Recognition

Recognition

1. Visual recognition

   • 1.Description (construct internal representaion)
   • 2.processes to store information
   • 3.processes to compare object viewed with stored descriptions
   • 4.different angles (nature of mechanism)
   • generating & comparing descriptions of current object viewed with previous object descriptions seen.

2. Humphreys & Bruce (1989)

   • 5-step model
     • PERCEPTUAL PROCESSING (what it is)
     • SEMANTIC CLASSIFICATION (object specifics)
     • NAMING (what object is)
     • VIEWPOINT-DEPENDANT OBJECT DESCRIPTIONS (similar to Marr's 2 1/2D sketch)
     • EARLY VISUAL PROCESSING (similar to Marr's full primal sketch)
   • humans CLASSIFY between items
     • 'object' (an apple)
       • class of object
     • specific (eg, Sigmund Freud)
       • specific instance
     • BETWEEN CATEGORY v's WITHIN CATEGORY
       • faces researched differently to object recognition

3. Pike et al. (2002)

   • can identify famous faces even if poorly presented (eg E-FIT)
   • recognition of unfamiliar faces is poor (eg, line-ups)

4. Young et al. (1993)

   • different cognitive functions used to recognise faces compared to emotions displayed

5. Gibson (1986)

   • Active processing (interact with environment)
   • recognition through touch
     • feedback system = brain + touch receptors)
       • where pressure applied is regulated by brain. Based on sensory information generated by touch
       • location of limbs (KINESTHESIS) using touch receptors can guide hands
         • exact position (kinesthesis) + what it feels like (touch receptors) = HAPTIC INFORMATION (generate description of object)
           • haptic info good for recognition of object's weight. Yet visual more accurate for processing shape (esp. 3-D) & distance (out of reach)
           • haptic is active perception. Not always passive, therefore, criticism of H & B model
       • keep track of relative location on limbs (PROPRIOCEPTION)

6. Lederman & Klatzky (1987)

   • exploratory procedure (use of hands to gain haptic information.
   • (1990) each procedure gained particular type of info (shape, texture) by moving hands over contour/surface

7. recognising 2-D processing may use different cognitive processing than 3-D

8. Marr & Nishihara (1978) recognising 3-D objects

   • generalised cones (3-D images) generating an object-centred description. canonical coordinate frame.
   • multi-step process
     • 1. derive the object shape. (identify central axis using 2 1/2 D sketch)
       • has 3 assumptions, must accept all 3 to correctly identify.
     • 2. locate object axis and derive 3-D description
       • 1.area of concavity. 2.divide area in primitives. 3.find axis for each primitive. 4.link them to form 3-D
     • 3. compare 3-D description to mental catalogue of objects
       • 1. compare mental catalogue. 2. hierarchical levels. 3. once match found process ends
   • evaluation
     • FOR
       • locating central axis critical to recognition- supported by Lawson & Humphreys (1996)
       • Warrington & Taylor (1978) patients with right hemisphere focal lesions- difficulty recognising from different viewpoints. eg. comparing photos of same object from different viewpoint.
         • unable to convert 2-D to 3-D. Features needed to identify was obscured by different angle (rotation)
       • Humphreys & Riddoch (1984) foreshortened images/ hidden features. foreshortened recognised less- suggesting major axis is important to forming 3-D model
       • explains misinterpretation if contour generator is misidentified
     • AGAINST
       • within-category discrimination hard to explain- converting to generalised cones leads to same representations (cannot distinguish differences)

9. Pattern matching theories ( uses templates in memory) unlikely though as could have too many templates or large generic template

10. Feature recognition theories (key features of image extracted)

11. sturctural description theories ( key features & how they are organised with each other) compared to internal representations until match found

    • adapts to variety & ambiguity. describe in computer & human language. can recognise 3-D versions of 2-D images

12. 3-D recognition must be able to occur independent of viewpoint. viewer-centred description change to object-centred description

13. Beiderman's Theory

    • agrees with Marr & Nishihara's theory
      • Enter text here
    • complex objects represented as hierarchies of similar shapes
    • approx 36 geons used to represent objects
    • concavity used to sub-divide objects