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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: 29545 MJ Tube
pattern analysis and machine intelligence
 
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DOTNET PROJECTS,2013 DOTNET PROJECTS,IEEE 2013 PROJECTS,2013 IEEE PROJECTS,IT PROJECTS,ACADEMIC PROJECTS,ENGINEERING PROJECTS,CS PROJECTS,JAVA PROJECTS,APPLICATION PROJECTS,PROJECTS IN MADURAI,M.E PROJECTS,M.TECH PROJECTS,MCA PROJECTS,B.E PROJECTS,IEEE PROJECTS AT MADURAI,IEEE PROJECTS AT CHENNAI,IEEE PROJECTS AT COIMBATORE,PROJECT CENTER AT MADURAI,PROJECT CENTER AT CHENNAI,PROJECT CENTER AT COIMBATORE,BULK IEEE PROJECTS,REAL TIME PROJECTS,RESEARCH AND DEVELOPMENT,INPLANT TRAINING PROJECTS,STIPEND PROJECTS,INDUSTRIAL PROJECTS,MATLAB PROJECTS,JAVA PROJECTS,NS2 PROJECTS, Ph.D WORK,JOURNAL PUBLICATION, M.Phil PROJECTS,THESIS WORK,THESIS WORK FOR CS
Views: 13 Ranjith Kumar
CVFX Lecture 23: LiDAR and time-of-flight sensing
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 23: LiDAR and time-of-flight sensing (4/21/14) 0:00:01 3D data acquisition 0:01:12 LiDAR scanning 0:05:06 LiDAR data example 0:09:33 LiDAR scanning difficulties 0:15:10 LiDAR scanning principles 0:15:20 Pulse-based LiDAR 0:20:23 Phase-based LiDAR 0:27:02 LiDAR scanning for VFX examples 0:31:58 LiDAR scanning for autonomous vehicles 0:34:05 Time-of-flight cameras 0:37:20 ToF image and video examples 0:42:27 Live ToF results from Microsoft Kinect 0:50:06 Skeleton estimation from Kinect SDK Follows Section 8.1 of the textbook. http://cvfxbook.com Key references: R. A. Jarvis. A perspective on range finding techniques for computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5(2):122--39, Mar. 1983. http://dx.doi.org/10.1109/TPAMI.1983.4767365 P. J. Besl. Active, optical range imaging sensors. Machine Vision and Applications, 1(2):127--52, June 1988. http://dx.doi.org/10.1007/BF01212277 A. Kolb, E. Barth, R. Koch, and R. Larsen. Time-of-flight cameras in computer graphics. In Eurographics, 2010. http://dx.doi.org/10.1111/j.1467-8659.2009.01583.x Y. Cui, S. Schuon, D. Chan, S. Thrun, and C. Theobalt. 3D shape scanning with a time-of-flight camera. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2010. http://dx.doi.org/10.1109/CVPR.2010.5540082
Views: 12854 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: 169926 TEDx Talks
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: 2395 Rich Radke
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: 30171 Rich Radke
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
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: 811 Andreas Holzinger
Vision-based detection of an Asctec Hummingbird
 
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This video shows the detection algorithm from the "Detecting Flying Objects using a Single Moving Camera" work, published in IEEE Transactions on Pattern Analysis and Machine Intelligence 2017
Views: 1160 Artem Rozantsev
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: 6887 Rich Radke
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: 396 Pedro Martins
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: 6408 DAHBOO777
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.
Single user pose recovery illustrative video
 
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Single user 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: 10 Markos Sigalas
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
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: 6256 Rich Radke
Random Forest based Classification
 
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This tutorial explains the Random Forest algorithm with a very simple example. Random Forest algorithm has gained a significant interest in the recent past, due to its quality performance in several areas. The random forest algorithm discussed in this tutorial is based on the following references: 1. Breiman L (2001). "Random Forests". Machine Learning. 45 (1): 5–32. doi:10.1023/A:1010933404324. 2. Ho TK (1998). "The Random Subspace Method for Constructing Decision Forests" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844. doi:10.1109/34.709601 3. Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. 4. Breiman L, Ghahramani Z (2004). "Consistency for a simple model of random forests". Statistical Department, University of California at Berkeley. Technical Report (670) 5. Dietterich, Thomas (2000). "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization". Machine Learning: 139–157
Views: 31702 Dr. Niraj Kumar
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
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
bigShipHighAlt_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: 40 Gonçalo Cruz
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: 2837 cmurobotics
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: 5956 TEDx Talks
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
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
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
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
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: 2705 Yann LeCun
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: 6585 Rich Radke
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: 1368 Yann LeCun
ALIEN 2.0: The Infinite Memory
 
