Authors:
Mircea Popa
;
Andras Majdik
and
Gheorghe Lazea
Affiliation:
Technical University, Romania
Keyword(s):
Adaptive edge detection, Haar-like features, Image analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early Vision and Image Representation
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Image Shape Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Structural and Syntactic Approach
Abstract:
This paper presents an adaptive method of the edge detection problem, based on the algorithm of Canny. It is designed to be used in the real-scene object recognition problems in those cases where, because of the complexity of the environment’s structure and the time-varying illumination, regular edge detection algorithms fail to offer a good and stabile response. The algorithm is based on the same principle as Canny’s method, but the hysteresis threshold values are adapted for each pixel considering the local approximation of the gradient value. The gradients are approximated by Haar-like features, computed with integral images in constant time. In terms of edge extraction, the proposed algorithm improves the performance obtained with the method of Canny in complex lightening conditions. It also provides to the user more control over the detection process and assures a more stable result concerning the illumination conditions. The results of the proposed algorithm are compared with t
hose obtained with the classic method of Canny for edge detection in real scenarios. Both implementations use the speed-optimized functions of Open Computer Vision (OpenCV) Library.
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