1. Computational Vision: Object Recognition Object Recognition Jeremy Wyatt

2. Computational Vision: Object Recognition Plan  David Marr: the model based approach to vision  Model based approaches: Geons, Model Fitting  Appearance based approaches: PCA, SIFT, implicit shape model  Psychological Evidence: View dependent vs. view independent recognition  Summary: who is right?

3. Computational Vision: Object Recognition Model based vision  David Marr was a brilliant young British vision researcher who defined a coherent approach to the study of vision during the 1970s  According to one tradition coming out of Marr’s work: Vision is process of reconstructing the 3d scene from 2d information The vision system has representations of 3d geometric structures Visual pipeline So selecting models and recovering their parameters from image data is a key task in vision Intensity image Primal sketch Model selection 2.5d sketch

4. Computational Vision: Object Recognition Model based vision  There is an infinite variety of objects. How do we represent, store and access models of them efficiently?  One suggestion was the use of a small library of 3d parts from which many complex models can be constructed  There are many schemes: generalised cylinders, Geons, Superquadrics  Vision researchers set about applying them

5. Computational Vision: Object Recognition Models vs Appearances  But they didn’t work very well …  By the early 1990s people were experimenting with statistical techniques, e.g. PCA  These learn a statistical summary of the appearance of each view of an object AppearanceModel

6. Computational Vision: Object Recognition Appearance based recognition: SIFT  These statistical approaches characterise some aspects of the appearance of an object that can be used to recognise it  But this means they are (largely) view dependent, you have to learn a different statistical model for each different view  e.g. SIFT based recognition (David Lowe, UBC) Find interest points in the scale space Re-describe the interest points so that they are robust to:  Image translation, scaling, rotation  Partially invariant to illumination changes, affine and 3d projection changes

7. Computational Vision: Object Recognition Category level recognition (Thanks to Bastian Liebe)

8. Computational Vision: Object Recognition Category level recognition (Thanks to Bastian Liebe)

9. Computational Vision: Object Recognition Category level recognition (Thanks to Bastian Liebe)

10. Computational Vision: Object Recognition Constellation model (Thanks to Bastian Liebe)

11. Computational Vision: Object Recognition Constellation Model (Thanks to Bastian Liebe)

12. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

13. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

14. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

15. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

16. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

17. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

18. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

19. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

20. Computational Vision: Object Recognition Implicit Shape Model (Thanks to Bastian Liebe)

21. Computational Vision: Object Recognition Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts Aleš Leonardis and Sanja Fidler University of Ljubljana Faculty of Computer and Information Science Visual Cognitive Systems Laboratory Reproduced with permission

22. Computational Vision: Object Recognition Framework  Main properties of the framework: Computational plausibilityComputational plausibility  Hierarchical representation  Compositionality (parts composed of parts)  Indexing & matching recognition scheme Statistics driven learning (unsupervised learning)Statistics driven learning (unsupervised learning) Fast, incremental (continuous) learningFast, incremental (continuous) learning

23. Computational Vision: Object Recognition Recognition: Indexing and matching image car motorcycledogperson hypotheses verification Gradually limiting the search LEARN

24. Computational Vision: Object Recognition Overview of the architecture  Starts with simple, local features and learns more and more complex compositions  Learns layer after layer to exploit the regularities in natural images as efficiently and compactly as possible  Builds computationally feasible layers of parts by selecting only the most statistically significant compositions of specific granularity  Learns lower layers in a category independent way (to obtain optimally sharable parts) and category specific higher layers which contain only a small number of highly generalizable parts for each category  New categories can efficiently and continuously be added to the representation without the need to restructure the complete hierarchy  Implements parts in a robust, layered interplay of indexing & matching

25. Computational Vision: Object Recognition Part based appearance recognition (Fidler & Leonardis 07)

26. Computational Vision: Object Recognition  Learned hierarchy for faces and cars (first three layers are the same; links show compositionality for each of the categories; spatial variability of parts is not shown) Results

27. Computational Vision: Object Recognition Part based appearance recognition (Fidler & Leonardis 07)

28. Computational Vision: Object Recognition Results - Detections

29. Computational Vision: Object Recognition Results - Specific categories, faces  Detection of Layer5 parts

30. Computational Vision: Object Recognition Results - Specific categories, faces

31. Computational Vision: Object Recognition Evidence from biology  Is human object recognition view dependent?  Shepherd & Miller  Pinker & Tarr  There is a quite a large body of experimental data that supports the view dependent camp.  Appearance based approaches fit neatly with this camp.

32. Computational Vision: Object Recognition Summary  This is not a resolved debate  There is evidence for both sides  Structural 3d information is almost certainly extracted by the brain too  Model based: how do we extract good enough low level features (e.g. a depth map)?  Appearance based: only seems to be good for recognition, which is a small part of the vision problem.