They say bees can recognize human faces... Can you believe it? :)
I am not sure about it, but as Dr. Forsyth said, we definitely have to have animal studies
on face recognition research. That's for sure.
- H. Choi
Showing posts with label vision. Show all posts
Showing posts with label vision. Show all posts
HMAX
M. Riesenhuber and T. Poggio,
"Hierarchical Models of Object Recognition in Cortex,"
Nature Neurosciece, Vol. 2, No. 11, November 1999. pp 1019-1025.
They say simple cells and complex cells and
complex cells are responsible for invariant properties.
Invariance can be implemented as a pooling mechanism
where there are view-invariant units and view-tuned units.
Hierarchical feedforward network is considered and
the network is based on MAX rather than linear summation (SUM).
MAX is proved to be more robust and invariant than SUM.
That is, Hierarchical MAX has view-invariant object recognition ability
and is supported by biological (physiological) facts.
Anyway, I say, this is a very similar concept to ISA, but a little more flexible.
But this paper doesn't say much of implementation such as
how to get the simple cells or complex cells,
and how to construct the network structure.
See [1] for a specific implementation and examples of HMAX.
In [1], interestingly, simple and complex cells are not learned from data
but designed by second derivative of Gaussians.
[1] T. Serre and M. Riesenhuber,
"Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model,
and Implications for Invariant Object Recognition in Cortex,"
AI Memo 2004-017, CBCL Memo 239, MIT, July 2004.
- H. Choi
"models of object recognition"
M. Riesenhuber and T. Poggio,
"Models of Object Recognition,"
Nature Neurosciece Supplement, Vol. 3, November 2000. pp 1199-1204
It's a review paper about object recognition models.
Here is what I got from the paper.
"the distinction between identification and categorization is mostly semantic."
There are two kinds of models for object recognition.
1. view-based model: "objects are represented as collections of view- specific features"
It is something like ICA.
2. object-centered model: there is 3-D model of the object.
It's something like Geon theory.
They took "view-based model" in this paper.
Considering the speed of processing in the brain,
feedback model cannot be the prime model for object recognition.
It should be more like feedforward processing
where invariant properties can be obtained by hierarchical structure.
Making the connections between input image to higher level units as in Fig. 3,
the different tasks (categorization and identification) can be achieved by learning.
Complex Cells and Object Recognition
S. Edelman, N. Intrator and T. Poggio, "Complex Cells and Object Recognition," NIPS97.
Note that this paper was published in 1997, which is 'long time ago.'
It says complex cells-like filter has invariant recognition. And, actually, it is really simple. Apply one filter for complex cells and check the correlation of the filter output to classify.
The problem is how to implement the complex cells-like filters. And we have one answer, which is independent subspace analysis (ISA) which is kind of a generalization of independent component analysis (ICA). BTW, ICA is a filter like simple cells.
The real problem is these theories are not like math theories. So the performance really depends on the situations such as noise or background or the shape of object.
Our brain is sooooo amazing... How does it do all these complicated stuffs?
- H. Choi
Note that this paper was published in 1997, which is 'long time ago.'
It says complex cells-like filter has invariant recognition. And, actually, it is really simple. Apply one filter for complex cells and check the correlation of the filter output to classify.
The problem is how to implement the complex cells-like filters. And we have one answer, which is independent subspace analysis (ISA) which is kind of a generalization of independent component analysis (ICA). BTW, ICA is a filter like simple cells.
The real problem is these theories are not like math theories. So the performance really depends on the situations such as noise or background or the shape of object.
Our brain is sooooo amazing... How does it do all these complicated stuffs?
- H. Choi
Active Control..
K. L. Harman, G. K. Humphrey, and M. A. Goodale, "Active Manual Control of Object Views Facilitates Visual Recognition," Current Biology, Vol. 9, No 22. pp1315-1318. 1999.
To sum up, first, observers who have active control on the object have better recognition than others who see the same sequence passively. The response time is shorter but accuracy is the same which is a little ackward. Second, active observers concentrate on certain angles than others. That is, this paper is kind of the first paper to emphasize on the active control.
But the reason why it is efficient is still a research topic to go.
In a word, active control is important on recognition, which now we all know and agree to... :)
- H. Choi
To sum up, first, observers who have active control on the object have better recognition than others who see the same sequence passively. The response time is shorter but accuracy is the same which is a little ackward. Second, active observers concentrate on certain angles than others. That is, this paper is kind of the first paper to emphasize on the active control.
But the reason why it is efficient is still a research topic to go.
In a word, active control is important on recognition, which now we all know and agree to... :)
- H. Choi
3D Geon Classification
W. Xing, W. Liu and B. Yuan, "A Novel Integrated Scheme for Extracting Superquadric-based Geons from 3D data," in Proc. of ICSP'04, 2004.
To my knowledge, this paper is the first paper to try to implement Geon Theory, even though this is just about Geon classification. They build up a superquadric-based model for 3D object data and optimize the parameters. And then they apply SVM to the extracted features from the parameters of the model to classify the object into geon classes.
This is not about 2D image but 3D data. And this makes a model for each 3D data and extract some features from the model parameters, which might be rotation and transition invariant. This does not sound natural and not a visual recognition.
- H. Choi
To my knowledge, this paper is the first paper to try to implement Geon Theory, even though this is just about Geon classification. They build up a superquadric-based model for 3D object data and optimize the parameters. And then they apply SVM to the extracted features from the parameters of the model to classify the object into geon classes.
