scholarly journals Scaling Results for the Variational Approach to Edge Detection

1991 ◽  
Author(s):  
Thomas J. Richardson ◽  
Sanjoy K. Mitter
Keyword(s):  
Edge Detection ◽  
Variational Approach

scholarly journals A Variational Approach to Joint Denoising, Edge Detection and Motion Estimation

2006 ◽  
pp. 525-535 ◽  
Author(s):  
Alexandru Telea ◽  
Tobias Preusser ◽  
Christoph Garbe ◽  
Marc Droske ◽  
Martin Rumpf
Keyword(s):  
Motion Estimation ◽  
Edge Detection ◽  
Variational Approach

scholarly journals A variational approach to edge detection

2016 ◽  
Vol 10 (2) ◽  
pp. 499-517 ◽  
Author(s):  
Monika Muszkieta
Keyword(s):  
Edge Detection ◽  
Variational Approach

scholarly journals Variational Method Integrating Edge Detection and Smoothing in Digital Images

2019 ◽  
Author(s):  
Italo M. F. Santos ◽  
Abimael D. Loula ◽  
Gilson A. Giraldi ◽  
Gastão F. Miranda Junior ◽  
Paulo S. S. Rodrigues
Keyword(s):  
Edge Detection ◽  
Variational Approach ◽  
Numerical Scheme ◽  
Iterative Scheme ◽  
Edge Extraction ◽  
Original Image ◽  
Image Smoothing ◽  
Linear Scheme ◽  
Low Pass ◽  
High Pass

There is a consensus in computer vision about the importance of the scale concept for edge extraction and for image smoothing or representation. In this paper we explore a variational approach that allows to put together edge detection and image smoothing in a unified linear scheme. Basically, the functional proposed by Mumford and Shah is re-written as an energy defined with two arguments: the first one representing smooth versions of the original image and the second one encompassing its edge set. We follow known results in the variational analysis to obtain a numerical scheme to minimize the energy. We apply Fourier analysis to verify that the iterative scheme converges to a low-pass representation of the original image in the first argument and a high-pass signal in the other one. In the experimental results we show that the obtained scheme encourages intraregion image smoothing in preference to interregion blurring with edge localization at a desired scale.

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
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