Multi-scale Mathematical Morphology Based Image Edge Detection

2012 ◽  
Author(s):  
Tao Wang ◽  
Na Wei
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Multi Scale ◽  
Image Edge Detection ◽  
Image Edge

A New Kind of Image Edge Detection Based on The Theory of The Adaptive Lifting Wavelet and Morphology

2018 ◽  
Vol 11 (3) ◽  
pp. 90-104
Author(s):  
Honge Ren ◽  
Xiyan Xu ◽  
Meng Zhu ◽  
Dongxu Huo
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Multi Scale ◽  
Cell Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Lifting Wavelet

This article describes how in traditional edge detection it is prone to defects such as fuzzy positioning, and noise influence. This article proposes a type of edge detection algorithm which combines lifting wavelet transform and adaptive mathematical morphology, which makes a lifting wavelet to analyze the wood cell image. Then, the high-frequency part is detected by using the algorithm fusing the wavelet packet and the rapid-combining multi-scale wavelet, which controls noise effectively; while for the low frequency part is detected with modified adaptive mathematical morphology, to locate the exact details. The final result will processes the edge of the image using “algebra” algorithm fusion. The example for a wood cell image which illustrates the algorithm is to detect the cell boundary relatively clearly, and effectively suppress the noise.

Image Edge Detection Based on Multi-Structure Elements and Nonsubsambled Contourlet Transform

2012 ◽  
Vol 591-593 ◽  
pp. 1822-1826
Author(s):  
Kun Xian He ◽  
Qing Wang ◽  
Fan He
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Fusion Algorithm ◽  
Edge Image ◽  
Multi Scale ◽  
Image Edge Detection ◽  
Image Edge ◽  
Modulus Maxima ◽  
Single Pixel ◽  
Suppress Noise

This paper presents a fusion algorithm for image edge detection based on the mathematical morphology and the NSCT. First the de-noised image is processed by the multi-structure elements of the mathematical morphology. And then the processed image is decomposed by the NSCT into multi-scale and multi-directional sub-bands. Edges in the high-frequency sub-bands are extracted with the dual-threshold modulus maxima method. Finally the edges of the de-noised image are refined into a single pixel edge image. The simulation results show that this method can effectively suppress noise, eliminate pseudo-edges, locate accurately and detect the complete outline.

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