New approach of estimating edge detection threshold and application of adaptive detector depending on image complexity

Optik ◽  
2021 ◽  
Vol 238 ◽  
pp. 166476
Author(s):  
Vladimir Maksimovic ◽  
Mile Petrovic ◽  
Dragan Savic ◽  
Branimir Jaksic ◽  
Petar Spalevic
Keyword(s):  
Edge Detection ◽  
Detection Threshold ◽  
Image Complexity ◽  
New Approach

scholarly journals Goose eggshell geometry

2014 ◽  
Vol 60 (No. 3) ◽  
pp. 100-106 ◽  
Author(s):  
Š. NEDOMOVÁ ◽  
J. BUCHAR
Keyword(s):  
Fourier Series ◽  
Edge Detection ◽  
Surface Area ◽  
Digital Image ◽  
Radius Of Curvature ◽  
Egg Volume ◽  
New Approach ◽  
Detection Techniques ◽  
The Common

The paper presents a new approach of the eggshell geometry determination using and analysing the egg digital image and edge detection techniques. The detected points on the eggshell contour were fitted by the Fourier series. The obtained equations describing an egg profile were used to calculate the egg volume, surface area, and radius of curvature with much higher degree of precision in comparison with previously published approaches. The paper shows and quantifies the limitations of the common and frequent procedures.

A New Approach for Edge Detection: Application to Gray Images

2008 ◽  
Vol 3 (4) ◽  
pp. 40-45
Author(s):  
G. Wiselin Jiji ◽  
◽  
L. Ganesan ◽  
Keyword(s):  
Edge Detection ◽  
New Approach

Edge Detection Combined Local Gray Level Difference and Competitive Fuzzy Logic

2012 ◽  
Vol 220-223 ◽  
pp. 1284-1287
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Experimental Result ◽  
Gray Level ◽  
Human Visual Perception ◽  
Edge Image ◽  
New Approach ◽  
Level Difference ◽  
Edge Detection Method ◽  
Noisy Condition

In this paper, we present a new approach by local gray level difference based competitive fuzzy edge detection. In the light of human visual perception, a preprocessing step is proposed to simplify original images and further enhance the performance of edge extraction. Then we define the feature vector of each pixel in four directions and six edge prototype. Finally, BP neural network is used to classify the type of edge, and the competitive rule is adopted to thin the thick edge image. From the experimental result, it can be seen that the edge detection method proposed in this paper is superior to Canny method and Log method under the noisy condition.

Edge detection using resistive threshold logic networks with CMOS flash memories

2014 ◽  
Vol 7 (1) ◽  
pp. 79-94 ◽  
Author(s):  
Alex Pappachen James ◽  
Anusha Pachentavida ◽  
Sherin Sugathan
Keyword(s):  
Edge Detection ◽  
Flash Memory ◽  
High Speed ◽  
Basic Feature ◽  
Spatial Change ◽  
Edge Detector ◽  
Resistance Change ◽  
New Approach ◽  
Content Type ◽  
Memory Network

Purpose – The purpose of this paper is to present a new approach to edge detection using semiconductor flash memory networks having scalable and parallel hardware architecture. Design/methodology/approach – A flash cell can store multiple states by controlling its voltage threshold. The equivalent resistance of the operation states controlled by threshold voltage of flash cell gives out different combinations of logic 0 and 1 states. The paper explores this basic feature of flash memory in designing a resistance change memory network for implementing novel edge detector hardware. This approach of detecting the edges is inspired from the spatial change detection ability of the human visual system. Findings – The proposed approach consumes less number of electronic components for its implementation, and outperforms the conventional approaches of edge detection with respect to the processing speed, scalability and ease of design. It is also demonstrated to provide edges invariant to changes in the direction of the spatial change in the images. Research limitations/implications – This research brings about a new direction in the development of edge detection, in terms of developing high-speed parallel processing edge detection and imaging circuits. Practical implications – The proposed approach reduces the implementation complexity by removing the need to have convolution operations for spatial edge filtering. Originality/value – This paper presents one of the first edge detection approaches that is purely a hardware oriented design, uses resistance of flash memory to form edge detector cells, and one that does not use computational operations such as additions or multiplications for its implementation.

New Approach to Image Edge Detection Based on Quantum Entropy

2016 ◽  
Vol 37 (2) ◽  
pp. 141-154 ◽  
Author(s):  
S. Abdel-Khalek ◽  
Gamil Abdel-Azim ◽  
Z. A. Abo-Eleneen ◽  
A.-S. F. Obada
Keyword(s):  
Edge Detection ◽  
Quantum Entropy ◽  
New Approach ◽  
Image Edge Detection ◽  
Image Edge
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