Adaptive edge orientation analysis

Eric van Reeth (1, 2) , Pascal Bertolino (3) , Marina Nicolas (2) , Jean-Marc Chassery (3)

1 GIPSA-lab - Grenoble Images Parole Signal Automatique
2 ST-CROLLES - STMicroelectronics [Crolles]
3 GIPSA-AGPIG - GIPSA - Architecture, Géométrie, Perception, Images, Gestes

This paper presents a method that detects edge orientations in still images. Edge orientation is a crucial information when one wants to optimize the quality of edges after different processings. The detection is carried out in the wavelet domain to take advantage of the multi-resolution features of the wavelet spaces, and locally adapts the resolution to the characteristics of edges. Our orientation detection method consists of finding the local direction along which the wavelet coefficients are the most regular. To do so, the image is divided in square blocks of varying size, in which Bresenham lines are drawn to represent different directions. The direction of the Bresenham line that contains the most regular wavelet coefficients, according to a criterion defined in the paper, is considered to be the direction of the edge inside the block. The choice of the Bresenham line drawing algorithm is justified in this paper, and we show that it considerably increases the angle precision compared to other methods as for instance, the method used for the construction of bandlet bases. An optimal segmentation is then computed in order to adapt the size of the blocks to the edge localization and to isolate in each block at most one contour orientation. Examples and applications on image interpolation are shown on real images.

Mots clés: Bandlets; Wavelets; Multi-resolution; Image Analysis; Edge Direction; Curvelets

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Eric van Reeth, Pascal Bertolino, Marina Nicolas, Jean-Marc Chassery. Adaptive edge orientation analysis. IS&T/SPIE Electronic Imaging, Jan 2010, San Jose, United States. pp.CD. ⟨hal-00448948⟩