Fractional Derivatives for Edge Detection: Application to Road Obstacles

2019 ◽  
pp. 115-137 ◽  
Author(s):  
Roy Abi Zeid Daou ◽  
Fabio El Samarani ◽  
Charles Yaacoub ◽  
Xavier Moreau
Keyword(s):  
Edge Detection ◽  
Fractional Derivatives

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

Edge detection using fractional derivatives and information sets

2018 ◽  
Vol 27 (05) ◽  
pp. 1 ◽  
Author(s):  
Vignesh Kothapalli ◽  
Shaveta Arora ◽  
Madasu Hanmandlu
Keyword(s):  
Edge Detection ◽  
Fractional Derivatives ◽  
Information Sets

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).

On Fractional Derivatives and Application

2013 ◽  
Vol 1 (2) ◽  
pp. 51-56
Author(s):  
Mark Borres ◽  
◽  
Efren Barabat ◽  
Jocelyn Panduyos ◽  
◽  
...  
Keyword(s):  
Fractional Derivatives

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
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