Computer Vision, Graphics, and Image Processing
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
An Operator Which Locates Edges in Digitized Pictures
Journal of the ACM (JACM)
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Image Processing
Hypotheses for Image Features, Icons and Textons
International Journal of Computer Vision
Median and related local filters for tensor-valued images
Signal Processing
Robust fusion of irregularly sampled data using adaptive normalized convolution
EURASIP Journal on Applied Signal Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
Matrix-valued filters as convex programs
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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Linear scale space methodology uses Gaussian probes at scale s to observe the differential structure. In observing the differential image structure through the Gaussian derivative probes at scale s we implicitly construct the Taylor series expansion of the smoothed image. The Gaussian facet model, as a generalization of the classic Haralick facet model, constructs a polynomial approximation of the unsmoothed image. The measured differential structure therefore is closer to the 'real' structure then the differential structure measured using Gaussian derivatives. At the points in an image where the differential structure changes abruptly (because of discontinuities in the imaging conditions, e.g. a material change, or a depth discontinuity) both the Gaussian derivatives and the Gaussian facet model diffuse the information from both sides of the discontinuity (smoothing across the edge). Robust estimators that are classically meant to deal with statistical outliers can also be used to deal with these 'mixed model distributions'. In this paper we introduce the robust estimators of local image structure. Starting with the Gaussian facet model model where we replace the quadratic error norm with a robust (Gaussian) error norm leads to a robust Gaussian facet model. We will show examples of using the robust differential structure estimators for luminance and color images, for zero and higher order differential structure. Furthermore we look at a 'robustified' structure tensor that forms the basis of robust orientation estimation.