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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
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.
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
Full Body Pose Tracking  - Quantitative evaluation vs. Microsoft Kinect SDK
 
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The video shows one sequence with body pose tracking results of 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 and the skeletonization module of the Kinect SDK. Results from both methods have been compared against ground truth data. The employed datasets, along with relevant annotation and processing results, are accessible at http://www.ics.forth.gr/cvrl/fbody/.
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: 6692 NoblisNetwork
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
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: 726 CVLABhoreshb
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: 3215 Rich Radke
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: 423 Pedro Martins
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: 8878 alibabach
CVFX Lecture 4: Markov Random Field (MRF) and Random Walk Matting
 
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ECSE-6969 Computer Vision for Visual Effects Rich Radke, Rensselaer Polytechnic Institute Lecture 4: Markov Random Field (MRF) and Random Walk Matting: (2/3/14) 0:00:33 Markov Random Field matting 0:03:29 Gibbs energy 0:07:06 Data and smoothness terms 0:14:22 Known and unknown regions 0:15:37 Belief propagation 0:25:51 Foreground and background sampling 0:32:52 MRF minimization code 0:34:35 Random walk matting 0:42:28 The graph Laplacian 0:45:23 Constraining the matte 0:49:33 Modifications to the approach 0:50:34 Robust matting 0:54:24 Soft scissors Follows Sections 2.5-2.6 of the textbook, http://cvfxbook.com Key references: J. Wang and M. Cohen. An iterative optimization approach for unified image segmentation and matting. In IEEE International Conference on Computer Vision (ICCV), 2005. http://dx.doi.org/10.1109/ICCV.2005.37 L. Grady. Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11):1768--83, Nov. 2006. http://dx.doi.org/10.1109/TPAMI.2006.233 J. Wang and M. Cohen. Optimized color sampling for robust matting. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2007. http://dx.doi.org/10.1109/CVPR.2007.383006 J. Wang, M. Agrawala, and M. Cohen. Soft scissors: an interactive tool for realtime high quality matting. In ACM SIGGRAPH (ACM Transactions on Graphics), 2007. http://dx.doi.org/10.1145/1276377.1276389
Views: 13246 Rich Radke
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
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
Artificial intelligence can recognize you by the way you walk
 
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Artificial intelligence can recognize you by the way you walk: http://bgr.com/2018/05/29/artificial-intelligence-people-walk/. Thanks for watching, subscribe for more videos: https://www.youtube.com/channel/UC68ZjqRTLFegIuA5PZhbgMA?sub_confirmation=1 Airport lines are the worst, no matter how early you arrive. You’ve got to check your bags, then go through the necessary security and ID checks, and you’re usually waiting in line for most of them. The emergence of artificial intelligence may speed that whole process by eliminating at least one of those types of queues. Researchers have discovered one way to effortlessly and passively identify a person with increased accuracy. Unfortunately, the system can also be used for spying on people. Don't Miss: Today’s top deals: $17 Anker earbuds, robot vacuum, $60 noise cancelling headphones, Crock-Pot, more Found by Gizmodo, the IEEE Transactions on Pattern Analysis and Machine Intelligence paper reveals that AI can be used to identify individuals by the way they walk. “Each human has approximately 24 different factors and movements when walking, resulting in every individual person having a unique, singular walking pattern,” lead author of the study Omar Costilla Reyes said in a statement. AI is so good at analyzing the data that it can spot people who’re faking their walking. The method is less invasive than other behavioral biometric verification systems and could be deployed inside airports and other areas to check the identity of people passing by instantly. The system was nearly 100% accurate in identifying individuals, with an error rate of just 0.7%. As I said, this makes it a great tool for spying, but it requires two essential elements to work. For starters, the system requires high-resolution cameras and unique flooring with sensors embedded into it to measure variables related to a user’s walking behavior. It also needs a database of information so that it can compare its findings against saved records. Therefore, mass-surveillance operations that could identify a large number of people by the way they walk seem highly unlikely for the time being. But such systems may very well be used to speed up airport checks for frequent flyers who would not mind having their walking habits recorded in a database the first time they go through it. Their walking fingerprint could then be shared with other airports who would deploy the same kind of technology. Tags: AI #Artificial, #intelligence, #recognize, #walk #computers
Views: 3 Heidi arwtdu
Structure-based Low-Rank Model with Graph Nuclear Norm Regularization for Noise Removal
 
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Structure-based Low-Rank Model with Graph Nuclear Norm Regularization for Noise Removal To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: http://www.jpinfotech.org Nonlocal image representation methods, including group-based sparse coding and BM3D, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves better performance than several state-of-the-art algorithms.
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
lanchaArgos_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: 24 Gonçalo Cruz
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
[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: 795 Sculdo
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