Pattern recognition (psychology)
Related papers: 20
About
Pattern recognition in robotics and AI refers to the computational process by which systems identify, classify, and interpret structured information from raw sensory data — including images, point clouds, depth maps, and time-series signals. Drawing from both psychological principles of human perception and statistical machine learning, it enables robots to detect and categorize objects, recognize faces and gestures, segment scenes, and estimate spatial poses. Techniques range from classical feature descriptors and hierarchical mixture models to deep convolutional neural networks that learn rich representations directly from RGB, RGB-D, or LiDAR data. In practice, pattern recognition underpins critical robotic capabilities such as autonomous navigation, grasp planning, pedestrian detection, action segmentation, and fiducial marker tracking. Standardized benchmarks like KITTI and large-scale object datasets have accelerated progress by enabling rigorous comparison of recognition algorithms under real-world conditions. Its importance lies in bridging raw sensor input and meaningful semantic understanding, allowing robots to operate reliably and adaptively in complex, unstructured environments.
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Top Cited Papers
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Andreas Geiger, P Lenz, R. Urtasun
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Color indexing
Michael J. Swain, Dana H. Ballard
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Yin Zhou, Oncel Tuzel
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VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana, Sebastian Scherer
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SECOND: Sparsely Embedded Convolutional Detection
Yan Yan, Yuxing Mao, Bo Li
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Michael I. Jordan, Robert A. Jacobs
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Automatic generation and detection of highly reliable fiducial markers under occlusion
Sergio Garrido-Jurado, Rafael Muñoz‐Salinas, F.J. Madrid-Cuevas, Manuel J. Marín‐Jiménez
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Temporal Convolutional Networks for Action Segmentation and Detection
Colin Lea, M. D. Flynn, Renè Vidal, Austin Reiter, Gregory D. Hager
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Saurabh Gupta, Ross Girshick, Pablo Arbeláez, Jitendra Malik
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Novelty detection: a review—part 1: statistical approaches
M. Markou, Sameer Singh
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Pedestrian detection: A benchmark
Piotr Dollár, Christian Wojek, Bernt Schiele, Pietro Perona
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A large-scale hierarchical multi-view RGB-D object dataset
Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox
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Myoelectric control systems—A survey
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Monocular Pedestrian Detection: Survey and Experiments
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Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
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DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, Silvio Savarese
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Ultrafast machine vision with 2D material neural network image sensors
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On Learning, Representing, and Generalizing a Task in a Humanoid Robot
Sylvain Calinon, F. Guenter, Aude Billard
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