Integrating quality in fuzzy reasoning edge detection

2002 ◽  
Author(s):  
V. Bombardier ◽  
O. Perez-Oramas ◽  
J. Bremont
Keyword(s):  
Edge Detection ◽  
Fuzzy Reasoning

A HYBRID EDGE DETECTOR USING FUZZY LOGIC AND MATHEMATICAL MORPHOLOGY

2010 ◽  
Vol 10 (02) ◽  
pp. 251-272 ◽  
Author(s):  
AMANDEEP KAUR ◽  
CHANDAN SINGH
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Fuzzy Reasoning ◽  
Image Understanding ◽  
Edge Detector ◽  
Image Detection ◽  
New Approach ◽  
Canny Edge ◽  
Computation Load ◽  
Grey Level Image

Edge detection concerns the localization of significant variations of the grey level image. Detection of edges in an image is very important step for a complete image understanding system. This paper proposes a new approach to edge detection which adopts fuzzy reasoning to detect edges and mathematical morphology for edge thinning. The results achieved by this algorithm are comparable to the Canny approach. In Canny edge detector we may require many runs using different combinations of the three parameters (two threshold and one sigma values) whereas in the proposed technique only one parameter needs to be set by the user coarsely to get the same results, also the computation load of Canny is higher than the proposed approach.

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).

Sign in / Sign up

Export Citation Format

Share Document