A Primer on Human Vision: Insights and Inspiration for Computer Vision
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1 A Primer on Human Vision: Insights and Inspiration for Computer Vision Guest&Lecture:&Marius&Cătălin&Iordan&& CS&131&8&Computer&Vision:&Foundations&and&Applications& 27&October&2014
2 detection recognition segmentation! visual! understanding
3 1. The Mammalian Visual System anatomy and processing pathways from processing to perception 2. Early Computation first stages of information processing edges: a basis for representing the visual world 3. Object Recognition in the Human Visual System building invariance: pooling and transformation untangling object representations Roadmap 23
4 The Camera : Retinal Projection Mammalian Visual System anatomy and processing pathways 34
5 Neuron Mammalian Visual System anatomy and processing pathways 35
6 figure courtesy of A. Alahi Mammalian Visual System anatomy and processing pathways 36
7 From Retina to Cortex world retina (compression) LGN V1 visual cortex (expansion) Mammalian Visual System anatomy and processing pathways 7
8 From Retina to Cortex size of! representation world retina (compression) LGN V1 visual cortex (expansion) Mammalian Visual System anatomy and processing pathways 7
9 Cortex: At Least Two Dozen Visual Areas plus more: motion areas, functional regions, etc. IT IT Weiner & Grill-Spector (2012) Mammalian Visual System anatomy and processing pathways 8
10 Visual Processing vs. Perception perception involves integration and higher functions IT IT Weiner & Grill-Spector (2012) Mammalian Visual System from processing to perception 9
11 Mammalian Visual System visual processing is done in stages but it is not synonymous with perception DiCarlo & Cox (2007) Mammalian Visual System key points 10
12 2. Early Computation local information processing Early Computation 11 3
13 Retinal Projection picture is inverted, but spatial relationships are preserved Early Computation first stages of processing 12 3
14 Receptive Fields extract similar features at each position in the visual field receptive fields figure adapted from Ebner & Hameroff (2011) Early Computation first stages of processing 13 3
15 Receptive Fields extract similar features at each position in the visual field ganglion cells center-surround receptive fields figure adapted from Ebner & Hameroff (2011) Early Computation first stages of processing 14 3
16 Receptive Fields extract similar features at each position in the visual field ON ganglion cells Early Computation first stages of processing 15 3
17 Receptive Fields extract similar features at each position in the visual field ON ganglion cells Early Computation first stages of processing 15 3
18 DoG Threshold figure courtesy of A. Alahi Early Computation first stages of processing 16 3
19 From Retina to Cortex world retina (compression) LGN V1 visual cortex (expansion) Early Computation first stages of processing 17 3
20 Single Electrode Recording Early Computation first stages of processing 18 3
21 Single Electrode Recording Hubel & Wiesel (1962) Early Computation first stages of processing 19 3
22 Types of V1 cells simple cells Hubel & Wiesel (1962) Early Computation first stages of processing 20
23 Types of V1 cells simple cells Hubel & Wiesel (1962) Early Computation first stages of processing 21
24 Types of V1 cells simple cells Hubel & Wiesel (1962) Early Computation first stages of processing 21
25 Types of V1 cells complex cells Hubel & Wiesel (1962) Early Computation first stages of processing 22
26 Types of V1 cells simple and complex cells are sensitive to: center-surround (difference of gaussians!) edges (symmetrical and asymmetrical) rectangles of various elongation, visual half fields V1 also has cells that are sensitive to: motion (in fact, it s a separate processing stream!) color (groups of cells called color blobs ) other stuff Hubel & Wiesel (1962) Early Computation first stages of processing 23
27 Why Are Edges Special? image = neurons * weights? 0? 0? 0? 1? 0? 0? 0? 0? 0? 0? 0? 1? 0? 2? 0? 0 Olshausen & Field (1996) Early Computation edges: a basis for natural images 24 3
28 Why Are Edges Special? image = neurons * weights Olshausen & Field (1996) Early Computation edges: a basis for natural images 24 3
29 Why Are Edges Special? each pixel activated independently INPUT BASIS FOUND Olshausen & Field (1996) Early Computation edges: a basis for natural images 25 3
30 Why Are Edges Special? images are sums of independent gratings INPUT BASIS FOUND Olshausen & Field (1996) Early Computation edges: a basis for natural images 26 3
31 Why Are Edges Special? patches of real-world images Olshausen & Field (1996) Early Computation edges: a basis for natural images 27 3
32 Early Computation V1 cells encode edge orientation and position across visual field: simple and complex receptive fields edges are a sufficient basis for real-world images! Hubel & Wiesel (1962), Olshausen & Field (1996) Early Computation key points 28 3
33 3. Object Recognition in the Human Visual System sequential transformations Object Recognition 29 3
34 Object Recognition building invariance 30 3
35 The Flow of Information IT IT Weiner & Grill-Spector (2012) Object Recognition building invariance 31 3
36 V1 Retina Van Essen (1991) Object Recognition building invariance 32 3
37 Specialization: What and Where Pathways monkey lesion studies lesion where pathway: difficulty in spatial reasoning lesion what pathway: difficulty in object recognition Mishkin & Ungerleider 1982 Object Recognition building invariance 33 3
38 Specialization: What and Where Pathways monkey lesion studies lesion where pathway: difficulty in spatial reasoning lesion what pathway: difficulty in object recognition Mishkin & Ungerleider 1982 Object Recognition building invariance 33 3
39 Object Recognition: The What Pathway DiCarlo & Cox (2007) Object Recognition building invariance 34 3
40 Object Recognition: The What Pathway V1 IT Freeman & Simoncelli (2011), Tanaka (1997) Object Recognition building invariance 35 3
41 Object as Manifolds in High Dimensional Space DiCarlo & Cox (2007) Object Recognition untangling object representation 36 3
42 Untangling Object Manifolds DiCarlo & Cox (2007) Object Recognition untangling object representation 37 3
43 Object Recognition: A Step Towards Visual Understanding in the human visual system, invariance is built gradually across many successive transformations human and computer vision both strive to achieve invariant representations Freeman & Simoncelli (2011), DiCarlo & Cox (2007) Object Recognition key points 38 3
44 Discussion human visual invariance vs. CV features incremental invariance vs. all-at-once should we build detectors invariant to everything?
45 Mammalian Visual System visual processing is done in stages but it is not synonymous with perception DiCarlo & Cox (2007) Conclusion #1 40
46 Early Computation V1 cells encode edge orientation and position across visual field: simple and complex receptive fields edges are a sufficient basis for real-world images! Hubel & Wiesel (1962), Olshausen & Field (1996) Conclusion #2 41 3
47 Object Recognition: A Step Towards Visual Understanding in the human visual system, invariance is built gradually across many successive transformations human and computer vision both strive to achieve invariant representations Freeman & Simoncelli (2011), DiCarlo & Cox (2007) Conclusion #3 42 3
48 Mid-Quarter Feedback Forms
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