Generalized optimum receiver for pattern recognition with multiplicative, additive, and nonoverlapping background noise

1998 ◽  
Author(s):  
Bahram Javidi ◽  
Nasser Towghi ◽  
Jian Li
Keyword(s):  
Pattern Recognition ◽  
Background Noise ◽  
Optimum Receiver

Performance of an optimum receiver designed for pattern recognition with nonoverlapping target and scene noise

1995 ◽  
Vol 34 (20) ◽  
pp. 3858 ◽  
Author(s):  
Bahram Javidi ◽  
Amir Fazlollahi ◽  
Peter Willett ◽  
Philippe Réfrégier
Keyword(s):  
Pattern Recognition ◽  
Optimum Receiver

scholarly journals Fast Intensity Adaptation Enhances the Encoding of Sound in Drosophila

2017 ◽  
Author(s):  
Jan Clemens ◽  
Nofar Ozeri-Engelhard ◽  
Mala Murthy
Keyword(s):  
Pattern Recognition ◽  
Computational Model ◽  
Auditory System ◽  
Sensory Neurons ◽  
Background Noise ◽  
Sound Intensity ◽  
Courtship Song ◽  
Sensory Stimuli ◽  
Complex Stimuli ◽  
Mean And Variance

AbstractTo faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus properties that corrupt pattern recognition. Here, we investigate sound intensity adaptation in the Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to wind or gravity, which affect the antenna’s mean position. Song pattern recognition therefore requires adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how they interact. A computational model explains our results using a sequence of subtractive and divisive adaptation modules, interleaved by rectification. These results lay the foundation for identifying the molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.

Spatial Distorted Target Recognition Based on Improved Maximum Average Correlation Height Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1811-1814
Author(s):  
Hong Zhi Liu ◽  
Yu Chen ◽  
Li Qin Zheng
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Experimental Analysis ◽  
Background Noise ◽  
Target Recognition ◽  
Angular Distortion ◽  
Joint Transform Correlator ◽  
Average Correlation ◽  
Different Shapes ◽  
Mach Filter

Spatial distorted target is very hard to be recognized for complexity and variety of targets, which has restricted the development of pattern recognition technology to a great extent. Joint transform correlator is one of the key equipments to detect and recognize distorted targets. The appearance and development of maximum average correlation height (MACH) algorithm is introduced in this paper. Based on the principle the algorithm and experimental analysis, an improved maximum average correlation height algorithm fit for joint transform correlator is proposed, which has powerful capability of suppressing background noise and widening distortion tolerance. Target images with different shapes including scale or angular distortion constitute MACH filter in frequency domain, which is projected to space domain as reference template including varieties of attitude. To show the feasibility of the algorithm, an airplane with angular distortion in sky is processed by MACH filter as an example. Simulation and optical experimental results are given in this paper. The experiments show the angular distortion tolerance can reach up to 15 degrees. The actual effect of the improved MACH filter algorithm is confirmed very well.

Optimum receiver design for pattern recognition with nonoverlapping target and scene noise

1993 ◽  
Vol 18 (19) ◽  
pp. 1660 ◽  
Author(s):  
Bahram Javidi ◽  
Philippe Refregier ◽  
Peter Willett
Keyword(s):  
Pattern Recognition ◽  
Receiver Design ◽  
Optimum Receiver
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