scholarly journals Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics

2005 ◽  
pp. 407-414 ◽  
Author(s):  
Vasileios Zografos ◽  
Bernard F. Buxton
Keyword(s):  
Object Recognition ◽  
Bayesian Statistics ◽  
Affine Invariant ◽  
Invariant Model ◽  
Model Based

Affine invariant model-based object recognition

1990 ◽  
Vol 6 (5) ◽  
pp. 578-589 ◽  
Author(s):  
Y. Lamdan ◽  
J.T. Schwartz ◽  
H.J. Wolfson
Keyword(s):  
Object Recognition ◽  
Affine Invariant ◽  
Invariant Model ◽  
Model Based

Using logic in a model-based approach to object recognition

1992 ◽  
Author(s):  
Suzanne G. W. Dunn ◽  
Michael A. Gennert
Keyword(s):  
Object Recognition ◽  
Model Based

scholarly journals A Biologically Plausible Computational Model for Auditory Object Recognition

2009 ◽  
Vol 101 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Eric Larson ◽  
Cyrus P. Billimoria ◽  
Kamal Sen
Keyword(s):  
Object Recognition ◽  
Spike Train ◽  
Nearest Neighbor ◽  
Primary Auditory Cortex ◽  
Spike Trains ◽  
Coincidence Detection ◽  
Model Based ◽  
Plausible Model ◽  
Field L ◽  
Song Discrimination

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

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