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Vision is inferential: Light
Vision is inferential: Light
Vision is Inferential
Vision is Inferential
Vision is Inferential: Prior Knowledge
Vision is Inferential: Prior Knowledge
Vision is Inferential: Prior Knowledge
Vision is Inferential: Prior Knowledge
Boundary Detection: Local cues
Boundary Detection: Local cues
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
(Sharon, Balun, Brandt, Basri)
(Sharon, Balun, Brandt, Basri)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
Boundary Detection
Boundary Detection
Boundary Detection
Boundary Detection
Texture
Texture
Texture
Texture
Texture
Texture
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Tracking
Stereo
Stereo
Stereo
Stereo
Stereo
Stereo
Motion
Motion
Motion - Application
Motion - Application
Motion - Application
Motion - Application
Pose Determination
Pose Determination
Recognition - Shading
Recognition - Shading
Recognition - Shading
Recognition - Shading
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
CMSC 426: Image Processing (Computer Vision)
Classification
Classification
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CMSC 426: Image Processing (Computer Vision)

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1CMSC 426: Image Processing (Computer 39Modeling + Algorithms. Build a simple
Vision). David Jacobs. model of the world (eg., flat, uniform
2Vision. ``to know what is where, by intensity). Find provably good algorithms.
looking.’’ (Marr). Where What. Experiment on real world. Update model.
3Why is Vision Interesting? Psychology Problem: Too often models are simplistic
~ 50% of cerebral cortex is for vision. or intractable.
Vision is how we experience the world. 40Bayesian inference. Bayes law: P(A|B)
Engineering Want machines to interact with = P(B|A)*P(A)/P(B). P(world|image) =
world. Digital images are everywhere. P(image|world)*P(world)/P(image)
4Vision is inferential: Light. P(image|world) is computer graphics
(http://www-bcs.mit.edu/people/adelson/che Geometry of projection. Physics of light
kershadow_illusion.html). and reflection. P(world) means modeling
5Vision is Inferential. objects in world. Leads to
6Vision is Inferential: Geometry. statistical/learning approaches. Problem:
movie. Too often probabilities can’t be known and
7Vision is Inferential: Prior are invented.
Knowledge. 41Engineering. Focus on definite tasks
8Vision is Inferential: Prior with clear requirements. Try ideas based
Knowledge. on theory and get experience about what
9Computer Vision. Inference ? works. Try to build reusable modules.
Computation Building machines that see Problem: Solutions that work under
Modeling biological perception. specific conditions may not generalize.
10A Quick Tour of Computer Vision. 42Marr. Theory of Computation
11Boundary Detection: Local cues. Representations and algorithms
12Boundary Detection: Local cues. Implementations. Primal Sketch 2?D Sketch
13 3D Representations Problem: Are things
14 really so modular?
15Boundary Detection. 43The State of Computer Vision. Science
http://www.robots.ox.ac.uk/~vdg/dynamics.h Study of intelligence seems to be hard.
ml. Some interesting fundamental theory about
16(Sharon, Balun, Brandt, Basri). specific problems. Limited insight into
17 how these interact.
18Boundary Detection. Finding the Corpus 44The State of Computer Vision.
Callosum (G. Hamarneh, T. McInerney, D. Technology Interesting applications:
Terzopoulos). inspection, graphics, security, internet….
19Texture. Photo. Pattern Repeated. Some successful companies. Largest
20Texture. Photo. Computer Generated. ~100-200 million in revenues. Many
21Tracking. (Comaniciu and Meer). in-house applications. Future: growth in
22Tracking. (www.brickstream.com). digital images exciting.
23Tracking. 45Related Fields. Graphics. “Vision is
24Tracking. inverse graphics”. Visual perception.
25Tracking. Neuroscience. AI Learning Math: eg.,
26Tracking. geometry, stochastic processes.
27Stereo. Optimization.
http://www.ai.mit.edu/courses/6.801/lect/l 46Contact Info. Prof: David Jacobs
ct01_darrell.pdf. Office: Room 4421, A.V. Williams Building
28Stereo. http://www.magiceye.com/. (Next to CSIC). Phone: (301) 405-0679
29Stereo. http://www.magiceye.com/. Email: djacobs@cs.umd.edu Homepage:
30Motion. http://www.cs.umd.edu/~djacobs TA:
http://www.ai.mit.edu/courses/6.801/lect/l Hyoungjune Yi Email: aster@umiacs.umd.edu.
ct01_darrell.pdf. 47Tools Needed for Course. Math Calculus
31Motion - Application. Linear Algebra (can be picked up).
(www.realviz.com). Computer Science Algorithms Programming,
32Pose Determination. Visually guided we’ll use Matlab. Signal Processing (we’ll
surgery. teach a little).
33Recognition - Shading. Lighting 48Rough Syllabus.
affects appearance. 49Course Organization. Reading
34 assignments in Forsyth & Ponce, plus
35 some extras. ~6-8 Problem sets -
36Classification. (Funkhauser, Min, Programming and paper and pencil Two
Kazhdan, Chen, Halderman, Dobkin, Jacobs). quizzes, Final Exam. Grading: Problem sets
37Vision depends on: Geometry Physics 30%, quizzes: first quiz 10%; second quiz
The nature of objects in the world (This 20%; final 40%. Web page:
is the hardest part). www.cs.umd.edu/~djacobs/CMSC426/CMSC426.ht
38Approaches to Vision. .
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CMSC 426: Image Processing (Computer Vision)

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