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IEEE PATTERN ANALYSIS AND MACHINE INTELLIGENCE  - FINAL YEAR IEEE COMPUTER SCIENCE PROJECTS
 
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TSYS Center for Research and Development (TCRD) is a premier center for academic and industrial research needs. We at TRCD provide complete support for final year Post graduate Student (M.E / M.Tech / M. Sc/ MCA/ M-phil) who are doing course in computer science and Information technology to do their final year project and journal work. For Latest IEEE PATTERN ANALYSIS AND MACHINE INTELLIGENCE Projects Contact: TSYS Center for Research and Development (TSYS Academic Projects) Ph.No: 9841103123 / 044-42607879, Visit us: http://www.tsys.co.in/ Email: [email protected] IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2016 TOPICS • Surface Regions of Interest for Viewpoint Selection • Parametric Regression on the Grassmannian • Bayesian Non-parametric clustering of ranking data • An Accurate and Robust Artificial Marker based on Cyclic Codes • Comments on the "Kinship Face in the Wild" Data Sets • Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications • A model selection approach for clustering a multinomial sequence with non-negative factorization • Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis • Person Re-Identification by Discriminative Selection in Video Ranking • Depth Estimation with Occlusion Modeling Using Light-field Cameras • Human Pose Estimation from Video and IMUs • EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis • A Novel Performance Evaluation Methodology for Single-Target Trackers • Fast Rotation Search with Stereographic Projections for 3D Registration • EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis • Spatio-temporal Matching for Human Pose Estimation in Video • Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes • Discriminative Bayesian Dictionary Learning for Classification • Discriminative and Efficient Label Propagation on Complementary Graphs for Multi-Object Tracking • Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning • An Efficient Joint Formulation for Bayesian Face Verification • Minimum Entropy Rate Simplification of Stochastic Processes • Shape Descriptions of Nonlinear Dynamical Systems for Video-based Inference • Dynamic Scene Recognition with Complementary Spatiotemporal Features • Histogram of Oriented Principal Components for Cross-View Action Recognition • Higher-order Graph Principles towards Non-rigid Surface Registration • Discriminative Bayesian Dictionary Learning for Classification • A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs • Selective Transfer Machine for Personalized Facial Expression Analysis • Unsupervised spectral mesh segmentation driven by heterogeneous graphs • On the Equivalence of the LC-KSVD and the D-KSVD Algorithms • Adaptive Visual Tracking with Minimum Uncertainty Gap Estimation • Multi-timescale Collaborative Tracking • Feature Selection with Annealing for Computer Vision and Big Data Learning • Hierarchical Clustering Multi-task Learning for Joint Human Action Grouping and Recognition • Super Normal Vector for Human Activity Recognition with Depth Cameras • Active Clustering with Model-Based Uncertainty Reduction • Expanded Parts Model for Semantic Description of Humans in Still Images • Higher-order Occurrence Pooling for Bags-of-Words: Visual Concept Detection
How to Prepare Research Paper for Publication in MS Word (Easy)
 
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How to Setup Research Paper for Publication in MS Word... Facebook Page : https://www.facebook.com/MeMJTube Follow on twitter: https://twitter.com/mj1111983 Website : http://www.bsocialshine.com IJERT, IEEE, IJSER, ,National Journal of System and Information Technology,Journal of Network and Information Security,Journal of IMS Group,Journal of Scientific and Technical Research,KIIT Journal of Library and Information Management,KIMI Hospitality Research Journal,Global Journal of Research in Management,Journal of Commerce and Accounting Research,Accounts of Chemical Research,Angewandte Chemie,Chemistry - A European Journal,Chemistry Letters,Helvetica Chimica Acta,Journal of the American Chemical Society,ACS Nano,Advanced Functional Materials,Advanced Materials,Annual Review of Condensed Matter Physics,Journal of Materials Chemistry ,Nano Letters,Annual Review of Fluid Mechanics,Archive for Rational Mechanics and Analysis,Acta Crystallographica – parts A, B,Advances in Physics,American Journal of Physics,Annalen der Physik,Applied Physics Letters,Journal of Physics – parts A–D, G,Nature Physics,New Journal of Physics,Reports on Progress in Physics,International Journal of Biological Sciences,Journal of Cell Biology,Journal of Molecular Biology,Journal of Theoretical Biology,Journal of Virology,PLOS Biology,European Journal of Biochemistry,FEBS Journal,Journal of Biological Chemistry,Journal of Molecular Biology,American Journal of Botany,Annals of Botany,Aquatic Botany,International Journal of Plant Sciences,New Phytologist,Genes, Brain and Behavior,Journal of Neurochemistry,Journal of Neurophysiology,Journal of Neuroscience,Nature Neuroscience,Archivos de Medicina Veterinaria,Journal of Veterinary Science,Veterinary Record,Artificial Intelligence,Communications of the ACM,Computer,IEEE Transactions on Pattern Analysis and Machine Intelligence,IEEE Transactions on Computers,IEEE Transactions on Evolutionary Computation,IEEE Transactions on Fuzzy Systems,IEEE Transactions on Information Theory,IEEE Transactions on Neural Networks and Learning Systems,International Journal of Computer Vision,Journal of Artificial Intelligence Research,Journal of Cryptology,Journal of Functional Programming,Journal of Machine Learning Research,Journal of the ACM,SIAM Journal on Computing,Advances in Production Engineering & Management,Annual Review of Biomedical Engineering,Archive of Applied Mechanics,Biomedical Microdevices,Chemical Engineering Science,Coastal Engineering Journal,Electronics Letters,Experiments in Fluids,Green Chemistry,Industrial & Engineering Chemistry Research,International Journal of Functional Informatics and Personalized Medicine,Journal of Environmental Engineering,Journal of Fluid Mechanics,Journal of Hydrologic Engineering,Journal of the IEST,Measurement Science and Technology,NASA Tech Briefs,Acta Mathematica,Annals of Mathematics,Bulletin of the American Mathematical Society,Communications on Pure and Applied Mathematics,Duke Mathematical Journal,Inventiones Mathematicae,Journal of Algebra,Journal of the American Mathematical Society,Journal of Differential Geometry,Publications Mathématiques de l'IHÉS,Topology,Archives of Internal Medicine,British Medical Journal,Cardiovascular Diabetology,International Journal of Medical Sciences,Journal of the American Medical Association,Journal of Clinical Investigation,Journal of Experimental Medicine,The Lancet,Molecular Medicine,Nature Medicine,
Views: 30141 MJ Tube
CVFX Lecture 15: Stereo correspondence
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 15: Stereo correspondence (3/20/14) 0:00:01 Stereo correspondence 0:02:09 Disparity 0:04:43 Differences between stereo and optical flow 0:11:42 Basic stereo algorithms 0:12:04 Sum of absolute differences 0:14:27 Birchfield-Tomasi measure 0:16:31 Census transform 0:20:46 Dynamic programming for stereo 0:25:19 Non-monotonic correspondence 0:26:53 The Ohta-Kanade algorithm 0:29:31 Stereo algorithm benchmarking 0:36:21 Graph cuts for stereo 0:52:07 Belief propagation for stereo 0:56:02 Occlusions and discontinuities 0:59:53 Incorporating segmentation 1:06:50 Stereo rigs for filming Follows Section 5.5 of the textbook. http://cvfxbook.com Key references: D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1):7--42, Apr. 2002. http://dx.doi.org/10.1023/A:1014573219977 Y. Ohta and T. Kanade. Stereo by intra- and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2):139--54, Mar. 1985. http://dx.doi.org/10.1109/TPAMI.1985.4767639 Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222--39, Nov. 2001. http://dx.doi.org/10.1109/34.969114 J. Sun, N.-N. Zheng, and H.-Y. Shum. Stereo matching using belief propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(7):787--800, July 2003. http://dx.doi.org/10.1109/TPAMI.2003.1206509
Views: 30223 Rich Radke
CVFX Lecture 11: Feature evaluation and use
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 11: Feature evaluation and use (2/27/14) 0:00:15 Detector and descriptor combinations 0:02:49 Feature evaluation: repeatability 0:08:09 Feature evaluation: matchability 0:13:35 Color features 0:15:45 Artificial features (tags) 0:26:55 Artificial features (3D structures) 0:30:18 Features in TV and movies 0:33:33 Features in consumer electronics (e.g., smartphones) Follows Sections 4.3-4.5 of the textbook. http://cvfxbook.com Key references: K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine region detectors. International Journal of Computer Vision, 65(1):43--72, Nov. 2005. http://dx.doi.org/10.1007/s11263-005-3848-x K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10):1615--30, Oct. 2005. http://dx.doi.org/10.1109/TPAMI.2005.188 M. Fiala. Designing highly reliable fiducial markers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7):1317--24, July 2010. http://dx.doi.org/10.1109/TPAMI.2009.146 See also: http://gizmodo.com/amazon-flow-is-the-wonderful-future-of-shopping-from-yo-1516828089 https://www.google.com/atap/projecttango/
Views: 2398 Rich Radke
The Deep End of Deep Learning | Hugo Larochelle | TEDxBoston
 
