Vision is inferential. (
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- Joella Price
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1 Announcements Final: Thursday, December 15, 8am, here. Review Session, Wednesday, Dec 14, 1pm, AV Williams Review sheet with practice problems on-line.
2 Hints for Final Focus on core techniques/ideas: convolution, gradients, statistical modeling, 3D geometry, motion matrices and optimization methods we studied, and their use in vision. Of course, other topics from course may show up.
3 Vision is inferential (
4 Boundary Detection Images don t come color-coded with boundaries. Filtering to combine evidence. Define discontinuities in 2D. Snakes to combine evidence further. Good continuation, comes from knowledge of shape. Texture understanding as modeling random processes. Boundary detection involves combination of different kinds of knowledge.
5 Modeling + Algorithms Build a simple model of the world (eg., flat, uniform intensity). Find provably good algorithms. Experiment on real world. Update model. Problem: Too often models are simplistic or intractable.
6 Stereo Modeling: Geometry, Photometry. Perspective camera, known pose allows reconstruction. Epipolar Constraint. Ordering constraint Match similar intensities. Disparities similar in nearby pixels. Algorithms Straightforward Reconstruction. Shortest path, Graph Algorithms.
7 Where is Computer Vision Going? More Data, Faster Machines => More Interaction with Other Fields. Fundamental Problems Remain
8 Optimization Partly push of video meant bigger optimization problems. Early 90s, SVD, Gradient Descent, Filtering. More recently, Graph Algorithms, Particle Filtering, Mean-shift, Multi-grid. Techniques from theoretical CS, applied math, physics, learning,.
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10 State of the art method Boykov et al., Fast Approximate Energy Minimization via Graph Cuts, International Conference on Computer Vision, September (Seitz) Ground truth
11 (Comaniciu and Meer)
12 Learning Recognition using supervised learning. Given examples of an object Use classifiers: eg, SVMs, Winnow, Boosting. Grouping using unsupervised learning. Eg., E-M Probabilistic Modeling Eg., Graphical models, texture,.
13 Viola and Jones: Real time Face Detection
14 Viola and Jones: Real time Face Detection
15 Graphics Common interest in modeling reflection, light, 3D shape. Image-Based Rendering.
16 input depth image novel view Szeliski
17 Figure 1: 3D tracking software developed at Digital Domain was used on nearly every shot of the movie Titanic. (From Vision in Film and Special Effects Doug Roble, Digital Domain)
18 Figure 3: 2D tracking was used to apply digital makeup to Brad Pitt in Interview with a Vampire.
19 Image Editing: Snakes, Intelligent Scissors, Contour-based editing (Vision-Assisted Image Editing Eric N. Mortensen Brigham Young University)
20 Biomedical Engineering Segmentation Identify organs to measure them. Find tumors. Tracking Is a heart beating properly? Is there dead tissue? Registration/Matching. Positions of Tumors in Surgery.
21 Figures by kind permission of Eric Grimson; further information can be obtained from his web site
22 Figures by kind permission of Eric Grimson; further information can be obtained from his web site
23 Figures by kind permission of Eric Grimson; further information can be obtained from his web site
24 Figures by kind permission of Eric Grimson; further information can be obtained from his web site
25 Figures by kind permission of Eric Grimson; further information can be obtained from his web site
26 Big Data Sets Image Data Bases Kodak, commercial data bases w/ tens of thousands of images. Internet, with millions? Satellite imagery (Petabytes). Monitoring effects of climate change. Custom Data Sets
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28 Vision at Maryland
29 Background Subtraction
30 Tracking and Activity Classification
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37 Smart Thumbnails
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39 Many Big Problems Not Answered Matching when brightness changes. Interaction between motion and lighting Single-frame segmentation and multiple images. Representing 3D shape, and its appearance in 2D images..
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