Sketch based coding of grey level images
Signal Processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour coding for image description
Pattern Recognition Letters
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Images to Surfaces: A Computational Study of the Human Early Visual System
From Images to Surfaces: A Computational Study of the Human Early Visual System
Journal of Computational Physics
A Nonlinear Derivative Scheme Applied to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the range of surface reconstructions from a gradient field?
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Reconstructing images from their most singular fractal manifold
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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In this paper, we show that a new edge detection scheme developed from the notion of transition in nonlinear physics, associated with the precise computation of its quantitative parameters (most notably singularity exponents) provide enhanced performances in terms of reconstruction of the whole image from its edge representation; moreover it is naturally robust to noise. The study of biological vision in mammals state the fact that major information in an image is encoded in its edges, the idea further supported by neurophysics. The first conclusion that can be drawn from this stated fact is that of being able to reconstruct accurately an image from the compact representation of its edge pixels. The paper focuses on how the idea of edge completion can be assessed quantitatively from the framework of reconstructible systems when evaluated in a microcanonical formulation; and how it redefines the adequation of edge as candidates for compact representation. In the process of doing so, we also propose an algorithm for image reconstruction from its edge feature and show that this new algorithm outperforms the well-known 'state-of-the-art' techniques, in terms of compact representation, in majority of the cases.