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Artificial Neural Networks are inspired by some of the "computations" that occur in human brains—real neural networks. In the past 10 years, much progress has been made with Artificial Neural Networks and Deep Learning due to accelerated computer power (GPUs), Open Source coding libraries that are being leveraged, and in-the-moment debates and corroborations via social media. Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning. Hugo Larochelle is a Research Scientist at Twitter and an Assistant Professor at the Université de Sherbrooke (UdeS). Before 2011, he spent two years in the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at Université de Montréal, under the supervision of Yoshua Bengio. He is the recipient of two Google Faculty Awards. His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 170198 TEDx Talks
pattern analysis and machine intelligence
 
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Views: 13 Ranjith Kumar
Coded Strobing Photography: Compressive Sensing of High Speed Periodic Videos
 
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Veeraraghavan, A., Reddy, D., Raskar, R. (2011) IEEE Transactions on Pattern Analysis and Machine Intelligence. Paper http://www.umiacs.umd.edu/~dikpal/Projects/codedstrobing.html We show that, via temporal modulation, one can observe and capture a high-speed periodic video well beyond the abilities of a low-frame-rate camera. By strobing the exposure with unique sequences within the integration time of each frame, we take coded projections of dynamic events. From a sequence of such frames, we reconstruct a high-speed video of the high-frequency periodic process. Strobing is used in entertainment, medical imaging, and industrial inspection to generate lower beat frequencies. But this is limited to scenes with a detectable single dominant frequency and requires high-intensity lighting. In this paper, we address the problem of sub-Nyquist sampling of periodic signals and show designs to capture and reconstruct such signals. The key result is that for such signals, the Nyquist rate constraint can be imposed on the strobe rate rather than the sensor rate. The technique is based on intentional aliasing of the frequency components of the periodic signal while the reconstruction algorithm exploits recent advances in sparse representations and compressive sensing. We exploit the sparsity of periodic signals in the Fourier domain to develop reconstruction algorithms that are inspired by compressive sensing.
Views: 6587 cameraculturegroup
www.giovannigualdi.com || Multi-Stage Particle Windows for Fast and Accurate Object Detection
 
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G. Gualdi, A. Prati, R. Cucchiara, "Multi-Stage Particle Windows for Fast and Accurate Object Detection" (to be published soon) IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Views: 275 Giovanni Gualdi
Biometrics - Technology for Human Recognition - Presented by Anil K. Jain, Ph.D.
 
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Anil K. Jain, Ph.D. recently spoke at a Noblis Technology Tuesdays Special Presentation entitled, "Biometrics: Technology for Human Recognition". Dr. Jain is a university distinguished professor in the Department of Computer Science and Engineering at Michigan State University. His research interests include pattern recognition, biometric authentication and computer vision. He served as the editor-in-chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (1991-1994). The holder of eight patents in the area of fingerprint matching and face recognition, he is the author of a number of books, including Introduction to Biometrics (2011), Handbook of Face Recognition (2011), Handbook of Fingerprint Recognition (2009), Handbook of Biometrics (2009), Handbook of Multibiometrics (2006), BIOMETRICS: Personal Identification in Networked Society (1999), and Algorithms for Clustering Data (1988). The Noblis Technology Tuesday speaker series covers a broad spectrum of political, technical and innovative ideas. Noblis is a nonprofit science, technology, and strategy organization that brings the best of scientific thought, management, and engineering expertise with a reputation for independence and objectivity. The opinions expressed in this video are those of the speaker and do not necessarily reflect the views or opinions of Noblis. Noblis is a nonprofit science, technology, and strategy organization that brings the best of scientific thought, management, and engineering expertise in an environment of independence and objectivity. We are accomplished scientists, analysts, engineers, management experts, researchers, and technology specialists who work in areas that are essential to our nation's well being. Our work focuses on solving complex problems in national and homeland security, healthcare, transportation, enterprise engineering, and environmental sustainability. http://www.noblis.org Twitter - @noblisnews
Views: 6693 NoblisNetwork
PD2T : Person-specific Detection, Deformable Tracking
 
