Fcv cross hertzmann
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This document discusses the cross-fertilization of computer graphics and computer vision. It provides examples of how ideas from vision have been applied successfully in graphics areas like image and video manipulation, non-photorealistic rendering, and modeling human motion. The document argues that vision and graphics will be fundamental to scientific study of art, and that graphics builds useful models of the visual world that vision can learn from.
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The HARVEST Programme evaluates feature detectors and descriptors through indirect and direct benchmarks. Indirect benchmarks measure repeatability and matching scores on the affine covariant testbed to evaluate how features persist across transformations. Direct benchmarks evaluate features on image retrieval tasks using the Oxford 5k dataset to measure real-world performance. VLBenchmarks provides software for easily running these benchmarks and reproducing published results. It allows comparing features and selecting the best for a given application.
Modern features-part-3-software
This document summarizes VLFeat, an open source computer vision library. It provides concise summaries of VLFeat's features, including SIFT, MSER, and other covariant detectors. It also compares VLFeat's performance to other libraries like OpenCV. The document highlights how VLFeat achieves state-of-the-art results in tasks like feature detection, description and matching while maintaining a simple MATLAB interface.
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This document summarizes and compares local image descriptors. It begins with an introduction to modern descriptors like SIFT, SURF and DAISY. It then discusses efficient descriptors such as binary descriptors like BRIEF, ORB and BRISK which use comparisons of intensity value pairs. The document concludes with an overview section.
Modern features-part-1-detectors
This document discusses various feature detectors used in computer vision. It begins by describing classic detectors such as the Harris detector and Hessian detector that search scale space to find distinguished locations. It then discusses detecting features at multiple scales using the Laplacian of Gaussian and determinant of Hessian. The document also covers affine covariant detectors such as maximally stable extremal regions and affine shape adaptation. It discusses approaches for speeding up detection using approximations like those in SURF and learning to emulate detectors. Finally, it outlines new developments in feature detection.
Modern features-part-0-intro
The document discusses modern feature detection techniques. It provides an introduction and agenda for a talk on advances in feature detectors and descriptors, including improvements since a 2005 paper. It also discusses software suites and benchmarks for feature detection. Several application domains are described, such as wide baseline matching, panoramic image stitching, 3D reconstruction, image search, location recognition, and object tracking.
Lecture 02 internet video search
System 1 and System 2 were basic early systems for image matching that used color and texture matching. Descriptor-based approaches like SIFT provided more invariance but not perfect invariance. Patch descriptors like SIFT were improved by making them more invariant to lighting changes like color and illumination shifts. The best performance came from combining descriptors with color invariance. Representing images as histograms of visual word occurrences captured patterns in local image patches and allowed measuring similarity between images. Large vocabularies of visual words provided more discriminative power but were costly to compute and store.
Lecture 01 internet video search
This document summarizes a research paper on internet video search. It discusses several key challenges: [1] the large variation in how the same thing can appear in images/videos due to lighting, viewpoint etc., [2] defining what defines different objects, and [3] the huge number of different things that exist. It also notes gaps in narrative understanding, shared concepts between humans and machines, and addressing diverse query contexts. The document advocates developing powerful yet simple visual features that capture uniqueness with invariance to irrelevant changes.
Lecture 03 internet video search
The document discusses computer vision techniques for object detection and localization. It describes methods like selective search that group image regions hierarchically to propose object locations. Large datasets like ImageNet and LabelMe that provide training examples are also discussed. Performance on object detection benchmarks like PASCAL VOC is shown to improve significantly over time. Evaluation standards for concept detection like those used in TRECVID are presented. The document concludes that results are impressively improving each year but that the number of detectable concepts remains limited. It also discusses making feature extraction more efficient using techniques like SURF that take advantage of integral images.
Icml2012 tutorial representation_learning
This document provides an outline and overview of Yoshua Bengio's 2012 tutorial on representation learning. The key points covered include:
1) The tutorial will cover motivations for representation learning, algorithms such as probabilistic models and auto-encoders, and analysis and practical issues.
2) Representation learning aims to automatically learn good representations of data rather than relying on handcrafted features. Learning representations can help address challenges like exploiting unlabeled data and the curse of dimensionality.
3) Deep learning algorithms attempt to learn multiple levels of increasingly complex representations, with the goal of developing more abstract, disentangled representations that generalize beyond local patterns in the data.
Advances in discrete energy minimisation for computer vision
This document discusses string algorithms and data structures. It introduces the Knuth-Morris-Pratt algorithm for finding patterns in strings in O(n+m) time where n is the length of the text and m is the length of the pattern. It also discusses common string data structures like tries, suffix trees, and suffix arrays. Suffix trees and suffix arrays store all suffixes of a string and support efficient pattern matching and other string operations in linear time or O(m+logn) time where m is the pattern length and n is the text length.
Gephi tutorial: quick start
This document provides a tutorial on how to use Gephi software to analyze and visualize network graphs. It outlines the basic steps of importing a sample graph file, applying layout algorithms to organize the nodes, calculating metrics, detecting communities, filtering the graph, and exporting/saving the results. The tutorial demonstrates features of Gephi including node ranking, partitioning, and interactive visualization of the graph.
