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  1. Visual Cognition IIObject Perception

  2. Theories of Object Recognition • Template matching models • Feature matching Models • Recognition-by-components • Configural models

  3. Template matching TEST INSTANCE “J” TEMPLATE “T” TEMPLATE Detect patterns by matching visual input with a set of templates stored in memory – see if any template matches.

  4. Problem: what if the object differs slightly from the template? E.g., it is rotated or scaled differently? Solution: use a set of transformations to best align the object with a template (using translation, rotation, scaling) TEST INSTANCE rotation “J” TEMPLATE “T” TEMPLATE

  5. Template-matching works well in constrained environments

  6. Figure 2-15 (p. 58)Examples of the letter M. Problem: template matching is not powerful enough for general object recognition

  7. Feature Theories • Detect objects by the presence of features • Each object is broken down into features • E.g. A = + +

  8. Problem • Many objects consist of the same collection of features • Need to also know how the features relate to each other  structural theories • One theory is recognition by components Different objects, similar sets of features

  9. Recognition by Components • Biederman (1987): Complex objects are made up of arrangements of basic, component parts: geons. • “Alphabet” of 24 geons • Recognition involves recognizing object elements (geons) and their configuration

  10. Why these geons? • Choice of shape vocabulary seems a bit arbitrary • However, choice of geons was based on non-accidental properties. The same geon can be recognized across a variety of different perspectives: except for a few “accidental” views:

  11. Viewpoint Invariance

  12. Viewpoint invariance is possible except for a few accidental viewpoints, where geons cannot be uniquely identified

  13. Deleting line segments Deleting vertices Prediction Object • Recognition is easier when geons can be recovered • Disrupting vertices disrupts geon processing more than just deleting parts of lines

  14. Evidence from priming experiments

  15. Problem • Theory does not say how color, texture and small details are processed. These are often important to tell apart similar objects. E.g.:

  16. Configural models of recognition • Individual instances are not stored; what is stored is an “exemplar” or representative element of a category • Recognition based on “distance” between perceived item and prototype prototype match no match “Face space”

  17. Configural Models

  18. Configural effects in face processing How about these ones? By disrupting holistic processing, it becomes easier to process the individual parts Do these faces have anything in common?

  19. Face superiority effect Farah (1994)

  20. Results

  21. Face superiority effect • Parts of faces are not processed independently. The context of other face parts (e.g. mouth) influences recognition of a particular part (e.g. nose) • Face superiority effect disappears when face is inverted

  22. Context and Object Recognition

  23. What do you see?

  24. Top-down vs. Bottom up Visual Input Low Level Vision Bottom-up processing  Stimulus driven High Level Vision Knowledge

  25. Top-down vs. Bottom up Visual Input Low Level Vision • Top-down processing • Knowledge driven • Context Effects High Level Vision Knowledge

  26. Top-down effects: knowledge influences perception

  27. Slide from Rob Goldstone

  28. Problem for many object recognition theories. How to model role of context? Context can often help in identification of an object Later identification of objects is more accurate when object is embedded in coherent context

  29. Context can alter the interpretation of an object

  30. Context Effects in Letter Perception The word superiority effect: discriminating between letters is easier in the context of a word than as letters alone or in the context of a nonword string. DEMO: (Reicher, 1969)

  31. Word superiority effect suggests that information at the word level might affect interpretation at the letter level • Interactive activation model: neural network model for how different information processing levels interact • Levels interact • bottom up: how letters combine to form words • top-down: how words affect detectability of letters

  32. Three levels: feature, letter, and word level Nodes represent features, letters and words; each has an activation level Connections between nodes are excitatory or inhibitory Activation flows from feature to letter to word level and back to letter level The Interactive Activation Model (McClelland & Rumelhart, 1981)

  33. PDP: parallel distributed processing Bottom-up: feature to word level Top-down: word back to letter level Model predicts Word superiority effect because of top-down processing The Interactive Activation Model (McClelland & Rumelhart, 1981)

  34. Predictions of the IA model – stimulus is “WORK” WORK WORD WEAR • At word level, evidence for “WORK” accumulates over time • Small initial increase for “WORD”

  35. At letter level, evidence for “K” accumulates over time – boost from word level “D” is never activated because of inhibitory influence from feature level Predictions of the IA model – stimulus is “WORK” K R D

  36. For a demo of the IA model, see:

  37. Take-home message • What you see is not what is out there in the outside world (ie., not like “taking a picture”), but instead a result of visual computation -- only those computations that are critical for survival, shaped by the evolution.