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This video accompanies the paper 'PD2T : Person-specific Detection, Deformable Tracking', IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). A copy of the paper can be found in http://ieeexplore.ieee.org/document/8094942/ This video demonstrates the adaptive deformable tracking of the proposed PD2T pipeline for various: i) initialisations (depicted in the middle column), ii) videos, iii) datasets.
Views: 108 Grigoris Chrysos
Semantic Segmentation (8 categories)
 
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Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun: Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, August 2013.
Views: 2707 Yann LeCun
[PAMI'17] Long-term Temporal Convolutions for Action Recognition
 
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Comparison between 100-frame and 16-frame optical flow input to a 3D convolutional neural network for learning action recognition on UCF101 dataset. Video for the paper: Gül Varol, Ivan Laptev and Cordelia Schmid, "Long-term Temporal Convolutions for Action Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence 2017. Available online at https://arxiv.org/abs/1604.04494 More at https://www.di.ens.fr/willow/research/ltc/
Views: 1427 Gül Varol Simsekli
RI Seminar: Greg Mori : Deep Structured Models for Human Activity Recognition
 
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Greg Mori Professor School of Computer Science, Simon Fraser University Friday, January 19, 2018 Abstract: Visual recognition involves reasoning about structured relations at multiple levels of detail. For example, human behaviour analysis requires a comprehensive labeling covering individual low-level actions to pair-wise interactions through to high-level events. Scene understanding can benefit from considering labels and their inter-relations. In this talk I will present recent work by our group building deep learning approaches capable of modeling these structures. I will present models for learning trajectory features that represent individual human actions, and hierarchical temporal models for group activity recognition. General purpose structured inference machines will be described, building from notions of message passing within graphical models. These will be used in models for inferring individual and group activity and modeling structured relations for image labeling problems. Bio: Greg Mori received the Ph.D. degree in Computer Science from the University of California, Berkeley in 2004. He received an Hon. B.Sc. in Computer Science and Mathematics with High Distinction from the University of Toronto in 1999. He spent one year (1997-1998) as an intern at Advanced Telecom munications Research (ATR) in Kyoto, Japan. He spent part of 2014-2015 as a Visiting Scientist at Google in Mountain View, CA. After graduating from Berkeley, he returned home to Vancouver and is currently a Professor and the Director of the School of Computing Science at Simon Fraser University. Dr. Mori’s research interests are in computer vision and machine learning. Dr. Mori has served on the organizing committees of the major computer vision conferences (CVPR, ECCV, ICCV). Dr. Mori is an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and an Editorial Board Member of the International Journal of Computer Vision (IJCV).
Views: 2852 cmurobotics
Semantic Segmentation (33 categories)
 
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Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun: Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, August 2013.
Views: 1370 Yann LeCun
Full Body Pose Tracking of Multiple Users
 
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Body pose tracking results of multiple users based on the model-based approach described in: Sigalas M., Pateraki M., Trahanias P., 2015. Full-body Pose Tracking – the Top View Reprojection Approach. IEEE Transaction on Pattern Analysis and Machine Intelligence doi: http://dx.doi.org/10.1109/TPAMI.2015.2502582 Abstract: Recent introduction of low-cost depth cameras triggered a number of interesting works, pushing forward the state-of-the-art in human body pose extraction and tracking. However, despite the remarkable progress, many of the contemporary methods cope inadequately with complex scenarios, involving multiple interacting users, under the presence of severe inter- and intra-occlusions. In this work, we present a model-based approach for markerless articulated full body pose extraction and tracking in RGB-D sequences. A cylinder-based model is employed to represent the human body. For each body part a set of hypotheses is generated and tracked over time by a Particle Filter. To evaluate each hypothesis, we employ a novel metric that considers the reprojected Top View of the corresponding body part. The latter, in conjunction with depth information, effectively copes with difficult and ambiguous cases, such as severe occlusions. For evaluation purposes, we conducted several series of experiments using data from a public human action database, as well as own-collected data involving varying number of interacting users. The performance of the proposed method has been further compared against that of the Microsoft’s Kinect SDK and NiTETM using ground truth information. The results obtained attest for the effectiveness of our approach.
CLM Local Detectors (MOSSE vs Linear SVM)
 
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Constrained Local Models: Local Detectors Evaluation Qualitative evaluation between the Minimum Output Sum of Squared Error (MOSSE) filter vs linear SVM built from aligned (positive) and misaligned (negative) examples. Bayesian Constrained Local Models Revisited Pedro Martins, João F. Henriques, R. Caseiro, Jorge Batista IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016
Views: 397 Pedro Martins
2018 HOLZINGER Machine Learning Research Topics
 
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Andreas Holzinger promotes a synergistic approach by integration of two areas to understand intelligence to realize context-adaptive systems: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD). Andreas has pioneered in interactive machine learning (iML) with the human-in-the-loop. Andreas Holzingers’ goal is to augment human intelligence with artificial intelligence to help to solve problems in health informatics. Due to raising legal and privacy issues in the European Union glass box AI approaches will become important in the future to be able to make decisions transparent, re-traceable, thus understandable. Andreas Holzingers’ aim is to explain why a machine decision has been made, paving the way towards explainable AI. 00:34 [1] June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo & Namkug Kim 2017. Deep learning in medical imaging: general overview. Korean journal of radiology, 18, (4), 570-584, doi:10.3348/kjr.2017.18.4.570. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5447633/ 01:26 [2] Andreas Holzinger 2017. Introduction to Machine Learning and Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1, (1), 1-20, doi:10.3390/make1010001. https://www.mdpi.com/2504-4990/1/1/1 03:21 [3] Andreas Holzinger 2013. Human–Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, Alfredo, Kittl, Christian, Simos, Dimitris E., Weippl, Edgar & Xu, Lida (eds.) Multidisciplinary Research and Practice for Information Systems, Springer Lecture Notes in Computer Science LNCS 8127. Heidelberg, Berlin, New York: Springer, pp. 319-328, doi:10.1007/978-3-642-40511-2_22. https://link.springer.com/chapter/10.1007/978-3-642-40511-2_22 04:00 [4] Andreas Holzinger & Klaus-Martin Simonic (eds.) 2011. Information Quality in e-Health. Lecture Notes in Computer Science LNCS 7058, Heidelberg, Berlin, New York: Springer, doi:10.1007/978-3-642-25364-5. https://www.springer.com/de/book/9783642253638 04:26 [5] Andreas Holzinger, Matthias Dehmer & Igor Jurisica 2014. Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions. Springer/Nature BMC Bioinformatics, 15, (S6), I1, doi:10.1186/1471-2105-15-S6-I1. https://www.ncbi.nlm.nih.gov/pubmed/25078282 04:40 [6] Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams & Nando De Freitas 2016. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104, (1), 148-175, doi:10.1109/JPROC.2015.2494218. https://www.semanticscholar.org/paper/Taking-the-Human-Out-of-the-Loop%3A-A-Review-of-Shahriari-Swersky/5ba6dcdbf846abb56bf9c8a060d98875ae70dbc8 05:10 [7a] Quoc V. Le, Marc'aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean & Andrew Y. Ng 2011. Building high-level features using large scale unsupervised learning. arXiv:1112.6209.05:16 https://arxiv.org/abs/1112.6209 [7b] Quoc V. Le. Building high-level features using large scale unsupervised learning. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013. IEEE, 8595-8598, doi:10.1109/ICASSP.2013.6639343. https://ieeexplore.ieee.org/abstract/document/6639343 05:24 [8] Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, (7639), 115-118, doi:10.1038/nature21056. https://cs.stanford.edu/people/esteva/nature [9] Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In: Pereira, Fernando, Burges, Christopher .J.C., Bottou, Leon & Weinberger, Kilian Q., eds. Advances in neural information processing systems (NIPS 2012), 2012 Lake Tahoe. NIPS, 1097-1105. https://github.com/abhshkdz/papers/blob/master/reviews/imagenet-classification-with-deep-convolutional-neural-networks.md 06:15 [10] Randy Goebel, Ajay Chander, Katharina Holzinger, Freddy Lecue, Zeynep Akata, Simone Stumpf, Peter Kieseberg & Andreas Holzinger. Explainable AI: the new 42? Springer Lecture Notes in Computer Science LNCS 11015, 2018 Cham. Springer, 295-303, doi:10.1007/978-3-319-99740-7_21. https://link.springer.com/chapter/10.1007/978-3-319-99740-7_21 06:45 [11] Zhangzhang Si & Song-Chun Zhu 2013. Learning and-or templates for object recognition and detection. IEEE transactions on pattern analysis and machine intelligence, 35, (9), 2189-2205, doi:10.1109/TPAMI.2013.35. https://ieeexplore.ieee.org/document/6425379 About the concept of the human-in-the-loop: [1] Andreas Holzinger 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131, doi:10.1007/s40708-016-0042-6. https://link.springer.com/article/10.1007/s40708-016-0042-6 https://hci-kdd.org http://www.aholzinger.at
Views: 820 Andreas Holzinger
Two users pose recovery illustrative video
 
