sliding_windows - 1/29/2009 1 Sliding window detection...

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Unformatted text preview: 1/29/2009 1 Sliding window detection January 29, 2009 Kristen Grauman UT-Austin Schedule http://www.cs.utexas.edu/~grauman/cours es/spring2009/schedule.htm http://www.cs.utexas.edu/~grauman/cours es/spring2009/papers.htm 1/29/2009 2 Plan for today Lecture Sliding window detection Contrast-based representations Face and pedestrian detection via sliding window classification Papers: HoG and Viola-Jones Demo Viola-Jones detection algorithm Tasks Detection: Find an object (or instance of object category) in the image category) in the image. Recognition: Name the particular object (or category) for a given image/subimage. How is the object (class) going to be modeled or learned? Given a new image, how to make a decision? 1/29/2009 3 Earlier: Knowledge-rich models for objects Irving Biederman, Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review, 1987. Earlier: Knowledge-rich models for objects Alan L. Yuille, David S. Cohen, Peter W. Hallinan. Feature extraction from faces using deformable templates,1989. 1/29/2009 4 Later: Statistical models of appearance Objects as appearance patches E.g., a list of pixel intensities Learning patterns directly from image features Learning patterns directly from image features Eigenfaces (Turk & Pentland, 1991) Later: Statistical models of appearance Objects as appearance patches E.g., a list of pixel intensities Learning patterns directly from image features Learning patterns directly from image features Eigenfaces (Turk & Pentland, 1991) 1/29/2009 5 For what kinds of recognition tasks is a holistic description of appearance suitable? Appearance-based descriptions Appropriate for classes with more rigid structure, and when good training examples available 1/29/2009 6 Appearance-based descriptions Scene recognition based on global texture pattern. [Oliva & Torralba (2001)] What if the object of interest may be embedded in clutter? 1/29/2009 7 Sliding window object detection Car/non-car Classifier Yes, car. No, not a car. Sliding window object detection If object may be in a cluttered scene, slide a window around looking for it. Car/non-car Classifier 1/29/2009 8 Detection via classification Consider all subwindows in an image Sample at multiple scales and positions Sample at multiple scales and positions Make a decision per window: Does this contain object category X or not? Detection via classification Fleshing out this pipeline a bit more, we need to: Training examples 1. Obtain training data 2. Define features 3. Define classifier Car/non-car Classifier Feature extraction 1/29/2009 9 Detector evaluation When do we have a correct detection?...
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sliding_windows - 1/29/2009 1 Sliding window detection...

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