Category Archives: Computer Vision & Pattern Recognition

Developing and Applying Optoelectronics in Machine Vision

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Infinite Images: Creating and Exploring a Large Photorealistic Virtual Space B. Due to large volume of data, quantitative nature and accurate historical data, machine learning can be used in financial analysis. Suen has given 195 seminars at major computer industries and various government and academic institutions around the world. Suen, "A novel hybrid CNN-SVM classifier for recognizing handwritten digits," Pattern Recognition, vol. 45, 1318-1325, 2012.

Computer Confluence: Exploring Tomorrow's Technology

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A rapid wide-area search to provide alerts in larger fields of regard is the classical example that has always been envisioned. The hierarchy makes it possible to apply previously learned skills to new tasks. I'll begin by identifying four common types of computational bottlenecks that occur across all of machine learning, or prototype algorithmic problems: N-body problems, graph operations, linear algebra, and optimization.

Multimodal Processing and Interaction: Audio, Video, Text

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Multiple View Geometry of Non-planar Algebraic Curves. Advances in Neural Information Processing Systems (NIPS), 2008. [ pdf ] [ HAL tech-report ] [ matlab code ] J. Each section provides background on a set of models or machine learning tools involved, and methods of inference. Memo Akten, IGGI, Literature Review 30/09/2015 CARAMIAUX, B., MONTECCHIO, N., TANAKA, A., AND BEVILACQUA, F. 2014. Blindenhilfsmittel: Für Blinde wird es durch die Texterkennung möglich, eingescannte Texte über Computer und Braillezeile zu lesen oder sich per Sprachausgabe vorlesen zu lassen. ↑ http://www.kurzweilai.net/how-bio-inspired-deep-learning-keeps-winning-competitions 2012 Kurzweil AI Interview mit Jürgen Schmidhuber zu den acht Wettbewerben, die sein Deep Learning Team zwischen 2009 und 2012 gewann ↑ Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K.

Real-Time Object Measurement and Classification (Nato ASI

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Some authors have argued that it is not trivial to fit or uncover these geon-like shapes in natural scenes [92]. More study on robot planning, programming, sensing, vision, and CAD/CAM. (Odd, spring) (Evenings) Prerequisites: ITCS 1215 or MATH 2164, or consent of the Department. This problem is exacerbated by sensors that are not visible imagers and do not have the resolution of visible imagers and are not familiar to normal human vision, such as thermal imaging.

Artificial Evolution: Third European Conference, AE '97,

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The Collective brings together researchers from multiple departments at UCLA, including Mathematics, Statistics, Computer Science, Brain Mapping, Computational Biology, Neuro Imaging, Image Informatics, Psychology, and Radiology. In order to give the reader a realistic feel for the task difficulty, no annotations are given to show the targets in the scenes. Native to Europe, the moth's range includes southern France and Italy, the Iberian Peninsula, and parts of Siberia and northern Africa.

Hands: A Pattern Theoretic Study of Biological Shapes

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View at Publisher · View at Google Scholar S. Pentland. "Probabilistic Visual Learning for Object Detection." Shao, “ Multimodal Dynamic Networks for Gesture Recognition ”, ACM International Conference on Multimedia (MM), Orlando, USA, 2014. Overview of the CLEF 2009 Robot Vision Track (A. Liu, “A Generalized Coding Artifacts and Noise Removal Algorithm for Digitally Compressed Video Signals”, International Conference on Multimedia Modelling (MMM), Taipei, Taiwan, January 2011.

Proceedings IWISP '96, 4-7 November 1996; Manchester, UK:

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Song Cao, Genquan Duan and Haizhou Ai, Who Are Like Me: Fast Human Pose Retrieval in Unconstrained Environments, First Asian Conference on Pattern Recognition, November 28-30, 2011 (ACPR 2011), Beijing, China. 36. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. Each time the voice system passes through an adaptation cycle, the resulting tongue position of the child for speaking “A” is saved by the learning process.

Sonet & T1: Architectures for Digital Transport Networks

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AlchemyVision features a set of APIs that includes a facial detection/recognition API, an image link extraction API and an image tagging API. David will receive his PhD from NYU in the spring, focusing on applications of convolutional neural networks. The heuristic search, to be discussed shortly, has also been used for handling this problem. known models of the process and the required control objective. Wei Xu, Jianguo Zhang, Hu Xu, Maojun Zhang, Height Estimation of Urban Buildings Using Angle Consistency of Borderlines of Roofs, IEEE International Conference on Multimedia and Expo (ICME) 2013.

