Image-based object recognition in man, monkey and machine. Tarr, M. & Bülthoff, H. Cognition, 67(1-2):1-20, 1998. abstract bibtex Theories of visual object recognition must solve the problem of recognizing 3D objects given that perceivers only receive 2D patterns of light on their retinae. Recent findings from human psychophysics, neurophysiology and machine vision provide converging evidence for 'image-based' models in which objects are represented as collections of viewpoint-specific local features. This approach is contrasted with 'structural-description' models in which objects are represented as configurations of 3D volumes or parts. We then review recent behavioral results that address the biological plausibility of both approaches, a well as some of their computational advantages and limitations. We conclude that, although the image-based approach holds great promise, it has potential pitfalls that may be best overcome by including structural information. Thus, the most viable model of object recognition may be one that incorporates the most appealing aspects of both image-based and structural description theories.
@Article{Tarr1998,
author = {MJ Tarr and HH B\"ulthoff},
journal = {Cognition},
title = {Image-based object recognition in man, monkey and machine.},
year = {1998},
number = {1-2},
pages = {1-20},
volume = {67},
abstract = {Theories of visual object recognition must solve the problem of recognizing
3D objects given that perceivers only receive 2D patterns of light
on their retinae. Recent findings from human psychophysics, neurophysiology
and machine vision provide converging evidence for 'image-based'
models in which objects are represented as collections of viewpoint-specific
local features. This approach is contrasted with 'structural-description'
models in which objects are represented as configurations of 3D volumes
or parts. We then review recent behavioral results that address the
biological plausibility of both approaches, a well as some of their
computational advantages and limitations. We conclude that, although
the image-based approach holds great promise, it has potential pitfalls
that may be best overcome by including structural information. Thus,
the most viable model of object recognition may be one that incorporates
the most appealing aspects of both image-based and structural description
theories.},
keywords = {Computing Methodologies, Human, Language, Learning, Mental Processes, Models, Theoretical, Stochastic Processes, Support, U.S. Gov't, Non-P.H.S., Cognition, Linguistics, Neural Networks (Computer), Practice (Psychology), Non-U.S. Gov't, Memory, Psychological, Task Performance and Analysis, Time Factors, Visual Perception, Adult, Attention, Discrimination Learning, Female, Male, Short-Term, Mental Recall, Orientation, Pattern Recognition, Visual, Perceptual Masking, Reading, Concept Formation, Form Perception, Animals, Corpus Striatum, Shrews, P.H.S., Visual Cortex, Visual Pathways, Acoustic Stimulation, Auditory Cortex, Auditory Perception, Cochlea, Ear, Gerbillinae, Glycine, Hearing, Neurons, Space Perception, Strychnine, Adolescent, Decision Making, Reaction Time, Astrocytoma, Brain Mapping, Brain Neoplasms, Cerebral Cortex, Electric Stimulation, Electrophysiology, Epilepsy, Temporal Lobe, Evoked Potentials, Frontal Lobe, Noise, Parietal Lobe, Scalp, Child, Language Development, Psycholinguistics, Brain, Perception, Speech, Vocalization, Animal, Discrimination (Psychology), Hippocampus, Rats, Calcium, Chelating Agents, Excitatory Postsynaptic Potentials, Glutamic Acid, Guanosine Diphosphate, In Vitro, Neuronal Plasticity, Pyramidal Cells, Receptors, AMPA, Metabotropic Glutamate, N-Methyl-D-Aspartate, Somatosensory Cortex, Synapses, Synaptic Transmission, Thionucleotides, Action Potentials, Calcium Channels, L-Type, Electric Conductivity, Entorhinal Cortex, Neurological, Long-Evans, Infant, Mathematics, Statistics, Probability Learning, Problem Solving, Psychophysics, Association Learning, Child Psychology, Habituation (Psychophysiology), Probability Theory, Analysis of Variance, Semantics, Symbolism, Behavior, Eye Movements, Macaca mulatta, Prefrontal Cortex, Cats, Dogs, Haplorhini, Photic Stimulation, Electroencephalography, Nervous System Physiology, Darkness, Grasshoppers, Light, Membrane Potentials, Neural Inhibition, Afferent, Picrotoxin, Vision, Deoxyglucose, Injections, Microspheres, Neural Pathways, Rhodamines, Choice Behavior, Speech Perception, Verbal Learning, Dominance, Cerebral, Fixation, Ocular, Language Tests, Random Allocation, Comparative Study, Saguinus, Sound Spectrography, Species Specificity, Audiometry, Auditory Threshold, Calibration, Data Interpretation, Statistical, Anesthesia, General, Electrodes, Implanted, Pitch Perception, Sound Localization, Paired-Associate Learning, Serial Learning, Auditory, Age Factors, Motion Perception, Brain Injuries, Computer Simulation, Blindness, Psychomotor Performance, Color Perception, Signal Detection (Psychology), Judgment, ROC Curve, Regression Analysis, Music, Probability, Arm, Cerebrovascular Disorders, Hemiplegia, Movement, Muscle, Skeletal, Myoclonus, Robotics, Magnetoencephalography, Phonetics, Software, Speech Production Measurement, Epilepsies, Partial, Laterality, Stereotaxic Techniques, Germany, Speech Acoustics, Verbal Behavior, Child Development, Instinct, Brain Stem, Coma, Diagnosis, Differential, Hearing Disorders, Hearing Loss, Central, Neuroma, Acoustic, Dendrites, Down-Regulation, Patch-Clamp Techniques, Wistar, Up-Regulation, Aged, Aphasia, Middle Aged, Cones (Retina), Primates, Retina, Retinal Ganglion Cells, Tympanic Membrane, Cell Communication, Extremities, Biological, Motor Activity, Rana catesbeiana, Spinal Cord, Central Nervous System, Motion, Motor Cortex, Intelligence, Macaca fascicularis, Adoption, Critical Period (Psychology), France, Korea, Magnetic Resonance Imaging, Multilingualism, Auditory Pathways, Cochlear Nerve, Loudness Perception, Neural Conduction, Sensory Thresholds, Sound, Language Disorders, Preschool, Generalization (Psychology), Vocabulary, Biophysics, Nerve Net, Potassium Channels, Sodium Channels, Cues, Differential Threshold, Arousal, Newborn, Sucking Behavior, Ferrets, Microelectrodes, Gestalt Theory, Mathematical Computing, Perceptual Closure, Vestibulocochlear Nerve, Brain Damage, Chronic, Regional Blood Flow, Thinking, Tomography, Emission-Computed, Case-Control Studies, Multivariate Analysis, Artificial Intelligence, Depth Perception, 9735534},
}
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Recent findings from human psychophysics, neurophysiology\n\tand machine vision provide converging evidence for 'image-based'\n\tmodels in which objects are represented as collections of viewpoint-specific\n\tlocal features. This approach is contrasted with 'structural-description'\n\tmodels in which objects are represented as configurations of 3D volumes\n\tor parts. We then review recent behavioral results that address the\n\tbiological plausibility of both approaches, a well as some of their\n\tcomputational advantages and limitations. We conclude that, although\n\tthe image-based approach holds great promise, it has potential pitfalls\n\tthat may be best overcome by including structural information. 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