A Linear Image Reconstruction Framework Based on Sobolev Type Inner Products
International Journal of Computer Vision
Hypotheses for Image Features, Icons and Textons
International Journal of Computer Vision
Image Compression with Anisotropic Diffusion
Journal of Mathematical Imaging and Vision
Image and Vision Computing
Linear Image Reconstruction by Sobolev Norms on the Bounded Domain
International Journal of Computer Vision
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Gaussian scale space from insufficient image information
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Linear image reconstruction by Sobolev norms on the bounded domain
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Combining different types of scale space interest points using canonical sets
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Maximum likelihood metameres for local 2nd order image structure of natural images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Motion compensated video super resolution
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Coding Images with Local Features
International Journal of Computer Vision
Towards a new paradigm for motion extraction
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Optic flow from multi-scale dynamic anchor point attributes
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
A linear image reconstruction framework based on sobolev type inner products
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
On image reconstruction from multiscale top points
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
On α kernels, Lévy processes, and natural image statistics
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Optimising spatial and tonal data for homogeneous diffusion inpainting
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Jet-Based local image descriptors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Uniqueness Results for Image Reconstruction from Features on Curves in α-Scale Spaces
Journal of Mathematical Imaging and Vision
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According to Marr's paradigm of computational vision the first process is an extraction of relevant features. The goal of this paper is to quantify and characterize the information carried by features using image-structure measured at feature-points to reconstruct images. In this way, we indirectly evaluate the concept of feature-based image analysis. The main conclusions are that (i) a reasonably low number of features characterize the image to such a high degree, that visually appealing reconstructions are possible, (ii) different feature-types complement each other and all carry important information. The strategy is to define metamery classes of images and examine the information content of a canonical least informative representative of this class. Algorithms for identifying these are given. Finally, feature detectors localizing the most informative points relative to different complexity measures derived from models of natural image statistics, are given.