EM algorithm and its application in probabilistic latent semantic analysis
The document discusses the EM algorithm and its application in Probabilistic Latent Semantic Analysis (pLSA). It begins by introducing the parameter estimation problem and comparing frequentist and Bayesian approaches. It then describes the EM algorithm, which iteratively computes lower bounds to the log-likelihood function. Finally, it applies the EM algorithm to pLSA by modeling documents and words as arising from a mixture of latent topics.
Object recognition with pictorial structures
This document describes an efficient framework for part-based object recognition using pictorial structures. The framework represents objects as graphs of parts with spatial relationships. It finds the optimal configuration of parts through global minimization using distance transforms, allowing fast computation despite modeling complex spatial relationships between parts. This enables soft detection to handle partial occlusion without early decisions about part locations.
Iccv2011 learning spatiotemporal graphs of human activities
The document presents a new approach for learning spatiotemporal graphs of human activities from weakly supervised video data. The approach uses 2D+t tubes as mid-level features to represent activities as segmentation graphs, with nodes describing tubes and edges describing various relations. A probabilistic graph mixture model is used to model activities, and learning estimates the model parameters and permutation matrices using a structural EM algorithm. The learned models allow recognizing and segmenting activities in new videos through robust least squares inference. Evaluation on benchmark datasets demonstrates the ability to learn characteristic parts of activities and recognize them under weak supervision.
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Brunelli 2008: template matching techniques in computer vision
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Fcv cross hertzmann
- 1. Cross-fertilization of graphics and vision Aaron Hertzmann University of Toronto
- 3. Image and video manipulation C. Barnes, E. Shechtman, A. Finkelstein, D. B. Goldman, “PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing, ACM Trans. Graph (SIGGRAPH) 2009
- 5. Special effects Stargate Studios (www.stargate lms.com)
- 6. Special effects Stargate Studios (www.stargate lms.com)
- 7. Look Effects
- 8. coming next year: Computer Vision for Visual Effects Richard J. Radke Cambridge University Press, Fall 2012
- 9. Generalization of vision ideas
- 10. Geometry signal processing Taubin, SIGGRAPH 1995 Jones, Durand, Desbrun, SIGGRAPH 2003
- 11. Geometry processing E. Kalogerakis, A. Hertzmann, K. Singh, "Learning 3D Mesh Segmentation and Labeling", ACM Trans. Graphics / SIGGRAPH 2011 A. M. Bronstein, M. M. Bronstein, M. Ovsjanikov, L. J. Guibas, "Shape Google: geometric words and expressions for invariant shape retrieval", ACM Trans. Graphics (TOG), 2011.
- 12. Non-photorealistic rendering (demo)
- 13. Non-photorealistic rendering Lambertian rendering Contours and Suggestive Contours [DeCarlo et al. 2003]
- 14. “Bone” by Jeff Smith © 2007 Our result
- 15. “Bone” by Jeff Smith © 2007 Our result
- 16. “Bone” by Jeff Smith © 2007 Our result
- 17. The Science of Art Claim: Vision and graphics will be fundamental to the scientific study of art See: A. Hertzmann, “Non-Photorealistic Rendering and The Science of Art,” NPAR 2010
- 18. Fei-Fei et al. 2007
- 19. Why vision should continue to look to graphics: Graphics builds models of precisely the “visual world”
- 20. Scene models Chaudhuri et al. SIGGRAPH 2011 Yu et al. SIGGRAPH 2011
- 21. Google 3D Warehouse M. Fisher, M. Savva, P. Hanrahan. “Characterizing Structural Relationships in Scenes Using Graph Kernels,” SIGGRAPH 2011
- 22. Human motion K. Grochow, S. L. Martin, A. Hertzmann, Z. Popović. “Style-Based Inverse Kinematics” ACM Trans. Graphics / SIGGRAPH 2004. R. Urtasun, D. J. Fleet, A. Hertzmann, P. Fua. “Priors for People Tracking from Small Training Sets” ICCV 2005
- 23. Claim: Understanding people in motion requires physical models
- 24. Physics-Based Characters Features Contact COM AM Posture M. de Lasa, I. Mordatch, A. Hertzmann, “Feature-Based Locomotion Controllers,” SIGGRAPH 2010
- 25. M. de Lasa, I. Mordatch, A. Hertzmann, “Feature-Based Locomotion Controllers,” SIGGRAPH 2010
- 26. M. de Lasa, I. Mordatch, A. Hertzmann, “Feature-Based Locomotion Controllers,” SIGGRAPH 2010
- 27. M. de Lasa, I. Mordatch, A. Hertzmann, “Feature-Based Locomotion Controllers,” SIGGRAPH 2010
- 29. M. Brubaker, D. J. Fleet, A. Hertzmann, “Physics-Based Person Tracking Using the Anthropomorphic Walker” CVPR 2007, IJCV 2010
- 30. Summary Many successes of vision ideas in graphics Vision and graphics are key to the science of art Vision should look to graphics for models of the visual world