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Two users pose recovery. Illustrative video from: M. Sigalas, M. Pateraki, and P. Trahanias, “Full-body pose tracking - the Top View Reprojection approach”, in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. PP, no. 99, 2015. doi: http://dx.doi.org/10.1109/TPAMI.2015.2502582 ground truth data: http://www.ics.forth.gr/cvrl/fbody/
Views: 8 Markos Sigalas
CVFX Lecture 25: Multiview stereo
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 25: Multiview stereo (4/28/14) 0:00:01 Multiview stereo introduction 0:07:50 Multiview stereo benchmarking 0:10:20 Volumetric methods 0:17:30 Surface deformation methods 0:23:57 Surface-based reprojection 0:28:17 Patch-based methods 0:35:51 Patch-based reconstruction videos 0:38:33 Patch-based MVS software 0:44:45 MVS on smartphones and PCs 0:48:41 MVS in L.A. Noire 0:51:20 Artificial lens blur from MVS Follows Section 8.3 of the textbook. http://cvfxbook.com Key references: Y. Furukawa and J. Ponce. Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8):1362--76, Aug. 2010. http://dx.doi.org/10.1109/TPAMI.2009.161 S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2006. http://dx.doi.org/10.1109/CVPR.2006.19 C. Strecha, W. von Hansen, L. Van Gool, P. Fua, and U. Thoennessen. On benchmarking camera calibration and multi-view stereo for high resolution imagery. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008. http://dx.doi.org/10.1109/CVPR.2008.4587706 K. N. Kutulakos and S. M. Seitz. A theory of shape by space carving. International Journal of Computer Vision, 38(3):199--218, July 2000. http://dx.doi.org/10.1023/A:1008191222954 J.-P. Pons, R. Keriven, and O. Faugeras. Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score. International Journal of Computer Vision, 72(2):179--93, June 2007. http://dx.doi.org/10.1007/s11263-006-8671-5 M. Goesele, B. Curless, and S. Seitz. Multi-view stereo revisited. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2006. http://dx.doi.org/10.1109/CVPR.2006.199
Views: 6890 Rich Radke
This AI Knows Who You Are by The Way You Walk
 
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Our individual walking styles, much like snowflakes, are unique. With this in mind, computer scientists have developed a powerful new footstep-recognition system using AI, and it could theoretically replace retinal scanners and fingerprinting at security checkpoints, including airports. Neural networks can find telltale patterns in a person’s gait that can be used to recognize and identify them with almost perfect accuracy, according to new research published in IEEE Transactions on Pattern Analysis and Machine Intelligence. The new system, called SfootBD, is nearly 380 times more accurate than previous methods, and it doesn’t require a person to go barefoot in order to work. It’s less invasive than other behavioral biometric verification systems, such as retinal scanners or fingerprinting, but its passive nature could make it a bigger privacy concern, since it could be used covertly. Learn More: https://gizmodo.com/this-ai-knows-who-you-are-by-the-way-you-walk-1826368997 Your Support of Independent Media Is Appreciated: https://www.paypal.me/dahboo7 Bitcoin- 1Nmcbook8TwAdtZHsMdVxRtjBnyrSArDH5 Bitcoin Cash- qzjvcvkfhzffcgc89mcnvuka0lljjuu4dvalrafmj0 www.undergroundworldnews.com https://www.minds.com/DAHBOO7 My Other Youtube Channel- https://www.youtube.com/Dahboo777 https://twitter.com/dahboo7 https://vid.me/DAHBOO7 https://www.facebook.com/DAHBOO7 https://www.instagram.com/dahboo7/
Views: 6410 DAHBOO777
ModDrop: adaptive multi-modal gesture recognition
 
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A video showing gesture recognition and localization from our PAMI paper (involving INSA-Lyon/LIRIS, University of Guelph, Awabot). Natalia Neverova, Christian Wolf, Graham W. Taylor and Florian Nebout. ModDrop: adaptive multi-modal gesture recognition. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. Also available on arxiv: http://arxiv.org/abs/1501.00102 This work was funded by the Interabot project (Call "Investissement d'Avenirs").
Views: 427 Christian Wolf
A.I. vs. Pathologists: Survival of the Fittest | Sahir Ali | TEDxSugarLand
 
