The edges detection in images using the clustering algorithm

Htet, Zaw Win; Koldaev, Victor D.; Dorogov, Viktor G.; Aung, Naing Linn

doi:10.1109/eiconrus.2017.7910628

Cited by 8 publications

(2 citation statements)

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“…A lot of method already exit steganography, but the edge detection algorithm gives the best results. So the research introduced LSBMR approach[8], [15].…”

Section: Related Workmentioning

confidence: 99%

Algorithmic Approaches For Image Edge Detection

2021

IJATCSE

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Edge detection is most important technique in digital image processing. It play an important role in image segmentation and many other applications. Edge detection providesfoundation to many medical and military applications.It difficult to generate a generic code for edge detection so many kinds ofalgorithms are available. In this article 4 different approaches Global image enhancement with addition (GIEA), Global image enhancement with Multiplication (GIEM),Without Global image enhancement with Addition (WOGIEA),and without Global image enhancement with Multiplication (WOGIEM)for edge detection is proposed. These algorithms are validatedon 9 different images. The results showthat GIEA give us more accurate results as compare to other techniques.

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“…A lot of method already exit steganography, but the edge detection algorithm gives the best results. So the research introduced LSBMR approach[8], [15].…”

Section: Related Workmentioning

confidence: 99%

Algorithmic Approaches For Image Edge Detection

2021

IJATCSE

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“…There are also a lot of improvements for these operators. For example, Htet et al 13 proposed an edge extraction method based on clustering. Solanki and Godfrey 14 proposed to use gradient uncertainty to obtain easily overlooked edge information.…”

Section: Related Workmentioning

confidence: 99%

A fast template matching algorithm based on principal orientation difference

Jiao

Wang

Deng

et al. 2018

International Journal of Advanced Robotic Systems

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In the case that the background scene is dense map regularization complex and the detected objects are low texture, the method of matching according to the feature points is not applicable. Usually, the template matching method is used. When training samples are insufficient, the template matching method gets a worse detection result. In order to resolve the problem stably in real time, we propose a fast template matching algorithm based on the principal orientation difference feature. The algorithm firstly obtains the edge direction information by comparing the images that are binary. Then, the template area is divided where the different features are extracted. Finally, the matching positions are searched around the template. Experiments on the videos whose speed is 30 frames/s show that our algorithm detects the lowtexture objects in real time with a matching rate of 95%. Compared with other state-of-art methods, our proposed method reduces the training samples significantly and is more robust to the illumination changes.

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