Results 231 to 240 of about 1,343,545 (271)
Some of the next articles are maybe not open access.
2004
AbstractThis chapter discusses object recognition in rats. It describes the two paradigms most often used to assess object recognition in rats: delayed nonmatching-to-sample (DNMS) and novel-object-preference (NOP). It shows that despite superficial similarities, DNMS and NOP tests engage different behavioral and motivational systems and therefore ...
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AbstractThis chapter discusses object recognition in rats. It describes the two paradigms most often used to assess object recognition in rats: delayed nonmatching-to-sample (DNMS) and novel-object-preference (NOP). It shows that despite superficial similarities, DNMS and NOP tests engage different behavioral and motivational systems and therefore ...
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Nature Neuroscience, 2000
Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance.
M, Riesenhuber, T, Poggio
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Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance.
M, Riesenhuber, T, Poggio
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Current Opinion in Neurobiology, 1991
Progress in the understanding of visual recognition in the past year has been signified by the demonstration of computational feasibility of and psychophysical support for two-dimensional view-interpolation methods.
S, Edelman, T, Poggio
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Progress in the understanding of visual recognition in the past year has been signified by the demonstration of computational feasibility of and psychophysical support for two-dimensional view-interpolation methods.
S, Edelman, T, Poggio
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1995
Abstract The task of recognizing objects using computers has been investigated fairly broadly since the earliest days of research into machine vision. However, very few reliable systems exist; those that actually work do so in very specific environments and adapt poorly to other circumstances.
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Abstract The task of recognizing objects using computers has been investigated fairly broadly since the earliest days of research into machine vision. However, very few reliable systems exist; those that actually work do so in very specific environments and adapt poorly to other circumstances.
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A triboelectric-inductive hybrid tactile sensor for highly accurate object recognition
Nano Energy, 2022Peng Taijiang, Chenyang Xing, Bo Meng
exaly
Neuronal circuitry for recognition memory of object and place in rodent models
Neuroscience and Biobehavioral Reviews, 2022Susanne Nikolaus +2 more
exaly
Fusion of tactile and visual information in deep learning models for object recognition
Information Fusion, 2023Karim Faez
exaly