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Artificial Intelligence and it’s promise in predicting cancer outcome: every patient deserves their own equation. Dr. Sahirzeeshan Ali is a research scientist at the Center for Computation Imaging and Personalized Medicine (CCIPD) at Case Western Reserve Medical University and Seidman Cancer Center. Dr. Ali received a bachelor’s and master’s degrees in Electrical and Computer Engineering from Rutgers University (2009 & 2011) and a Ph.D in Biomedical Engineering from Case Western Reserve University. He also was the recipient of a Prostate Cancer Research Grant from the Department of Defense in 2014. Dr. Ali’s research interest lies in developing image analysis, statistical pattern recognition, machine learning and artificial intelligence tools to computationally interrogate biomedical image data of digital pathology tissue images. The tools can be used to predict disease progression and provide a score to clinicians on the aggressiveness of a patient’s disease, such as breast cancer and prostate cancer, which can in turn help physicians decide on appropriate treatment option. Dr. Ali has written more than 30 peer-reviewed journal, conference and abstract publications, appearing in journals such as Nature Scientific Reports, American Journal of Surgical Pathology, the Annual Review of Biomedical Engineering, Medical Image Analysis, IEEE Transactions on Medical Imaging. This research work has also culminated in various commercialized patents. In addition, Dr. Ali has consulted with hedge funds and fortune 100 companies as a Salesforce architect and machine learning expert. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 5995 TEDx Talks
Light field reconstruction (main video)
 
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Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2019
Views: 17 Gaochang Wu
RI Seminar: Deva Ramanan : Recognizing objects using model-based statistics
 
01:16:38
Deva Ramanan Associate Professor, Department of Computer Science, University of California at Irvine May 03, 2013 Recognizing objects using model-based statistics Abstract Object recognition is a central task in computer vision. It is difficult because objects can vary greatly in appearance. Classic approaches tended to focus on geometric models inspired by computer graphics. Contemporary work follows a more statistical approach and learns models from "big" collections of training data. In this talk I will discuss a family of approaches that combine these schools of thought through the use of latent variable statistical models. These models produce state-of-the-art performance on a variety of established benchmark tasks including object detection, human pose estimation, and facial analysis. Speaker Biography: Deva Ramanan is an associate professor of Computer Science at the University of California at Irvine. Prior to joining UCI, he was a Research Assistant Professor at the Toyota Technological Institute at Chicago. He received his B.S. in computer engineering from the University of Delaware in 2000, graduating summa cum laude. He received his Ph.D. in Electrical Engineering and Computer Science from UC Berkeley in 2005 under the supervision of David Forsyth. His research interests span computer vision, machine learning, and computer graphics, with a focus on visual recognition. He was awarded the David Marr Prize in 2009, the PASCAL VOC Lifetime Achievement Prize in 2010, an NSF Career Award in 2010, the Outstanding Young Researcher in Image and Vision Computing Award in 2012, and was selected as one of Popular Science's Brilliant 10 researchers in 2012. His work is supported by NSF, ONR, DARPA, as well as industrial collaborations with the Intel Science and Technology Center for Visual Computing, Google Research, and Microsoft Research. He has held visiting researcher positions at the Robotics Institute at CMU, the Visual Geometry Group at Oxford, and has been a consultant for Microsoft and Google. He is on the editorial board of the International Journal of Computer Vision (IJCV) and is an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). He regularly serves as a senior program committee member for the IEEE Conference of Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), and the European Conference on Computer Vision (ECCV). He also regularly serves on NSF panels for computer vision and machine learning.
Views: 5729 cmurobotics
Visual attention modeling, called HFT,
 
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Visual attention modeling, called HFT, see details in my paper: @article{li2013visual, title={Visual saliency based on scale-space analysis in the frequency domain}, author={Li, Jian and Levine, Martin D and An, Xiangjing and Xu, Xin and He, Hangen}, journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, volume={35}, number={4}, pages={996--1010}, year={2013}, publisher={IEEE} }
Views: 65 Li Jian
Basic Edge Detector
 
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Canny algorithm. Canny, J. (1986). A computational approach to edge detection. In IEEE Transactions on Pattern Analysis and Machine Intelligence, (6), 679-698.
Richard Szeliski - "Visual Reconstruction and Image-Based Rendering" (TCSDLS 2017-2018)
 
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Speaker: Richard Szeliski, Research Scientist and Director of the Computational Photography Group, Facebook Research Title: Visual Reconstruction and Image-Based Rendering Abstract: The reconstruction of 3D scenes and their appearance from imagery is one of the longest-standing problems in computer vision. Originally developed to support robotics and artificial intelligence applications, it has found some of its most widespread use in support of interactive 3D scene visualization. One of the keys to this success has been the melding of 3D geometric and photometric reconstruction with a heavy re-use of the original imagery, which produces more realistic rendering than a pure 3D model-driven approach. In this talk, I give a retrospective of two decades of research in this area, touching on topics such as sparse and dense 3D reconstruction, the fundamental concepts in image-based rendering and computational photography, applications to virtual reality, as well as ongoing research in the areas of layered decompositions and 3D-enabled video stabilization. Biography: Richard Szeliski is a Research Scientist in the Computational Photography group at Facebook, which he founded in 2015. He is also an Affiliate Professor at the University of Washington, and is member of the NAE and a Fellow of the ACM and IEEE. Dr. Szeliski has done pioneering research in the fields of Bayesian methods for computer vision, image-based modeling, image-based rendering, and computational photography, which lie at the intersection of computer vision and computer graphics. His research on Photo Tourism, Photosynth, and Hyperlapse are exciting examples of the promise of large-scale image and video-based rendering. Dr. Szeliski received his Ph.D. degree in Computer Science from Carnegie Mellon University, Pittsburgh, in 1988 and joined Facebook as founding Director of the Computational Photography group in 2015. Prior to Facebook, he worked at Microsoft Research for twenty years, the Cambridge Research Lab of Digital Equipment Corporation for six years, and several other industrial research labs. He has published over 150 research papers in computer vision, computer graphics, neural nets, and numerical analysis, as well as the books Computer Vision: Algorithms and Applications and Bayesian Modeling of Uncertainty in Low-Level Vision. He was a Program Committee Chair for CVPR’2013 and ICCV’2003, served as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and on the Editorial Board of the International Journal of Computer Vision, and as Founding Editor of Foundations and Trends in Computer Graphics and Vision. cs.unc.edu/tcsdls
Real Time Abnormal Event Detection in Highways
 
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This video presents an abnormal event detector in real time highway videos. The system was developed in C++ and OpenCV. Frame rate: 20~25fps The video shows the cells classified as car at the left, the foreground image at the middle and the optical flow at the right. The stopped car detections and the wrong way detections are displayed only after some consistency evaluation procedures. The SVM classifiers for each cell are updated in an online fasion using gradient descent in order to adapt to different weather and illumination conditions. The algorithm combines SVM, HOGs, Optical Flow, Gradient Descent and some mathematical morphology operations. Authors: Joao Faro ([email protected]), Patrick Brandao, ([email protected]) Some citations: Bottou, Léon. "Large-scale machine learning with stochastic gradient descent." Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. 177-186. Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part-based models." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.9 (2010): 1627-1645. Gunnar Farneback, Two-frame motion estimation based on polynomial expansion, Lecture Notes in Computer Science, 2003, (2749), 363-370.
Views: 1375 Joao Faro
Richard Szeliski: Reflections on Image-Based Modeling and Rendering
 
