1. Reading Assignments: Lecture 6. Object Recognition None
CS664, USC, Spring 2002
Lecture 6. Object Recognition
Reading Assignments:
None

3. Four stages of representation (Marr, 1982)
1) pixel-based (light intensity)
2) primal sketch (discontinuities in intensity)
3) 2 ½ D sketch (oriented surfaces, relative depth between surfaces)
4) 3D model (shapes, spatial relationships, volumes)
problem: computationally intractable!

5. Challenges of Object Recognition
The binding problem: binding different features (color, orientation, etc) to yield a unitary percept (see next slide)
Bottom-up vs. top-down processing: how
much is assumed top-down vs. extracted
from the image?
Perception vs. recognition vs. categorization: seeing an object vs. seeing is as something. Matching views of known objects to memory vs. matching a novel object to object categories in memory.
Viewpoint invariance: a major issue is to recognize objects irrespectively of the viewpoint from which we see them.

7. Viewpoint Invariance Major problem for recognition.
Biederman & Gerhardstein, 1994:
We can recognize two views of an unfamiliar object as being the same object.
Thus, viewpoint invariance cannot only rely on matching views to memory.

8. Models of Object Recognition
See Hummel, 1995, The Handbook of Brain Theory & Neural Networks
Direct Template Matching:
Processing hierarchy yields activation of view-tuned units.
A collection of view-tuned units is associated with one object.
View tuned units are built from V4-like units,
using sets of weights which differ for each object.
e.g., Poggio & Edelman, 1990; Riesenhuber & Poggio, 1999

9. Computational Model of Object Recognition (Riesenhuber and Poggio, 1999)

10. the model neurons are
tuned for size
and 3D orientation
of object

11. Models of Object Recognition
Hierarchical Template Matching:
Image passed through layers of units with progressively more complex features at progressively less specific locations.
Hierarchical in that features at one stage are built from features at
earlier stages.
e.g., Fukushima & Miyake (1982)’s Neocognitron:
Several processing layers, comprising
simple (S) and complex (C) cells.
S-cells in one layer respond to conjunc-
tions of C-cells in previous layer.
C-cells in one layer are excited by
small neighborhoods of S-cells.

12. Models of Object Recognition
Transform & Match:
First take care of rotation, translation, scale, etc. invariances.
Then recognize based on standardized pixel representation of objects.
e.g., Olshausen et al, 1993,
dynamic routing model
Template match: e.g., with
an associative memory based on
a Hopfield network.

13. Recognition by Components
Structural approach to object recognition:
Biederman, 1987:
Complex objects are composed so simpler pieces
We can recognize a novel/unfamiliar object by parsing it in terms of its component pieces, then comparing the assemblage of pieces to those of known objects.

14. Recognition by components (Biederman, 1987)
GEONS: geometric elements of which all objects are composed (cylinders, cones, etc). On the order of 30 different shapes.
Skips 2 ½ D sketch: Geons are directly recognized from edges, based on their nonaccidental properties (i.e., 3D features that are usually preserved by the projective imaging process).

15. Basic Properties of GEONs
They are sufficiently different from each other to be easily discriminated
They are view-invariant (look identical from most viewpoints)
They are robust to noise (can be identified even with parts of image missing)

16. Support for RBC: We can recognize partially occluded
objects easily if the occlusions do not obscure the set
of geons which constitute the object.

17. Potential difficulties
Structural description not
enough, also need metric info
Difficult to extract geons
from real images
Ambiguity in the structu-
ral description: most often
we have several candidates
For some objects,
deriving a structural repre-
sentation can be difficult
Edelman, 1997

18. Geon Neurons in IT?
These are preferred
stimuli for some IT neurons.

20. Fusiform Face Area in Humans

21. Standard View on Visual Processing
representation
visual processing
The intuition is quite simple. If the visual system needs to construct some highly abstracted representation for certain object-recognition tasks, which we believe it does, then it must do so via a number of stages. The intermediate results at each stage is effective a representation. The entire processing pathway thus contains a hierarchy of representations, ranging from the most image-specific at the earliest stage to the most image-invariant at the latest stage.
Image specific
Supports fine discrimination
Noise tolerant
Image invariant
Supports generalization
Noise sensitive
Tjan, 1999

