Common Vector Approach Based Image Gradients Computation for Edge Detection

2020 ◽  
pp. 408-421
Author(s):  
Sahin Isik ◽  
Kemal Ozkan
Keyword(s):  
Edge Detection ◽  
Image Gradients ◽  
Vector Approach

scholarly journals A scale-vector approach for edge detection

1995 ◽  
Vol 16 (6) ◽  
pp. 637-646 ◽  
Author(s):  
J.A. García ◽  
J. Fdez-Valdivia ◽  
A. Garrido
Keyword(s):  
Edge Detection ◽  
Vector Approach

scholarly journals General Type-2 Fuzzy Sugeno Integral for Edge Detection

2019 ◽  
Vol 5 (8) ◽  
pp. 71
Author(s):  
Gabriela E. Martínez ◽  
Claudia I. Gonzalez ◽  
Olivia Mendoza ◽  
Patricia Melin
Keyword(s):  
Edge Detection ◽  
Figure Of Merit ◽  
General Type ◽  
Detection Method ◽  
General Process ◽  
Sugeno Integral ◽  
Morphological Gradient ◽  
Edge Detection Method ◽  
Image Gradients

A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients in the four directions—horizontal, vertical, and the two diagonals—and this technique is known as the morphological gradient. After that, the general type-2 fuzzy Sugeno integral (GT2 FSI) is used to integrate the four image gradients. In this second step, the GT2 FSI establishes criteria to determine at which level the obtained image gradient belongs to an edge during the process; this is calculated assigning different general type-2 fuzzy densities, and these fuzzy gradients are aggregated using the meet and join operators. The gradient integration using the GT2 FSI provides a methodology for achieving more robust edge detection, even more if we are working with blurry images. The experimental evaluations are performed on synthetic and real images, and the accuracy is quantified using Pratt’s Figure of Merit. The results values demonstrate that the proposed edge detection method outperforms other existing algorithms.

Difference mode EXELFS with parallel-detection EELS

1992 ◽  
Vol 50 (2) ◽  
pp. 1262-1263
Author(s):  
Michael K. Kundmann ◽  
Ondrej L. Krivanek
Keyword(s):  
Edge Detection ◽  
Important Distinction ◽  
Edge Structure ◽  
Parallel Detection ◽  
Gain Variation ◽  
Ideal Solutions ◽  
Detector Cell ◽  
The Difference ◽  
Elemental Detection ◽  
Partial Correction

Parallel detection has greatly improved the elemental detection sensitivities attainable with EELS. An important element of this advance has been the development of differencing techniques which circumvent limitations imposed by the channel-to-channel gain variation of parallel detectors. The gain variation problem is particularly severe for detection of the subtle post-threshold structure comprising the EXELFS signal. Although correction techniques such as gain averaging or normalization can yield useful EXELFS signals, these are not ideal solutions. The former is a partial throwback to serial detection and the latter can only achieve partial correction because of detector cell inhomogeneities. We consider here the feasibility of using the difference method to efficiently and accurately measure the EXELFS signal.An important distinction between the edge-detection and EXELFS cases lies in the energy-space periodicities which comprise the two signals. Edge detection involves the near-edge structure and its well-defined, shortperiod (5-10 eV) oscillations. On the other hand, EXELFS has continuously changing long-period oscillations (∼10-100 eV).

A Dividing Ratio Changeable Digital PLL Based on Phase State Memory and Double Clock-Edge Detection

2008 ◽  
Vol 128 (7) ◽  
pp. 1185-1190 ◽  
Author(s):  
Kuniaki Fujimoto ◽  
Hirofumi Sasaki ◽  
Mitsutoshi Yahara
Keyword(s):  
Edge Detection ◽  
Phase State

Leading Edge Detection of RF-Signal for Power Arcing Source Location

2014 ◽  
Vol 9 (5) ◽  
pp. 1060
Author(s):  
Frank Zoko Ble ◽  
Matti Lehtonen ◽  
Ari Sihvola ◽  
Charles Kim
Keyword(s):  
Edge Detection ◽  
Leading Edge ◽  
Source Location ◽  
Rf Signal
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