Model based object recognition by robust information fusion

2004 ◽  
Author(s):  
Haifeng Chen ◽  
I. Shimshoni ◽  
P. Meer
Keyword(s):  
Object Recognition ◽  
Information Fusion ◽  
Model Based

Model-based information fusion investigation on fault isolation of subsea systems considering the interaction among subsystems and sensors

2020 ◽  
Vol 67 ◽  
pp. 104267
Author(s):  
Guozheng Song ◽  
Pierluigi Salvo Rossi ◽  
Faisal Khan ◽  
Nicola Paltrinieri ◽  
Ahmad BahooToroody
Keyword(s):  
Information Fusion ◽  
Fault Isolation ◽  
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.

Quantify: An Information Fusion Model Based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness

2019 ◽  
pp. 563-586
Author(s):  
Leonardo Castro Botega ◽  
Allan Cesar Moreira de Oliveira ◽  
Valdir Amancio Pereira Junior ◽  
Jordan Ferreira Saran ◽  
Lucas Zanco Ladeira ◽  
...  
Keyword(s):  
Information Fusion ◽  
Situation Awareness ◽  
Semantic Analysis ◽  
Fusion Model ◽  
Model Based ◽  
Quality Assessments
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