New approach for quantitative coronary analysis (QCA) tool using multiresolution edge detection technique

2003 ◽  
Author(s):  
Amjed S. Al-Fahoum ◽  
Jalal Zanoun
Keyword(s):  
Edge Detection ◽  
Detection Technique ◽  
New Approach ◽  
Multiresolution Edge Detection

An edge detection technique based on geometrical features

2011 ◽  
Author(s):  
Hongjie Wu ◽  
Baochuang Fu ◽  
Yin Zhu ◽  
Jianlan Ji ◽  
Fuyuan Hu
Keyword(s):  
Edge Detection ◽  
Detection Technique ◽  
Geometrical Features

scholarly journals A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model

2013 ◽  
Vol 84 (10) ◽  
pp. 27-33 ◽  
Author(s):  
Deepak RanjanNayak ◽  
Sumit Kumar Sahu ◽  
Jahangir Mohammed
Keyword(s):  
Cellular Automata ◽  
Edge Detection ◽  
Detection Technique

scholarly journals Novel Adaptive Neuro-Fuzzy based Edge Detection Technique

2012 ◽  
Vol 49 (4) ◽  
pp. 23-27
Author(s):  
Suryakant Suryakant ◽  
Renu Dhir
Keyword(s):  
Edge Detection ◽  
Detection Technique ◽  
Neuro Fuzzy

An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images

2020 ◽  
pp. 252-272
Author(s):  
Kalyan Kumar Jena ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Images ◽  
Color Images ◽  
Experimental Results ◽  
Detection Technique ◽  
Algorithmic Approach ◽  
Current Scenario ◽  
Edge Detection Method

Research in the field of digital image processing (DIP) has increased in the current scenario. Edge detection of digital images is considered as an important area of research in DIP. Detecting edges in different digital images accurately is a challenging work in DIP. Different methods have been introduced by different researchers to detect the edges of images. However, no method works well under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color images. This method focuses on the combination of Canny, mathematical morphological, and Sobel (CMS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny, and mathematical morphological edge detection operators. The experimental results show that the proposed method works better as compared to other existing methods in detecting the edges of images.

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