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A Multi-Level Threshold Method for Edge Detection and Segmentation Based on Entropy 被引量:1
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作者 mohamed A.El-Sayed Abdelmgeid A.Ali +1 位作者 mohamed e.hussien Hameda A.Sennary 《Computers, Materials & Continua》 SCIE EI 2020年第4期1-16,共16页
The essential tool in image processing,computer vision and machine vision is edge detection,especially in the fields of feature extraction and feature detection.Entropy is a basic area in information theory.The entrop... The essential tool in image processing,computer vision and machine vision is edge detection,especially in the fields of feature extraction and feature detection.Entropy is a basic area in information theory.The entropy,in image processing field has a role associated with image settings.As an initial step in image processing,the entropy is always used the image’s segmentation to determine the regions of image which is used to separate the background and objects in image.Image segmentation known as the process which divides the image into multiple regions or sets of pixels.Many applications have been development to enhance the image processing.This paper utilizes the Shannon entropy to achieve edge detection process and segmentation of the image.It introduces a new method of edge detection for 2-D histogram and Shannon entropy based on multilevel threshold.The method utilizes the gray value and the average gray value of the pixels to achieve the two dimensional histogram.The current method has apriority in comparison to some upper classical methods.The experimental results exhibited that the proposed method could capture a moderate quality and execution time better than other comparative methods,particularly in the largest images size.The proposed method offers good results when applied with images of different sizes from the civilization of ancient Egyptians. 展开更多
关键词 Multi-level threshold edge detection 2D histogram ENTROPY
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