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This work presents a full generic approach to the detection and recognition of traffic signs. The approach, originally developed for a mobile mapping application, is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization – Error-Correcting Output Codes – and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future lines of research, and continuing challenges for traffic sign recognition.

Image processing and computer vision. --- Pattern recognition systems. --- Traffic signs and signals -- Data processing. --- Computer vision --- Pattern recognition systems --- Traffic signs and signals --- Driver assistance systems --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Applied Physics --- Transportation Engineering --- Data processing --- Control systems. --- Traffic signal control systems --- Computer science. --- Image processing. --- Computer Science. --- Image Processing and Computer Vision. --- Sociotechnical systems --- Computer vision. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.

Artificial intelligence. --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical data processing. --- Pattern recognition. --- Artificial Intelligence. --- Image Processing and Computer Vision. --- Pattern Recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.

Computer science. --- Artificial intelligence. --- Image processing. --- Pattern recognition. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Image Processing and Computer Vision. --- Pattern Recognition. --- Design perception --- Pattern recognition --- Pictorial data processing --- Picture processing --- Processing, Image --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Informatics --- Pattern recognition systems. --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Computer vision. --- Optical pattern recognition. --- Artificial Intelligence. --- Optical data processing --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical data processing. --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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Mathematical statistics --- Electronics --- Telecommunication technology --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- factoranalyse --- optische communicatie --- optische elektronica --- datacommunicatie --- KI (kunstmatige intelligentie)

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This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.

Mathematical statistics --- Electronics --- Telecommunication technology --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- factoranalyse --- optische communicatie --- optische elektronica --- datacommunicatie --- KI (kunstmatige intelligentie)