JOURNAL ARTICLE

Image Thresholding based on Edge Detection

Soonhak KwonKrishnamoorthy Sivakumar

Year: 2013 Journal:   Journal of Korean institute of intelligent systems Vol: 23 (2)Pages: 139-143   Publisher: Korean Institute of Intelligent Systems

Abstract

기존의 영상 이진화에 대한 알고리즘의 기본 아이디어는 영상이 회색 수준의 차이에 의하여 물체와 배경으로 구분된다는 가정에서 이루어지고 있다. 본 논문에서는 이러한 가정을 확장하여 영상은 물체와 배경뿐만 아니라 하나를 더 추가하여 그 테두리로 이루어진다고 가정하고 테두리 검출에 기반한 이진화 기법을 제안한다. 제안한 방법의 타당성을 보이기 위하여 6개의 잘 알려진 영상에 대하여 모의실험을 수행하고, 그 결과로부터 기존의 방법들과의 성능을 비교 검토한다. The basic idea of conventional thresholding is that an image consists of objects and their background where the gray levels of the objects are different from those of the background. In this paper, we extend it to one where an image consists of not only objects and the background but also their edges. Based on this extension, we propose an edge detection-based thresholding method. The effectiveness of the proposed method is demonstrated by experimental results tested on six well-known test images and compared with conventional methods.

Keywords:
Thresholding Artificial intelligence Edge detection Computer vision Balanced histogram thresholding Image (mathematics) Enhanced Data Rates for GSM Evolution Computer science Pattern recognition (psychology) Image edge Extension (predicate logic) Mathematics Image processing

Metrics

6
Cited By
1.30
FWCI (Field Weighted Citation Impact)
7
Refs
0.85
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Industrial Vision Systems and Defect Detection
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering

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