This is not about 2D image but 3D data. And this makes a model for each 3D data and extract some features from the model parameters, which might be rotation and transition invariant. This does not sound natural and not a visual recognition.
- H. Choi
Geon Theory and Its Implementation (?)
K. Casey and C. Exton, "A Java 3D Implementation of a Geon Based Visualisation Tool for UML," PPPJ 2003, Kilkenny City, Ireland, 16-18 June, 2003.
I was googling to check if there has been any kind of implementation of Geon Theory. So, when I typed in 'goen' and 'implementation,' this paper popped up to my surprise. I was thinking this theory was too hard to be implemented.
But... it turned out that this paper is just about the implementation of a visualization tool based on geon theory... not implementation of geon theory, in that the geons and their relations are equivalent to ULM diagram, which is a visual language for modeling software designs.
Anyway, so far, to my knowledge, there has been no successful implementation of Geon Theory. If you know any implementation, plz let me know. :)
- H. Choi
I was googling to check if there has been any kind of implementation of Geon Theory. So, when I typed in 'goen' and 'implementation,' this paper popped up to my surprise. I was thinking this theory was too hard to be implemented.
But... it turned out that this paper is just about the implementation of a visualization tool based on geon theory... not implementation of geon theory, in that the geons and their relations are equivalent to ULM diagram, which is a visual language for modeling software designs.
Anyway, so far, to my knowledge, there has been no successful implementation of Geon Theory. If you know any implementation, plz let me know. :)
- H. Choi
Three-dimensional object recognition
M. J. Tarr, P. Williams, W. G. Hayward and I. Gauthier, "Three-Dimensional Object Recognition is Viewpoint Dependent," Nature Neuroscience, Vol. 1, No 4, 1998. pp275-277.
It is a little against Biederman's RBC (recognition-by-components) which is based on geons and their relations. Biederman's geons are viewpoint independent. This paper says they are not independent.
Obviously, object recognition is a viewpoint-dependent process. So this paper makes more sense. And this paper makes a way to connect this whole things to X which means a lot to me*.
One thing more interesting is one of the assumptions of whole this geon theory, which is that recognition of individual geons are equally accurate. Actually in real pattern recognition, it is a big deal. We should handle this first, if we want this theory.
* X refers to something that I am currently working on.
- H. Choi
It is a little against Biederman's RBC (recognition-by-components) which is based on geons and their relations. Biederman's geons are viewpoint independent. This paper says they are not independent.
Obviously, object recognition is a viewpoint-dependent process. So this paper makes more sense. And this paper makes a way to connect this whole things to X which means a lot to me*.
One thing more interesting is one of the assumptions of whole this geon theory, which is that recognition of individual geons are equally accurate. Actually in real pattern recognition, it is a big deal. We should handle this first, if we want this theory.
* X refers to something that I am currently working on.
- H. Choi
Visual Recognition
K. Grill-Spector and N. Kanwisher, "Visual Recognition: As soon as you know it is there, you know what it is," Psychological Science, Vol 16, No 2, 2005. pp152-160
It tests two hypotheses about processing order between detection and recognition, between basic level recognition and other level recognition. To sum up, detection (or segmentation) and recognition work at the same time and general categorization is faster than finer level identification. And we can not tell nothing more than these, which are still hidden mysteries.
It is really amazing and intuitive that detection and recognition are not independent and work together. We should try to recognize an object and the class information should be applied to segment the object and the segmentation result should be used to classify the object again and again. Does it sound familiar to you? Yes!!! It sounds like the EM algorithm. How beautiful!!!
- H. Choi
It tests two hypotheses about processing order between detection and recognition, between basic level recognition and other level recognition. To sum up, detection (or segmentation) and recognition work at the same time and general categorization is faster than finer level identification. And we can not tell nothing more than these, which are still hidden mysteries.
It is really amazing and intuitive that detection and recognition are not independent and work together. We should try to recognize an object and the class information should be applied to segment the object and the segmentation result should be used to classify the object again and again. Does it sound familiar to you? Yes!!! It sounds like the EM algorithm. How beautiful!!!
- H. Choi
Visual Object Recognition
Chapter 4 Visual Object Recognition, Irving Biederman
in An Invitation to Cognitive Science, 2nd Edition, Visual Cognition, Vol2.
It's about a psychological understanding on viewpoint-invariant object recognition. Especially it says about Geon Theory. Geon is a component of an object, which is viewpoint-invariant and object recognition is based on geons and their relations with other geons.
So far so good...
As always in some psycological research, however, we don't know how to find these geons and the relations from image inputs. With different illuminations, different angles and noise and background, finding geons is a big deal itself in machine-based object recognition. Even the experiments in this book were conducted with human brain. It says just what the result of brain function on visual input is, not how it works in details. So it doesn't say that much about how to implement this concept in machine.
- H. Choi
in An Invitation to Cognitive Science, 2nd Edition, Visual Cognition, Vol2.
It's about a psychological understanding on viewpoint-invariant object recognition. Especially it says about Geon Theory. Geon is a component of an object, which is viewpoint-invariant and object recognition is based on geons and their relations with other geons.
So far so good...
As always in some psycological research, however, we don't know how to find these geons and the relations from image inputs. With different illuminations, different angles and noise and background, finding geons is a big deal itself in machine-based object recognition. Even the experiments in this book were conducted with human brain. It says just what the result of brain function on visual input is, not how it works in details. So it doesn't say that much about how to implement this concept in machine.
- H. Choi
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