Authors:
Safa Mefteh
1
;
Mohamed-Bécha Kaâniche
1
;
Riadh Ksantini
2
and
Adel Bouhoula
1
Affiliations:
1
Digital Security Research Lab, Higher School of Communication of Tunis (Sup’Com), University of Carthage and Tunisia
;
2
Digital Security Research Lab, Higher School of Communication of Tunis (Sup’Com), University of Carthage, Tunisia, University of Windsor, 401, Sunset Avenue, Windsor, ON and Canada
Keyword(s):
Multispectral Edge Detection, Lab-D Image, Gradient based Approach, Occlusion Handling.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
This paper presents a new method for edge detection based on both Lab color and depth images. The principal challenge of multispectral edge detection consists of integrating different information into one meaningful result, without requiring empirical parameters. Our method combines the Lab color channels and depth information in a well-posed way using the Jacobian matrix. Unlike classical multi-spectral edge detection methods using depth information, our method does not use empirical parameters. Thus, it is quite straightforward and efficient. Experiments have been carried out on Middlebury stereo dataset (Scharstein and Szeliski, 2003; Scharstein and Pal, 2007; Hirschmuller and Scharstein, 2007) and several selected challenging images (Rosenman, 2016; lightfieldgroup, 2016). Experimental results show that the proposed method outperforms recent relevant state-of-the-art methods.