Results 241 to 250 of about 160,762 (267)
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Generic object recognition with boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of either discontinuity or homogeneity. A variety of local descriptors can be applied to form a set of feature vectors for each local region.
Andreas Opelt +3 more
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Probabilistic object recognition and localization
Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999Objects can be represented by regions of local structure as well as dependencies between these regions. The appearance of local structure can be characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters.
Bernt Schiele, Alex Pentland
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Deformation Invariants in Object Recognition
Computer Vision and Image Understanding, 1997Summary: We study invariance to transformations having two components. The first is an arbitrary large affine transformation. This approximates a viewpoint change. The second is a small, but otherwise general, non-linear deformation. Such a deformation can arise from several sources, including change in the object itself.
Ehud Rivlin, Isaac Weiss
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Object Recognition, Neurophysiology
2002As viewing distance, viewing angle or lighting conditions change, so too does the image of an object which we see. Despite the seemingly endless variety of images that objects can project, the human visual system remains able to rapidly and reliably identify them across huge changes in appearance.
Wallis, G., Bülthoff, H. H.
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Tracking objects with a recognition algorithm
Pattern Recognition Letters, 1998In this paper, we propose an efficient method for tracking 3D modelled objects in cluttered scenes. Rather than tracking objects in the image, our approach relies on the object recognition aspect of tracking. Possible matches between image and model features define volumes within a transformation space.
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Dataset Issues in Object Recognition
2006Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in images, and evaluating the performance of recognition algorithms.
Ponce, J +12 more
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Coordinate transformations in object recognition.
Psychological Bulletin, 2006A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation processes ...
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Neuronal circuitry for recognition memory of object and place in rodent models
Neuroscience and Biobehavioral Reviews, 2022Owen Y Chao +2 more
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Object recognition datasets and challenges: A review
Neurocomputing, 2022Homayoun Najjaran, Xiangrui Liu
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Using spin images for efficient object recognition in cluttered 3D scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999A E Johnson, Martial Hebert
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