Outline

1. Outline • Announcements • Theoretical approaches to computer vision • Classical theories of vision • Visual perception as information processing

2. Announcements • Class web page http://www.cs.fsu.edu/~liux/courses/research/index.html • Lecture notes and papers can be obtained from http://www.cs.fsu.edu/~liux/courses/research/calendar.html • Possible programming assignments • You can certainly do your own project • Implement a method from the literature • Implement your own novel ideas on a problem as you are going to discuss in this class • You can also do a project on top of my programs • I will make my programs available to you and you can make changes Visual Perception Modeling

3. Theoretical Approaches to Vision • Classical theories of vision • Visual perception as information processing Visual Perception Modeling

4. Visual Perception as an Inverse Problem • Retinal images are generated by the light reflected from the 3-D world • The image formation is determined by the laws of optics • The area of image rendering is called computer graphics • Vision as an inverse problem • Get from optical images of scenes back to knowledge of the objects that gave rise to them Visual Perception Modeling

5. Problems with Inversion • Image formation is a well-defined function • Each point in the environment maps into a unique point in the image • Inverse process is not well-defined • Vision goes from 2-D to 3-D • Each point in the image could map into an infinite number of points in the environment • It is underconstrained Visual Perception Modeling

6. Vision as a Heuristic Process • Visual system makes a lot of assumptions about the nature of the environment and conditions under which it is viewed • These assumptions constrain the inverse problem enough to make it solvable most of the time • The resulting solution will be veridical if the assumptions are true • Vision is a heuristic process in which inferences are made about the most likely environmental condition that could have produced a given image Visual Perception Modeling

7. Computer Vision • The study of how computers can be programmed to extract useful information about the environment from the optical images • Computer metaphor • Minds are like programs that run on machines called brains • Minds are the “software” of biological computation and the brains are the “hardware” • Strong AI vs. weak AI • Artificial intelligence Visual Perception Modeling

8. Three Levels of Information Processing • Marr proposed this meta-theory, a theory about theories on vision • Three levels of any information processing systems • Computational level • Algorithmic level • Implementation level Visual Perception Modeling

9. Computational Level • Computational level • The most abstract description level • Informational constraints available for mapping input information to output information • It specifies what computation needs to be performed and on what information it should be based Visual Perception Modeling

10. Algorithmic Level • Algorithmic level specifies how a computation is executed in terms of information processing operations • There are in principle many different algorithms to accomplish the computational-level mapping of input to output • Two fundamental components • One must decide a representation for input and output information • One must construct a set of processes Visual Perception Modeling

11. Implementation Level • This level specifies how an algorithm actually is embodied as a physical process within a physical system • The same algorithm can be implemented using many physically different devices • Devices include brains as biological devices as well as different kinds of computers Visual Perception Modeling

12. Representations • A representation refers to a state of the visual system that stands for an environmental property, object, or event • Represented world • External world outside the information processing system • Representing world • The internal representation within the information processing system Visual Perception Modeling

13. Processes • Processes are the active components in an information processing system that transform or operate on information by changing the representation into the next • Dynamic aspect of the system that causes informational transformations to occur • Implicit vs. explicit • One of the most important aspects of processes is to make information that was implicit in the input representation explicit in the output Visual Perception Modeling

14. Processing as Inference • Helmholtz proposed that vision arrives at the interpretation that is the most likely state of affairs in the external world that could have caused the retinal stimulation • This proposal is called the likelihood principle Visual Perception Modeling

15. Perception as Bayesian Inference • Images I are observations • Scene properties S are not known • p(S) specifies the prior knowledge about the scene • The knowledge you have without looking at the image • Bayes’ rule Visual Perception Modeling

16. Top-down vs. Bottom-up Processes • Bottom-up processing • Data driven processing • Take a lower-level representation as input and create or modify a higher-level representation • Top-down processing • Expectation-driven processing • Processes that take a higher-level representation as input and produce or modify a lower-level representation Visual Perception Modeling

17. Four Stages of Visual Processing • Retinal image • Image-based stage • Surface-based stage • Object-based stage • Category-based stage Visual Perception Modeling