title: Models of object recognition.
abstract: 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. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.
authors: Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02142, USA.
authors: M
authors: M
authors: Riesenhuber
authors: T
authors: T
authors: Poggio
mesh_terms: D000818:Animals; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D009474:Neurons / Q000166:cytology / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013702:Temporal Lobe / Q000033:anatomy & histology / Q000502:physiology*; D014793:Visual Cortex / Q000033:anatomy & histology / Q000502:physiology*; D014795:Visual Pathways / Q000166:cytology / Q000502:physiology