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Statements

Subject Item
dbr:Geometric_feature_learning
rdf:type
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Geometric feature learning
rdfs:comment
Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find a set of representative features of geometric form to represent an object by collecting geometric features from images and learning them using efficient machine learning methods. Humans solve visual tasks and can give fast response to the environment by extracting perceptual information from what they see. Researchers simulate humans' ability of recognizing objects to solve computer vision problems. For example, M. Mata et al.(2002) applied feature learning techniques to the mobile robot navigation tasks in order to avoid obstacles. They used genetic algorithms for learning features and recognizing objects (figures). Geometric feature lear
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dbc:Feature_detection_(computer_vision) dbc:Applications_of_computer_vision
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34633465
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1091156660
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dbr:Curve_fitting dbr:Support_vector_machines dbr:Corner_detection dbr:Genetic_algorithms dbr:Kadir–Brady_saliency_detector dbr:Bayesian_network dbr:Line_detection dbc:Feature_detection_(computer_vision) dbr:Distinctive_features dbr:Artificial_intelligence dbc:Applications_of_computer_vision dbr:Edge_detection dbr:Image_texture dbr:Machine_learning dbr:Object_recognition dbr:Feature_vector dbr:Ridge_detection dbr:Computer_vision dbr:Motion_estimation dbr:Feature_space dbr:Hyperplane dbr:Connected-component_labeling dbr:Blob_detection dbr:Feature_detection_(computer_vision) dbr:Mobile_robot_navigation
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Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find a set of representative features of geometric form to represent an object by collecting geometric features from images and learning them using efficient machine learning methods. Humans solve visual tasks and can give fast response to the environment by extracting perceptual information from what they see. Researchers simulate humans' ability of recognizing objects to solve computer vision problems. For example, M. Mata et al.(2002) applied feature learning techniques to the mobile robot navigation tasks in order to avoid obstacles. They used genetic algorithms for learning features and recognizing objects (figures). Geometric feature learning methods can not only solve recognition problems but also predict subsequent actions by analyzing a set of sequential input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probability of each hypothesis give a most probable action. This technique is widely used in the area of artificial intelligence.
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