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Image-based modeling and rendering have been active areas of in computer vision and graphics since the early 1990s. After seminal work in representations and algorithms based on Light Fields and Lumigraphs, the field has seen steady improvements in many of the component technologies, including image-based modeling of 3D proxies, dealing with irregularly sampled data, the incorporation of video, and an interesting interplay with non-photorealistic rendering and computational photography. It has also spawned widely used consumer experiences such as panoramic "VR" photography, street-level (and indoor) immersive tours, and rich 3D navigation of Internet photo collections. In this talk, I review the evolution of this field, tease out some of the common themes and techniques, and speculate on the remaining difficulties and promises in this field, including the handling of reflections and transparent motion that commonly occur in such applications. Richard Szeliski is a Distinguished Scientist at Microsoft Research, where he leads the Interactive Visual Media Group. He is also an Affiliate Professor at the University of Washington, and is a Fellow of the ACM and IEEE. Dr. Szeliski has done pioneering research in the fields of Bayesian methods for computer vision, image-based modeling, image-based rendering, and computational photography, which lie at the intersection of computer vision and computer graphics. His research on Photo Tourism and Photosynth is an exciting example of the promise of large-scale image-based rendering. Dr. Szeliski received his Ph.D. degree in Computer Science from Carnegie Mellon University, Pittsburgh, in 1988 and joined Microsoft Research in 1995. Prior to Microsoft, he worked at Bell-Northern Research, Schlumberger Palo Alto Research, the Artificial Intelligence Center of SRI International, and the Cambridge Research Lab of Digital Equipment Corporation. He has published over 150 research papers in computer vision, computer graphics, medical imaging, neural nets, and numerical analysis, as well as the books Computer Vision: Algorithms and Applications and Bayesian Modeling of Uncertainty in Low-Level Vision. He is a Program Committee Chair for CVPR'2013, served as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and on the Editorial Board of the International Journal of Computer Vision, and is a Founding Editor of Foundations and Trends in Computer Graphics and Vision.
Views: 5898 BrownComputerScience
Mohammed Bennamoun - University of Western Australia
 
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Video describing some of the work that Dr. Umar Asif did for his PhD work under the supervision of Mohammed Bennamoun and Ferdous Sohel. Work was published in IEEE Transactions on Pattern Analysis, and Machine Intelligence (PAMI), IEEE Transactions on Robotic & Automation, and various other conferences, including IROS, ICRA, et.
Views: 32 Mohammed Bennamoun
lanchaArgos_clip2ProcessedSigSalRGB
 
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Video obtained using the algorithm described in: "Image Signature: Highlighting sparse salient regions", by Xiaodi Hou, Jonathan Harel, and Christof Koch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
Views: 12 Gonçalo Cruz
smallBoatStoppedProcessedSigSalRGB
 
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Video obtained using the algorithm described in: "Image Signature: Highlighting sparse salient regions", by Xiaodi Hou, Jonathan Harel, and Christof Koch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
Views: 17 Gonçalo Cruz
CVFX Lecture 3: Closed-form matting
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 3: Closed-form matting (1/30/14) 0:00:01 Closed-form matting 0:02:09 The color line assumption 0:14:04 alpha is a linear function of I 0:23:26 The cost function J 0:37:25 J as a function of alpha 0:39:20 The matting Laplacian 0:44:21 Constraining the matte with scribbles 0:48:36 An example result 0:56:27 Spectral matting 1:06:45 Combining matting components Follows Section 2.4 of the textbook, http://cvfxbook.com Key references: A. Levin, D. Lischinski, and Y. Weiss. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):228--42, Feb. 2008. http://dx.doi.org/10.1109/TPAMI.2007.1177 A. Levin, A. Rav-Acha, and D. Lischinski. Spectral matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10):1699--1712, Oct. 2008. http://dx.doi.org/10.1109/TPAMI.2008.168
Views: 6265 Rich Radke
lanchaArgos_clip3ProcessedSigSalRGB
 
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Video obtained using the algorithm described in: "Image Signature: Highlighting sparse salient regions", by Xiaodi Hou, Jonathan Harel, and Christof Koch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
Views: 28 Gonçalo Cruz
Tracklet-based Multi Commodity Network Flow tracking - APIDIS basekball dataset
 
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This is the tracking results presented in the paper: H. Ben Shitrit, J. Berclaz, F. Fleuret and P. Fua. Multi-Commodity Network Flow for Tracking Multiple People, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. Applied to the APIDIS dataset: http://www.apidis.org/Dataset/ For more information please visit: http://cvlab.epfl.ch/research/body/surv/
Views: 727 CVLABhoreshb
Application of Eigen values & Eigen vectors : 2D PCA
 
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EE5120 Project Jul-Nov 2018 - IIT Madras by Raju (EE18S038) & Rahul (EE17D202) Refernce Paper : J. Yang, D. Zhang, S. Member, A. F. Frangi, and J. Yang, “Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 26, no. 1, pp. 131–137, 2004
Views: 36 Rahul Manoj
Augmented reality coloring book on non-planar pages
 
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An augmented reality application for coloring books with possibly non-planar book pages. Deformable surface tracking algorithm is employed to presicely extract colors from the drawing to paint animated virtual 3D characters. References: [1] S Magnenat, D.T. Ngo, F Zund, M Ryffel, G Noris, G Rothlin, A Marra, M Nitti, P Fua, M Gross, R Sumner. Live Texturing of Augmented Reality Characters from Colored Drawings, ISMAR 2015. Best paper honorable mention. [2] D.T. Ngo, J. Ostlund and P. Fua. Template-based Monocular 3D Shape Recovery using Laplacian Meshes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, in press. For more details: http://cvlab.epfl.ch/research/surface/laplacianshaperecovery
Views: 8889 alibabach
ALIEN 2.0: The Infinite Memory
 