Optical Pattern Recognition XXIV (Proceedings of SPIE)

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From our experience, we know the answer is “not always”. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. In Computer Vision and Pattern Recognition (CVPR), pages 3354–3361, 2012. The article focuses on the recent flurry of work at FAIR, DeepMind and other places on neural nets (particularly recurrent neural nets) augmented with a separate memory module. From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.

Object Detection and Analysis: A Coherency Filtering

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Two types of connection are usually distinguished: excitatory and inhibitory. Is a student’s current grade 10% below last month’s? Invited paper [bib] Matthias Kranz, Alexis Maldonado, Benedikt Hoernler, Radu Bogdan Rusu, Michael Beetz, Gerhard Rigoll, Albrecht Schmidt, "A Knife and a Cutting Board as Implicit User Interface - Towards Context-Aware Kitchen Utilities", In Proceedings of First International Conference on Tangible and Embedded Interaction 2007, TEI 2007, February 15-17 Baton Rouge, Louisiana, USA, 2007. [bib] Matthias Kranz, Alexis Maldonado, Radu Bogdan Rusu, Benedikt Hoernler, Gerhard Rigoll, Michael Beetz, Albrecht Schmidt, "Sensing Technologies and the Player-Middleware for Context-Awareness in Kitchen Environments", In Proceedings of Fourth International Conference on Networked Sensing Systems, June 6 - 8, 2007, Braunschweig, Germany, 2007. [bib] Armin Müller, Michael Beetz, "Towards a Plan Library for Household Robots", In Proceedings of the ICAPS'07 Workshop on Planning and Plan Execution for Real-World Systems: Principles and Practices for Planning in Execution, Providence, USA, 2007. [bib] Martin Buss, Michael Beetz, Dirk Wollherr, "CoTeSys --- Cognition for Technical Systems", In Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM), 2007. [bib] Michael Beetz, Jan Bandouch, Alexandra Kirsch, Alexis Maldonado, Armin Müller, Radu Bogdan Rusu, "The Assistive Kitchen --- A Demonstration Scenario for Cognitive Technical Systems", In Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM), 2007. [bib] Radu Bogdan Rusu, Alexis Maldonado, Michael Beetz, Brian Gerkey, "Extending Player/Stage/Gazebo towards Cognitive Robots Acting in Ubiquitous Sensor-equipped Environments", In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) Workshop for Network Robot Systems, 2007, April 14, Rome, Italy, 2007. [bib] Freek Stulp, Michael Isik, Michael Beetz, "Implicit Coordination in Robotic Teams using Learned Prediction Models", In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1330-1335, 2006. [bib] Freek Stulp, Mark Pflüger, Michael Beetz, "Feature Space Generation using Equation Discovery", In Proceedings of the 29th German Conference on Artificial Intelligence (KI), 2006. [bib] Freek Stulp, Michael Beetz, "Action Awareness -- Enabling Agents to Optimize, Transform, and Coordinate Plans", In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006. [bib] Markus Geipel, Michael Beetz, "Learning to shoot goals, Analysing the Learning Process and the Resulting Policies", In RoboCup-2006: Robot Soccer World Cup X, Springer Verlag, Berlin, 2006. to be published [bib] Radu Bogdan Rusu, Alexis Maldonado, Michael Beetz, Matthias Kranz, Lorenz Mösenlechner, Paul Holleis, Albrecht Schmidt, "Player/Stage as Middleware for Ubiquitous Computing", In Proceedings of the 8th Annual Conference on Ubiquitous Computing (Ubicomp 2006), Orange County California, September 17-21, 2006. [bib] Michael Beetz, Jan Bandouch, Suat Gedikli, Nico von Hoyningen-Huene, Bernhard Kirchlechner, Alexis Maldonado, "Camera-based Observation of Football Games for Analyzing Multi-agent Activities", In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006. [bib] Armin Müller, Michael Beetz, "Designing and Implementing a Plan Library for a Simulated Household Robot", In Cognitive Robotics: Papers from the AAAI Workshop, American Association for Artificial Intelligence, Menlo Park, California, pp. 119-128, 2006. [bib] Matthias Kranz, Radu Bogdan Rusu, Alexis Maldonado, Michael Beetz, Albrecht Schmidt, "A Player/Stage System for Context-Aware Intelligent Environments", In Proceedings of UbiSys'06, System Support for Ubiquitous Computing Workshop, at the 8th Annual Conference on Ubiquitous Computing (Ubicomp 2006), Orange County California, September 17-21, 2006, 2006. [bib] Michael Beetz, Henrik Grosskreutz, "Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior", In Journal of Artificial Intelligence Research, vol. 24, pp. 799-849, 2005. [bib] Michael Beetz, "Towards Comprehensive Computational Models for Plan-Based Control of Autonomous Robots", Chapter in Mechanizing Mathematical Reasoning: Essays in Honor of Jörg H.