1. Lecture 08 27/12/2011 Shai Avidan הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.

2. Today Hough Transform Generalized Hough Transform Implicit Shape Model Video Google

3. Hough Transform & Generalized Hough Transform

4. K. Grauman, B. Leibe Hough Transform Origin: Detection of straight lines in clutter – Basic idea: each candidate point votes for all lines that it is consistent with. – Votes are accumulated in quantized array – Local maxima correspond to candidate lines Representation of a line – Usual form y = a x + b has a singularity around 90º. – Better parameterization: x cos(  ) + y sin(  ) =  θ ρ x y

5. K. Grauman, B. Leibe Examples – Hough transform for a square (left) and a circle (right)

6. K. Grauman, B. Leibe Hough Transform: Noisy Line Problem: Finding the true maximum TokensVotes θ ρ

7. K. Grauman, B. Leibe Hough Transform: Noisy Input Problem: Lots of spurious maxima TokensVotes θ ρ

8. K. Grauman, B. Leibe Generalized Hough Transform [Ballard81] Generalization for an arbitrary contour or shape – Choose reference point for the contour (e.g. center) – For each point on the contour remember where it is located w.r.t. to the reference point – Remember radius r and angle  relative to the contour tangent – Recognition: whenever you find a contour point, calculate the tangent angle and ‘vote’ for all possible reference points – Instead of reference point, can also vote for transformation  The same idea can be used with local features! Slide credit: Bernt Schiele

9. Implicit Shape Model

10. K. Grauman, B. Leibe Gen. Hough Transform with Local Features For every feature, store possible “occurrences” For new image, let the matched features vote for possible object positions

11. K. Grauman, B. Leibe When is the Hough transform useful? Textbooks wrongly imply that it is useful mostly for finding lines – In fact, it can be very effective for recognizing arbitrary shapes or objects The key to efficiency is to have each feature (token) determine as many parameters as possible – For example, lines can be detected much more efficiently from small edge elements (or points with local gradients) than from just points – For object recognition, each token should predict location, scale, and orientation (4D array) Bottom line: The Hough transform can extract feature groupings from clutter in linear time! Slide credit: David Lowe

12. K. Grauman, B. Leibe 3D Object Recognition Gen. HT for Recognition – Typically only 3 feature matches needed for recognition – Extra matches provide robustness – Affine model can be used for planar objects Slide credit: David Lowe [Lowe99]

13. K. Grauman, B. Leibe View Interpolation Training – Training views from similar viewpoints are clustered based on feature matches. – Matching features between adjacent views are linked. Recognition – Feature matches may be spread over several training viewpoints.  Use the known links to “transfer votes” to other viewpoints. [Lowe01]

14. K. Grauman, B. Leibe Recognition Using View Interpolation

15. K. Grauman, B. Leibe Location Recognition Training

16. 16K. Grauman, B. Leibe Applications Sony Aibo (Evolution Robotics) SIFT usage – Recognize docking station – Communicate with visual cards Other uses – Place recognition – Loop closure in SLAM Slide credit: David Lowe

17. Video Google

18. Indexing local features Each patch / region has a descriptor, which is a point in some high-dimensional feature space (e.g., SIFT) K. Grauman, B. Leibe

19. Indexing local features When we see close points in feature space, we have similar descriptors, which indicates similar local content. Figure credit: A. Zisserman K. Grauman, B. Leibe

20. Indexing local features We saw in the previous section how to use voting and pose clustering to identify objects using local features K. Grauman, B. Leibe Figure credit: David Lowe

21. Indexing local features With potentially thousands of features per image, and hundreds to millions of images to search, how to efficiently find those that are relevant to a new image? – Low-dimensional descriptors : can use standard efficient data structures for nearest neighbor search – High-dimensional descriptors: approximate nearest neighbor search methods more practical – Inverted file indexing schemes K. Grauman, B. Leibe

22. For text documents, an efficient way to find all pages on which a word occurs is to use an index… We want to find all images in which a feature occurs. To use this idea, we’ll need to map our features to “visual words”. K. Grauman, B. Leibe Indexing local features: inverted file index

23. Visual words K. Grauman, B. Leibe More recently used for describing scenes and objects for the sake of indexing or classification. Sivic & Zisserman 2003; Csurka, Bray, Dance, & Fan 2004; many others.

24. Inverted file index for images comprised of visual words Image credit: A. Zisserman K. Grauman, B. Leibe Word number List of image numbers

25. Bags of visual words Summarize entire image based on its distribution (histogram) of word occurrences. Analogous to bag of words representation commonly used for documents. K. Grauman, B. Leibe Image credit: Fei-Fei Li

26. Video Google System 1.Collect all words within query region 2.Inverted file index to find relevant frames 3.Compare word counts 4.Spatial verification Sivic & Zisserman, ICCV 2003 Demo online at : http://www.robots.ox.ac.uk/~vgg/ research/vgoogle/index.html 26K. Grauman, B. Leibe Query region Retrieved frames

27. Visual vocabulary formation Issues: Sampling strategy Clustering / quantization algorithm What corpus provides features (universal vocabulary?) Vocabulary size, number of words K. Grauman, B. Leibe

28. Sampling strategies K. Grauman, B. Leibe Image credits: F-F. Li, E. Nowak, J. Sivic Dense, uniformly Sparse, at interest points Randomly Multiple interest operators To find specific, textured objects, sparse sampling from interest points often more reliable. Multiple complementary interest operators offer more image coverage. For object categorization, dense sampling offers better coverage. [See Nowak, Jurie & Triggs, ECCV 2006]

29. Clustering / quantization methods k-means (typical choice), agglomerative clustering, mean-shift,… 29K. Grauman, B. Leibe