07:06:12
Abstract— Visual data is massive, is growing faster than our ability to store or index it [1] [2] and the cost of manual annotation is critically expensive. Effective methods for unsupervised learning are of paramount need. A possible scenario is that of considering visual data coming in the form of streams. In dynamically changing and non-stationary environments, the data distribution can change over time yielding the general phenomenon of concept drift [3], [4], [5] which violates the basic assumption of traditional machine learning algorithms (iid). This demo presents our recent results in learning an instancelevel object detector from a potentially infinitely long video-stream (i.e. YouTube). This is an extremely challenging problem largely unexplored, since a great deal of work has been done on learning under the iid assumption [6], [7], [8]. Our approach starts from the recent success of long term object tracking [9], [10], [11], [12], [13], [14] extending our previously developed [12] and demostrated [15], [16], [17] method (ALIEN). The novel contribution is the introduction of an online appearance learning procedure based on a incremental condensing [18] strategy which is shown to be asymptotically stable. Asymptotic stability evidence will be interactively evaluated by attendants based on a real time face tracking application using webcam or YouTube data. References [1] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248–255. IEEE, 2009. [2] P. Perona. Vision of a visipedia. Proceedings of the IEEE, 98(8):1526 –1534, aug. 2010. [3] Jeffrey C. Schlimmer and Richard H. Granger, Jr. Incremental learning from noisy data. Mach. Learn., 1(3):317–354, March 1986. [4] Gerhard Widmer and Miroslav Kubat. Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1):69–101, 1996. [5] Jo˜ao Gama, Indr˙e ˇZliobait˙e, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM Comput. Surv., 46(4):44:1–44:37, March 2014. [6] Vladimir N Vapnik and A Ya Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability & Its Applications, 16(2):264–280, 1971. [7] Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144–152. ACM, 1992. [8] Yoav Freund, Robert E Schapire, et al. Experiments with a new boosting algorithm. 1996. [9] Z. Kalal, J. Matas, and K. Mikolajczyk. P-n learning: Bootstrapping binary classifiers by structural constraints. In CVPR, june 2010. [10] Karel Lebeda, Simon Hadfield, Jiri Matas, and Richard Bowden. Long- term tracking through failure cases. In Proceeedings, IEEE workshop on visual object tracking challenge at ICCV 2013, Sydney, Australia, 2 December 2013. IEEE, IEEE. [11] Supancic and D. Ramanan. Self-paced learning for long-term tracking. Computer Vision and Pattern Recognition (CVPR), 2013. [12] Federico Pernici and Alberto Del Bimbo. Object tracking by oversam- pling local features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99(PrePrints):1, 2013. [13] Yang Hua, Karteek Alahari, and Cordelia Schmid. Occlusion and motion reasoning for long-term tracking. In Computer Vision–ECCV 2014, pages 172–187. Springer, 2014. [14] Zhibin Hong, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, and Dacheng Tao. Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking. June 2015. [15] Federico Pernici. Facehugger: The alien tracker applied to faces. In Computer Vision–ECCV 2012. Workshops and Demonstrations, pages 597–601. Springer, 2012. [16] Federico Pernici. Facehugger: The alien tracker applied to faces. In CVPR 2012. Workshops and Demonstrations, 2012. [17] Federico Pernici. Back to back comparison of long term tracking systems. In ICCV 2013. Workshops and Demonstrations, 2013. [18] P. E. Hart. The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 1968.
Views: 266 Federico Pernici
CVFX Lecture 12: Parametric Transformations and Scattered Data Interpolation
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 12: Parametric Transformations and Scattered Data Interpolation (3/3/14) 0:00:01 Computer Vision for Visual Effects 0:00:43 Dense correspondence vs. feature matching 0:01:51 Motion vectors 0:05:40 Parametric transformations 0:06:11 Translation 0:06:31 Rotation 0:06:59 Similarity transformations 0:08:03 Shears 0:09:40 Affine transformations 0:10:50 Projective transformations 0:13:51 Estimating projective transformations 0:18:33 Pre-normalizing correspondences 0:19:59 The Direct Linear Transform (DLT) 0:21:29 Outlier rejection 0:25:59 Scattered data interpolation 0:26:50 Bilinear interpolation 0:28:57 Thin-plate spline interpolation 0:38:00 Thin-plate interpolation example 0:44:27 B-spline interpolation 0:45:50 Diffeomorphic transformations Follows Sections 5.1-5.2 of the textbook. http://cvfxbook.com Key references: R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2nd edition, 2004. http://www.robots.ox.ac.uk:5000/~vgg/hzbook/ F. Bookstein. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567--85, June 1989. http://dx.doi.org/10.1109/34.24792 S. Joshi and M. Miller. Landmark matching via large deformation diffeomorphisms. IEEE Transactions on Image Processing, 9(8):1357--70, Aug. 2000. http://dx.doi.org/10.1109/83.855431
Views: 3219 Rich Radke
bigShipHighAlt_clip1ProcessedSigSalRGB
 
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Video obtained using the algorithm described in: "Image Signature: Highlighting sparse salient regions", by Xiaodi Hou, Jonathan Harel, and Christof Koch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
Views: 86 Gonçalo Cruz
Fast Randomized Singular Value Thresholding for Nuclear Norm Minimization
 
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[References] - Tae-Hyun Oh, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon, Fast Randomized Singular Value Thresholding for Nuclear Norm Minimization, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. - Tae-Hyun Oh, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon, Fast Randomized Singular Value Thresholding for Nuclear Norm Minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Submitted. [Abstract] Rank minimization problem can be boiled down to a tractable surrogate problem, such as Nuclear Norm Minimization (NNM), Weighted NNM (WNNM) problem. The problems related to NNM (or WNNM) can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT) (or Weighted SVT), but they suffer from high computational cost to compute a Singular Value Decomposition (SVD) at each iteration. In this paper, we propose an accurate and fast approximation method for SVT, called fast randomized SVT (FRSVT), where we avoid direct computation of SVD. The key idea is to extract an approximate basis for the range of a matrix from its compressed matrix. Given the basis, we compute the partial singular values of the original matrix from a small factored matrix. While the basis approximation is the bottleneck, our method is already severalfold faster than thin SVD. By adopting a range propagation technique, we can further avoid one of the bottleneck at each iteration. Our theoretical analysis provides a stepping stone between the approximation bound of SVD and its effect to NNM via SVT. Along with the analysis, our empirical results on both quantitative and qualitative studies show our approximation rarely harms the convergence behavior of the host algorithms. We apply it and validate the efficiency of our method on various vision problems, e.g. subspace clustering, weather artifact removal, simultaneous multi-image alignment and rectification.
Views: 978 Tae-Hyun Oh
NYU Semantic Segmentation with a Convolutional Network (8 categories)
 
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Demo of the semantic segmentation system described in the following paper: Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun: Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, August, 2013 The system labels every pixel with the category of the object it belongs to. The system is a multi-scale convolutional network trained on the "Stanford Backgrounds" dataset (8 object categories). It is post-processed using a simple majority vote on superpixels. It is applied to a stitched panoramic video shot near Washington Square Village in New York City. Each frame is labeled independently.
Views: 673 Yann LeCun
[Extended Demo] Robust 3D Object Trackinf fro Monocular Images using Stable Parts
 