22. ? (e.g. Kanwisher et al; Ishai et al) (Tjan, 1999) Face
Early visual processing
Place ?
Common objects
(e.g. Kanwisher et al; Ishai et al)
primary visual processing
[Work in progress. Please do not circulate.]
A convergence of data seemed to suggest that the brain recognize a multitude of objects by the means of several distinct processing pathways, each with a particular functional specialization. In this talk, I am going to propose an alternative, which is more parsimonious but still can explain the same set of data. This alternative relies on a single processing pathway. Flexibility and self-adaptiveness are achieved by having multiple memory and decision sites along the processing pathway.
(Tjan, 1999)
Multiple memory/decision sites

23. Tjan’s “Recognition by Anarchy”
primary visual processing
...
Sensory
Memory
memory
memory
memory
Independent
Decisions
“R1”
“Ri”
“Rn”
Delays t1 ti tn
The central idea of our proposal is that the brain can tap this rich collection of representations, which are already there, by attaching memory modules along the visual-processing pathway. We further speculate that each memory site makes independent decisions about the identity of the incoming image. However, such response is not immediately sent out to the homunculus, but delayed by an amount set by each memory site at a trial by trial basis, depending on the site's confidence about its current decision and the amount of memory it needs to consult before reaching the decision.
[Animation] The homunculus does nothing but simple takes the first-arriving response as the system's response.
Homunculus’
Response
the first arriving response

24. A toy visual system
Task: Identify letters from arbitrary positions & orientations
“e”
For this purpose, we are going to build a simple toy visual system. The task for this toy system is to identify letters from arbitrary position and orientations. A generic implementation would go something like this:

25. normalize position normalize orientation Image down- sampling memory
An image comes in. The target letter is first centered in the image by computing the centroid of its luminance profile. Once centered, the principle axis of the luminance profile is determined and the entire image is rotated so that this axis is vertical. at this point, we have a representation that is invariant in both position and orientation. The traditional view is that this will be stored in memory or compared against existing items in memory, leading to recognition.
memory

26. normalize position normalize orientation Image Site 1 Site 2 Site 3
down-
sampling
In contrast, our proposal stated that the intermediate results are also committed to some form of sensory memory.
memory
Site 1
memory
Site 2
memory
Site 3

27. Study stimuli: 5 orientations  20 positions at high SNR
Test stimuli: 1) familiar (studied) views, 2) new positions, 3) new position & orientations
We measure performance of such a system by first exposing it to 5 orientations and 20 positions of each letters at high contrast. The system keeps these learning views in memory. We then tested that system by presenting it with letters selected from either the views studied, or views it hasn't seen before. The test stimuli are also presented at different SNR or contrast levels.
1800 {30%}
1500 {25%}
800 {20%}
450 {15%}
210 {10%}
Signal-to-Noise Ratio {RMS Contrast}

28. Processing speed for each recognition module depends
raw image
norm. pos.
norm. ori.
Site 3
Site 2
Site 1
We are going to use this little icon to represent our toy system in the data plots that follows. The gray arrows indicate the primary visual pathway. Starting with a raw image, its position is first normalized, then its orientation is also normalized. The color arrows represents memory sites: the raw image (red, Site 1), a representation that is invariant to position (green, Site 2), and one that is both position and orientation invariant (blue, Site 3).
Processing speed for each recognition module depends
on recognition difficulty by that module.

29. Novel positions & orientations Familiar views Novel positions
raw image
norm. pos.
norm. ori.
Site 3
Site 2
Site 1
Proportion Correct
These three plots show the performance obtained at each individual site under different stimulus condition. The red curves indicate performance for Site 1, green curves from Site 2 and blue curves for Site 3. As expected, Site 1, which keeps raw images in memory, has the best accuracy when tested with study views, but it cannot generalize to novel views. Site 3 on the other hand maintains essentially the same level of performance regardless of the view condition. This is because it uses a representation invariant to position and orientation.
Contrast (%)

30. Black curve: full model in which recognition is based
Novel positions
& orientations
Familiar views
Novel positions
raw image
norm. pos.
norm. ori.
Site 3
Site 2
Site 1
Proportion Correct
The black curve indicated the system's or the homunculus' performance based on the first-arriving response. Clearly, it tracks the performance of the best performing site in all conditions. Note that in the condition with novel positions, the system's performance is even better than the best performance sites. This is because even though Site 2 and 3 perform equally well, they make different kinds of errors. The simple timing rule used to delay the responses effectively picks out the most reliable response for each trial.
Contrast (%)
Black curve: full model in which recognition is based
on the fastest of the responses from the three stages.