1 / 38

Object Recognizing

Object Recognizing. Object Classes. Individual Recognition. Object parts Full Interpretation. Window. Mirror. Window. Door knob. Headlight. Back wheel. Bumper. Front wheel. Headlight. Action recognition (except 2). Class Non-class . Class Non-class.

terrieg
Download Presentation

Object Recognizing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Object Recognizing

  2. Object Classes

  3. Individual Recognition

  4. Object partsFull Interpretation Window Mirror Window Door knob Headlight Back wheel Bumper Front wheel Headlight

  5. Action recognition (except 2)

  6. Class Non-class

  7. Class Non-class

  8. Is this an airplane?

  9. Features and Classifiers Same features with different classifiers Same classifier with different features

  10. Generic Features Simple (wavelets) Complex (Geons)

  11. Marr-Nishihara

  12. Mental Rotation

  13. 3-D Parts • Implementations – poor results • View-specific recognition • fMRI studies • Instead: Using image patches

  14. Class-specific Features: Common Building Blocks

  15. Optimal Class Components? • Large features are too rare • Small features are found everywhere Find features that carry the highest amount of information

  16. Mutual information H(C) F=0 F=1 H(C) when F=1 H(C) when F=0 I(C;F) = H(C) – H(C/F)

  17. Mutual Information I(C,F) Class: 1 1 0 1 0 1 0 0 Feature: 1 0 0 1 1 1 0 0 I(F,C) = H(C) – H(C|F)

  18. Horse-class features Car-class features Pictorial features Learned from examples

  19. Star model Detected fragments ‘vote’ for the center location Find location with maximal vote In variations, a popular state-of-the art scheme

  20. Recognition Features in the Brain

  21. fMRI Functional Magnetic Resonance Imaging

  22. תמונות של פעילות המח

  23. LO object recognition V1 early processing

  24. Class-fragments and Activation Malach et al 2008

  25. Bag of words

  26. – Bag of visual words A large collection of image patches

  27. – – – Each class has its words historgram Limited or no Geometry Simple and popular Visual words are used, but not for full recognition model

  28. HoG Descriptor Dallal, N & Triggs, B. Histograms of Oriented Gradients for Human Detection

  29. SIFT: Scale-invariant Feature Transform • MSER: Maximally Stable Extremal Regions • SURF: Speeded-up Robust Features • Cross correlation • …. • HoG and SIFT are the most widely used.

  30. DPM Felzenszwalb • Felzenszwalb, McAllester, Ramanan CVPR 2008. A Discriminatively Trained, Multiscale, Deformable Part Model • Many implementation details, will describe the main points.

  31. HoG descriptor

  32. Using patches with HoG descriptors and classification by SVM Person model: HoG

  33. Object model using HoG A bicycle and its ‘root filter’ The root filter is a patch of HoG descriptor Image is partitioned into 8x8 pixel cells In each block we compute a histogram of gradient orientations

  34. Dealing with scale: multi-scale analysis The filter is searched on a pyramid of HoG descriptors, to deal with unknown scale

  35. Adding Parts A part Pi = (Fi, vi, si, ai, bi). Fi is filter for the i-th part, vi is the center for a box of possible positions for part i relative to the root position, si the size of this box ai and bi are two-dimensional vectors specifying coefficients of a quadratic function measuring a score for each possible placement of the i-th part. That is, ai and bi are two numbers each, and the penalty for deviation ∆x, ∆y from the expected location is a1 ∆x + a2 ∆y+ b1 ∆x2 + b2 ∆y2

  36. Bicycle model: root, parts, spatial map Person model

More Related