David

Computational geometry, generative design, optimization. Geometry and Optimization. Zdenek Kalal. OpenTLD. Disclaimer: This project is now old and will no longer be updated.

Please have a look at our new tracker CMT instead. On this page you can find a C++ implementation of OpenTLD that was originally published in MATLAB by Zdenek Kalal. OpenTLD is used for tracking objects in video streams. What makes this algorithm outstanding is that it does not make use of any training data. This implementation is based solely on open source libraries, meaning that you do not need any commercial products to compile or run it. The easiest way to get started is to download the precompiled binaries that are available for Windows and Ubuntu. The source code of OpenTLD is published under the terms of the GNU General Public License, so feel free to dig through it. Result Videos Download You can download this project in either zip or tar formats. . $ git clone Frequently Asked Questions. OpenTLD. TLD Vision. Our technology is based on TLD, an award winning algorithm well known in computer vision community and industry.

TLD integrates research in object tracking, machine learning and object detection into a real-time process. How does it work? Tracking estimates the object motion. Tracking in OpenTLD aka Predator — Jay Rambhia. As my Google Summer of Code 2012 project, I have to port OpenTLD to python using OpenCV and SimpleCV.

OpenTLD a.k.a. Predator was first made by Zdenek Kalal in MATLAB. Raspberry Pi Image Tracking. OpenCV. Photogrammetry. Photogrammetry is an estimative scientific method that aims at recovering the exact positions and motion pathways of designated reference points located on any moving object, on its components and in the immediately adjacent environment.

Photogrammetry employs high-speed imaging and the accurate methods of remote sensing in order to detect, measure and record complex 2-D and 3-D motion fields (see also SONAR, RADAR, LiDAR etc.). Photogrammetry feeds the measurements from remote sensing and the results of imagery analysis into computational models in an attempt to successively estimate, with increasing accuracy, the actual, 3-D relative motions within the researched field. Its applications include satellite tracking of the relative positioning alterations in all Earth environments (e.g. tectonic motions etc), the research on the swimming of fish, of bird or insect flight, other relative motion processes (International Society for Photogrammetry and Remote Sensing). Integration[edit] Optical Flow-Based Person Tracking by Multiple Cameras. BibTeX @INPROCEEDINGS{Tsutsui98opticalflow-based, author = {Hideki Tsutsui and Jun Miura and Yoshiaki Shirai}, title = {Optical Flow-Based Person Tracking by Multiple Cameras}, booktitle = {Machine Vision and Applications}, year = {1998}, pages = {91--96}}

Stream_client.c. Image and Vision Computing - Human skeleton tracking from depth data using geodesic distances and optical flow. Volume 30, Issue 3, March 2012, Pages 217–226 Best of Automatic Face and Gesture Recognition 2011.

Eprints.qut.edu.au/14756/1/14756.pdf. Streaming OpenCV Videos Over the Network. Emgu CV Head Movement Direction Tutorial. 21 of the Best Free Linux Financial Software. The repercussions of the credit crunch have continued to occupy headline news in recent weeks.

We have witnessed the demise of Lehman Brothers and Washington Mutual, the rescue of AIG, Fannie Mae and Freddie Mac, the sale of Merrill Lynch, the proposed merger of Lloyds TSB and HBOS (which would never have been permitted under normal circumstances), and the list goes on. Yet, even in such volatile markets there remain significant profit opportunities for the day trader. For example, earlier this month £103bn was added to the UK blue chip companies (representing a record 8.84% jump) in a single day. Linux Stock Market Tracking Software. Trading With Linux. Wave Function - Sixty Symbols. Heisenberg's Uncertainty Principle Experiment.

Quantum Mechanics: Propability. What is the Uncertainty Principle? Probability: the Science of Uncertainty. Computational Neuroscience. About the Course This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function.

We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. Computer Networks. Quantum Mechanics & Computation. Intro to Linear Dynamical Systems. Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems.

Linear and Discrete Optimization. About the Course This course serves as an introduction to linear and discrete optimization from the viewpoint of a mathematician or computer scientist.

Besides learning how linear and discrete optimization can be applied, we focus on understanding methods that solve linear programs and discrete optimization problems in a mathematically rigorous way. We will answer questions like: Does a particular method work correctly? Does it terminate and, if yes, in what time? Statistical Mechanics: Algorithms & computations. About the Course This course discusses the computational approach in modern physics in a clear yet accessible way. Individual modules contain in-depth discussions of algorithms ranging from basic enumeration methods to cutting-edge Markov-chain techniques.

AI: Neural Networks. About the Course Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.

Recommended Background. Chaos theory. A double rod pendulum animation showing chaotic behavior. Starting the pendulum from a slightly different initial condition would result in a completely different trajectory. The double rod pendulum is one of the simplest dynamical systems that has chaotic solutions. Chaos: When the present determines the future, but the approximate present does not approximately determine the future. Chaotic behavior can be observed in many natural systems, such as weather and climate.[6][7] This behavior can be studied through analysis of a chaotic mathematical model, or through analytical techniques such as recurrence plots and Poincaré maps.

Introduction[edit] Chaos theory concerns deterministic systems whose behavior can in principle be predicted. Chaotic dynamics[edit] The map defined by x → 4 x (1 – x) and y → x + y mod 1 displays sensitivity to initial conditions. In common usage, "chaos" means "a state of disorder".[9] However, in chaos theory, the term is defined more precisely. Fabulous Fractals and Difference Equations. OpenCV.