csc508 computer vision introduction. csc508 what is it? a ripe lemon
TRANSCRIPT
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Computer Vision
Introduction
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What is it?
A ripe lemon
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What is it?
An unripe lemon
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What is it?
A ripe lemon
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What is it?
A ripe lemon
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What is it?
A ripe lemon
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What is it?
A ripe orange
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What is it?
An unripe orange
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What is it?
A ripe orange?
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What is it?
A tennis ball
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What is it?
A baseball
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What is it?
A “frisbee”
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What is it?
A ripe lemon
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What is it?
An unripe lemon
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What is it?
A ripe orange?(see next slide)
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What are they?
A ripe tangerine and a ripe orange
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What is it?
A flounder
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Meet Lenna
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Still Lenna?
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Still Lenna?
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Still Lenna?
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Still Lenna?
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Still Lenna?
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Illusions
• And now for something completely different…
Kanizsa Triangle (and variants)
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What do we process?
• Shape• Color• Texture • Shading• Gradients (edges)• Context• Transformations
– Rotation– Translation– Stretching
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How do we process visual scenes?
• Preprocessing– Image conditioning
• Low Level Vision– Feature extraction
• Mid Level Vision– Feature description
• High Level Vision– Object recognition
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What we (the human viewer) “see”
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Mechanization of Processing
• Cones: process color (3 types)• Primarily in the foveal region of the retina
• Rods: process shades of gray• Primarily in the peripheral region of the retina
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How should a computer process visual scenes?
• As it turns out, mimicking a biological system in circuits and software is extremely difficult– Cameras are not as sophisticated as the eye– Processors/software are not as sophisticated as the
brain
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156 145 137 146 163 151
150 173 171 177 174 171
104 132 133 142 164 165
86 116 164 162 155 152
95 121 150 137 123 136
123 128 141 129 126 129
Here’s what the computer “sees”
177 167 153 141 145 126
144 153 160 172 173 158
116 138 138 140 152 151
65 96 131 140 148 145
71 98 147 127 120 131
99 105 144 116 117 123
238 237 234 227 223 216
229 227 224 220 225 221
205 212 221 220 225 220
177 192 213 207 212 217
164 180 211 208 209 215
190 194 220 212 210 219
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Here’s what the computer “sees”
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Biological System is Restrictive
• We can improve a computer vision system by including multi-spectral data
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Visible Light (gray scale)
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Visible Light (color)
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Infrared Image
“Night Vision”
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Radar Image
“Through Smoke and Clouds”
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Magnetic Resonance Imaging (MRI)
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X-Ray
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Ultrasound
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How should a computer process visual scenes?
• Preprocessing– Image conditioning
• Low Level Vision– Feature extraction
• Mid Level Vision– Feature description
• High Level Vision– Object recognition
• Multi-spectral fusion– Combine images from different cameras
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Plans for the Class
• We will be studying selected algorithms from each of the areas– We will be…
• …writing code to implement the algorithms
• …experimenting with parameters to understand the sensitivity of the algorithms
– There’s no better way to learn this topic than by doing
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Programming
• Choose any language you like– Some languages provide image I/O, some do not– For those that do not, you’ll have to figure out how to
read/write images
• One option: ImageJ– Free from http://rsb.info.nih.gov/ij/– Full featured image processing tool kit– Extendable through plugins written in Java– Note that if you choose this option, you may NOT use the
provided processing functions. ImageJ will only be used to perform image I/O and display
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Reference Books• Don’t feel obligated to run out and buy these for the class!
– Feature Extraction & Image Processing• Nixon and Aguado – Newnes Press
– Vision Science: Photons to Phenomenology• Stephen E. Palmer – MIT Press
– Vision• David Marr – Freeman Press
– Eye, Brain and Vision• David H. Hubel – Scientific American Library
– Computer Vision• Linda G. Shapiro, George C. Stockman – Prentice Hall
– Robot Vision• Berthhold Klaus Paul Horn – MIT Press
– Digital Image Processing• Rafael C. Gonzalez, Richard E. Woods – Prentice Hall
– Visual Intelligence• Donald D. Hoffman – W. W. Norton & Company
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Reading
• Chapter 1
• Chapter 2
• Chapter 7
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Homework
• Describe a task that you think might benefit from computer vision (i.e. one that does not currently use visual processing)– How is the task currently performed?– How could computer vision make it better?– Do you think it’s feasible to implement?– For example: a few years back someone felt that a
computer mouse might be well suited to visual processing
• Due next class meeting