for object identification. However, it fails with multiresolution, and unevenly illuminated images. Finally, an optimal local thresholding technique based on random fuzzy sets and entropy measures is presented in this book for segmenting multiresolution and unevenly illuminated images. The results are shown in the form of tables and graphs for three methods.