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September 3, 2013 Computer Vision Lecture 1: Human Vision
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Welcome to
CS 675 – Computer Vision
Fall 2013
Instructor: Marc Pomplun
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Instructor – Marc PomplunOffice: S-3-171
Lab: S-3-135
Office Hours: Tuesdays 3:00pm – 4:00pm Thursdays 6:45pm – 8:45pm
Phone: 287-6443 (office) 287-6485 (lab)
E-Mail: [email protected]
Website: http://www.cs.umb.edu/~marc/cs675/
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The Visual Attention Lab
Cognitive Science, esp. eye movements
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A poor guinea pig:
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Computer Vision:
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Modeling of Brain Functions
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Modeling of Brain Functionsunit and connectionin the interpretive network
unit and connectionin the gating network
unit and connectionin the top-down bias network
layer l +1
layer l -1
layer l
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Example: Distribution of Visual Attention
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Selectivity in Complex Scenes
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Selectivity in Complex Scenes
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Selectivity in Complex Scenes
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Selectivity in Complex Scenes
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Selectivity in Complex Scenes
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Selectivity in Complex Scenes
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Human-Computer Interfaces:
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Your Evaluation
• 4 sets of exercises each set 7.5% 30%
(only individual submissions allowed)
• Oral Presentation
10%
• midterm (75 minutes) 25%
• final exam (2.5 hours) 35%
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Grading
95%: A 90%: A-
74%: C+ 70%: C 66%: C-
86%: B+ 82%: B 78%: B-
62%: D+ 56%: D 50%: D-
50%: F
For the assignments, exams and your course grade, the following scheme will be used to convert percentages into letter grades:
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Complaints about Grading
If you think that the grading of your assignment or exam was unfair,
• write down your complaint (handwriting is OK),• attach it to the assignment or exam, • and give it to me or put it in my mailbox.
I will re-grade the exam/assignment and return it to you in class.
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Computer VisionComputer Vision is the science of building systems that can extract certain task-relevant information from a visual scene.
Such systems can be used for applications such as optical character recognition, analysis of satellite and microscopic images, magnetic resonance imaging, surveillance, identity verification, quality control in manufacturing etc.
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Computer VisionIn a way, Computer Vision can be considered the inversion of Computer Graphics.
A computer graphics systems receives as its input the formal description of a visual scene, and its output is a visualization of that scene.
A computer vision system receives as its input a visual scene, and its output is a formal description of that scene with regard to the system’s task.
Unfortunately, while a computer graphics task only allows one solution, computer vision tasks are often ambiguous, and it is unclear what the correct output should be.
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Computer VisionDigital Images
Binary Image Processing
Color
Image Filtering
Basic Image Transformation
Edge Detection
Image Segmentation
Shape Representation
Texture
Depth
Motion
Object Recognition
Image Understanding
Visible light is just a part of the electromagnetic spectrum
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Cross Section of the Human Eye
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Photoreceptor
Bipolar
Ganglion
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Major Cell Types of the Retina
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Receptive Fields
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Coding of Visual Information in the Retina Photoreceptors: Trichromatic Coding
Peak wavelength sensitivities of the three cones:Blue cone: Short- Blue-violet (420 nm) Green cone: Medium- Green (530 nm)Red Cone: Long- Yellow-green (560nm)
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Coding of Visual Information in the Retina Retinal Ganglion Cells:
Opponent-Process Coding
Negative afterimage: The image seen after a portion of the retina is exposed to an
intense visual stimulus; consists of colors complimentary to those of the physical stimulus.
Complimentary colors: Colors that make white or gray when mixed together.
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Stimuli in receptive field of neuron
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Cat V1 (striate cortex)
Orientation preference map
Ocular dominance map
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Modeling of Brain Functions
