Model-Based Object Recognition from a Complex Binary Imagery Using Genetic Algorithm

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.

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A Model-based Approach for Rigid Object Recognition

18th International Conference on Pattern Recognition (ICPR'06) ◽

10.1109/icpr.2006.103 ◽

2006 ◽

Author(s):

Chee Boon Chong ◽

Tele Tan ◽

Fee Lee Lim

Keyword(s):

Object Recognition ◽

Rigid Object ◽

Model Based

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A Biologically Plausible Computational Model for Auditory Object Recognition

Journal of Neurophysiology ◽

10.1152/jn.90664.2008 ◽

2009 ◽

Vol 101 (1) ◽

pp. 323-331 ◽

Cited By ~ 19

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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