Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
International Journal of Computer Vision
What Do Features Tell about Images?
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Image reconstruction from multiscale critical points
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in 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
Image Compression with Anisotropic Diffusion
Journal of Mathematical Imaging and Vision
Exploring and exploiting the structure of saddle points in Gaussian scale space
Computer Vision and Image Understanding
Transitions of a Multi-scale Image Hierarchy Tree
SSVM '09 Proceedings of the Second 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
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and 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
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Image reconstruction from a fiducial collection of scale space interest points and attributes (e.g. in terms of image derivatives) can be used to make the amount of information contained in them explicit. Previous work by various authors includes both linear and non-linear image reconstruction schemes. In this paper, the authors present new results on image reconstruction using a top point representation of an image. A hierarchical ordering of top points based on a stability measure is presented, comparable to feature strength presented in various other works. By taking this into account our results show improved reconstructions from top points compared to previous work. The proposed top point representation is compared with previously proposed representations based on alternative feature sets, such as blobs using two reconstruction schemes (one linear, one non-linear). The stability of the reconstruction from the proposed top point representation under noise is also considered.