object and face recognition Flashcards
gestalt psychology
- Gestalt means “whole” (in German)
- They suggested that perception could not be done by breaking it down into parts, but by considering the whole experience
- The whole is other than the sum of its parts.
- Obvious links to object identification
gestalt laws
Law of Pragnanz (i.e. Good figure - Law of simplicity)
- The percept you see should be the simplest interpretation of the scene
- They are not really “laws”
- Not like laws of physics or the law of natural selection
object ground segmentation
considered as an inference problem about where a given object is throughout a video sequence.
object recognition
Arguably one of the most difficult tasks the visual system has to perform
Clear dichotomy between the human ability to perform a task effortlessly and its intrinsic complexity
Computer scientists have been trying to solve this problem for decades!
How do we do it?
marr’s vision
provides a framework which is both computationally traceable and biologically plausible in the understanding of human vision.
Object parts are represented independently of their spatial configuration and viewpoint.
image based models
Specific views are ‘stored’ and recognition performance is (somehow) based on generalization from these
Image-based models encode ‘structured’ templates of
viewpoint-dependent representations
structural description models
Information about the 3D structure of an object is extracted from a single view
e.g. you see a teapot as a collection of different parts
recognition by component
Objects are represented using basic shape units called geons.
geons
Geons are the simple 2D or 3D forms such as cylinders, bricks, wedges, etc, corresponding to the simple parts of an object in Biederman’s theory of object recognition.
They are defined by variations in a small number of basic parameters called Non-Accidental Properties (NAP)
NAP
• NAPs are basic features that define variation in shapes which are view independent
Properties of Naps
• Curvilinearity – curviness in the 2D image
• Parallelism – lines in parallel in 2D object
• Cotermination – two or more edges that terminate
at same point
• Symmetry – axis of symmetry in 2D image reflect the axis of symmetry on object
• Collinearity – a straight line in the 2D image
Role of disparity and view generalisation during object recognition
Two main theories prediction:
Image based models
Structural description models
canonical viewpoint
describe perspectives in which identification performance is best.
results revealed a double dissociation between task (recognition and recall) and type of object perspective.
The “front”, “side”, and “top” views of an object.
Frequency hypothesis
Maximal information hypothesis
frequency hypothesis
The easiness of recognition is related to the number of times we have see the objects from each viewpoint.
maximal information hypothesis
Maximal information hypothesis: Some views provide more information than others about the objects.
e.g. Clocks are preferred as purely frontal
Why is face recognition important to psychology
- It involves ‘within-category’ discrimination
- Not is it a face, but which face it is?
- Errors in face-recognition can have catastrophic consequences
- Eye witness testimony (e.g. Devlin, 1976).
- Technology