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1. Introduction and Overview. Part I: Basic Scale-Space Theory. 2. Linear Scale-Space and Related Multi-Scale Representations. 3. Scale-Space for 1-D Discrete Signals. 4. Scale-Space for N-D Discrete Signals. 5. Discrete Derivative Approximations with Scale-Space Properties. 6. Feature Detection in Scale-Space. Part II: The Scale-Space Primal Sketch: Theory. 7. The Scale-Space Primal Sketch. 8. Behaviour of Image Structures in Scale-Space: Deep Structure. 9. Algorithm for Computing the Scale-Space Primal Sketch. Part III: The Scale-Space Primal Sketch: Applications. 10. Detection Salient Blob-like Image Structures and their Scales. 11. Guiding Early Visual Processing with Qualitative Scale and Region. 12. Summary and Discussion. Part IV: Scale Selection and Shape Computation in a Visual Front-End. 13. Scale Selection for Differential Operators. 14. Direct Computation of Shape Cues by Scale-Space Operations. 15. Non-Uniform Smoothing. Appendix A: Technical Details. Bibliography. Index.
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Add Scale-Space Theory in Computer Vision, We perceive objects in the world as having structures at both coarse and fine scales. A tree, for instance, may appear as having a roughly round or cylindrical shape when seen from a distance, even though it is built up from a large number of branches. At, Scale-Space Theory in Computer Vision to the inventory that you are selling on WonderClubX
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Add Scale-Space Theory in Computer Vision, We perceive objects in the world as having structures at both coarse and fine scales. A tree, for instance, may appear as having a roughly round or cylindrical shape when seen from a distance, even though it is built up from a large number of branches. At, Scale-Space Theory in Computer Vision to your collection on WonderClub |