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Computer Vision GroupUniversity of California Berkeley
From Pixels to Perception
TigerGrass
Water
Sand
outdoorwildlife
Tiger
tail
eye
legs
head
back
shadow
mouse
Computer Vision GroupUniversity of California Berkeley
Detection can be very fast
• On a task of judging animal vs no animal, humans can make mostly correct saccades in 150 ms (Kirchner & Thorpe, 2006)
– Comparable to synaptic delay in the retina, LGN, V1, V2, V4, IT pathway.
– Doesn’t rule out feed back but shows feed forward only is very powerful
EZ-Gimpy Results (Mori & Malik 03)
• 171 of 192 images correctly identified: 92 %
horse
smile
canvas
spade
join
here
Computer Vision GroupUniversity of California Berkeley
Caltech-101 [Fei-Fei et al. 04]
• 102 classes, 31-300 images/class
Computer Vision GroupUniversity of California Berkeley
Caltech 101 classification results
(By combining cues, one can get above 80% !)
Examples of Actions• Movement and posture change
– run, walk, crawl, jump, hop, swim, skate, sit, stand, kneel, lie, dance (various), …
• Object manipulation– pick, carry, hold, lift, throw, catch, push, pull, write, type, touch, hit,
press, stroke, shake, stir, turn, eat, drink, cut, stab, kick, point, drive, bike, insert, extract, juggle, play musical instrument (various)…
• Conversational gesture– point, …
• Sign Language
What makes computer vision interesting?
• Great scientific problem– 30-50% of the brain is devoted to it– Visual perception has been richly studied– Long history with contributions from greats such as Euclid, Maxwell,
Helmholtz, Mach, Schrodinger etc
• Great engineering problem– Search on the web for images/video– Enhancing visual experiences– Essential for robotics and AI
• Finally, we are making great progress– Availability of computing resources– Large collections make possible the use of machine learning techniques– Adoption of interdisciplinary approach