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This is a demonstration of the 3D pose tracker developed at EPFL CVLab. The method is an extension of the original tracker [1], which gets help from a SLAM method to fill in the detection gaps. We even have an newer version that is even faster and more robust that the one shown here! See project webpage [2] for more details [1] Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, and Vincent Lepetit, "Robust 3D Object Tracking fro Monocular Images using Stable Parts", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 [2] http://cvlab.epfl.ch/research/3d_part_based_tracking
Views: 802 Sculdo
Matlab vs OpenCV fDSST
 
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I´m trying to run the code that I found in github, but I get different results ???¿?¿?¿¿¿ The OpenCV implementation of the fDSST tracker: https://github.com/TuringKi/fDSST_cpp Original Work: Discriminative Scale Space Tracking Martin Danelljan; Gustav Hager; Fahad Shahbaz Khan; Michael Felsberg IEEE Transactions on Pattern Analysis and Machine Intelligence
Views: 872 runMsconfig
HighSpy: Continuous Image Segmentation
 
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The final video from my BSc (Hons) Dissertation. School of Electronics and Computer Science (ECS) University of Southampton Southampton, England, UK Project Supervisor: Mahesan Niranjan Second Examiner: Srinandan Dasmahapatra Full paper here: http://www.psweiss.com/dissertation/ GCS Algorithm Credits: Matlab wrapper of Shai Bagon (http://www.wisdom.weizmann.ac.il/~bagon/matlab.html) for the code of Olga Veksler (http://www.csd.uwo.ca/faculty/olga/code.html), Vladimir Kolmogorov, Yuri Boykov and Ramih Zabih. [1] Efficient Approximate Energy Minimization via Graph Cuts Yuri Boykov, Olga Veksler, Ramin Zabih, IEEE transactions on PAMI, vol. 20, no. 12, p. 1222-1239, November 2001. [2] What Energy Functions can be Minimized via Graph Cuts? Vladimir Kolmogorov and Ramin Zabih. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). Earlier version appeared in European Conference on Computer Vision (ECCV), May 2002. [3] An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. Yuri Boykov and Vladimir Kolmogorov. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), September 2004 Footage Credits: DraganFly Innovations Inc. MarcusUAV Music: Me.
Views: 808 Musicguy208
Reconstruct and augment non-planar objects in realtime
 
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Reconstruct and augment non-planar objects in realtime. References: [1] T. D. Ngo, J. Östlund and P. Fua. Template-based Monocular 3D Shape Recovery using Laplacian Meshes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, in press. [2] J. Östlund, A. Varol, T. D. Ngo and P. Fua, Laplacian Meshes for Monocular 3D Shape Recovery, European Conference on Computer Vision, Florence, 2012. For more details: http://cvlab.epfl.ch/research/surface/laplacianshaperecovery
Views: 641 alibabach
Comparison of template update strategies while tracking needle tip using visual tracking
 
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Comparison of template update strategies while tracking needle tip using visual tracking. For more info on template update strategies, refer to this paper: L. Matthews, T. Ishikawa, and S. Baker, “The template update problem,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 810–815, June 2004.
Views: 7 OzU Robotics Lab
CVFX Lecture 26: 3D features and registration
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 26: 3D features and registration (5/1/14) 0:00:04 Algorithms for processing 3D data 0:04:24 3D feature detection 0:05:42 Spin images 0:13:38 Shape contexts 0:14:55 Features in 3D+color scans 0:15:43 Backprojected SIFT features 0:16:43 Physical scale keypoints 0:22:16 3D registration 0:24:27 Iterative Closest Points (ICP) 0:30:42 ICP refinements 0:35:24 3D registration example 0:38:23 Exploiting free space 0:39:41 Multiscan fusion 0:42:57 Combining triangulated meshes 0:44:31 VRIP 0:47:40 Scattered data interpolation 0:51:38 Poisson surface reconstruction 0:53:39 3D object detection 0:55:29 3D stroke-based segmentation 0:56:09 3D inpainting Follows Section 8.4 of the textbook. http://cvfxbook.com Key references: A. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5):433--49, May 2002. http://dx.doi.org/10.1109/34.765655 A. Frome, D. Huber, R. Kolluri, T. Bülow, and J. Malik. Recognizing objects in range data using regional point descriptors. In European Conference on Computer Vision (ECCV), 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_18 E. Smith, R. J. Radke, and C. Stewart. Physical scale keypoints: Matching and registration for combined intensity/range images. International Journal of Computer Vision, 97(1):2--17, Mar. 2012. http://dx.doi.org/10.1007/s11263-011-0469-4 E. R. Smith, B. J. King, C. V. Stewart, and R. J. Radke. Registration of combined range-intensity scans: Initialization through verification. Computer Vision and Image Understanding, 110(2):226--44, May 2008. http://dx.doi.org/10.1016/j.cviu.2007.08.004 S. Rusinkiewicz and M. Levoy. Efficient variants of the ICP algorithm. In International Conference on 3-D Digital Imaging and Modeling (3DIM), 2001. http://dx.doi.org/10.1109/IM.2001.924423 B. Curless and M. Levoy. A volumetric method for building complex models from range images. In ACM SIGGRAPH (ACM Transactions on Graphics), 1996. http://dx.doi.org/10.1145/237170.237269 G. Turk and J. F. O'Brien. Shape transformation using variational implicit functions. In ACM SIGGRAPH (ACM Transactions on Graphics), 1999. http://dx.doi.org/10.1145/311535.311580 J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, and T. R. Evans. Reconstruction and representation of 3D objects with radial basis functions. In ACM SIGGRAPH (ACM Transactions on Graphics), 2001. http://dx.doi.org/10.1145/383259.383266 M. Kazhdan, M. Bolitho, and H. Hoppe. Poisson surface reconstruction. In Eurographics Symposium on Geometry Processing, 2006. http://dl.acm.org/citation.cfm?id=1281965
Views: 6594 Rich Radke
smallBoatMovingProcessedSigSalRGB
 
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Video obtained using the algorithm described in: "Image Signature: Highlighting sparse salient regions", by Xiaodi Hou, Jonathan Harel, and Christof Koch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
Views: 30 Gonçalo Cruz
BCLM Fitting in the LFW
 
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Bayesian Constrained Local Model (BCLM-KDE) fitting performance in the Labeled Faces in the Wild (LFW) dataset. Bayesian Constrained Local Models Revisited Pedro Martins, João F. Henriques, R. Caseiro, Jorge Batista IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016
Views: 424 Pedro Martins