Coherent optical methods of pattern recognition and scene analysis using the correlation function of invariant image description

1989 ◽  
Vol 10 (5) ◽  
pp. 420-438
Author(s):  
A. A. Vasil'ev ◽  
S. N. Vereskov ◽  
V. V. Dadeshidze ◽  
I. N. Kompanets ◽  
G. A. Lunyakova ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Correlation Function ◽  
Scene Analysis ◽  
Optical Methods ◽  
Image Description

A New Automatic Seamless Image Stitching Algorithm Based on the Gray Value of Edges

2014 ◽  
Vol 496-500 ◽  
pp. 2241-2245
Author(s):  
Jian Li Cao ◽  
Li Xu ◽  
Shan Shan Guo ◽  
De Xiang Ding
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Computer Simulation ◽  
Correlation Function ◽  
Image Stitching ◽  
Mutual Correlation ◽  
Absolute Value ◽  
Least Absolute Value ◽  
Characteristic Value ◽  
Image Edge

The automatic seamless image stitching technology is a new problem in the field of computer vision and pattern recognition. This paper brings forward a new seamless image stitching algorithm based on the mutual correlation which combines the least absolute value method with the gray value information. The gray characteristic value on the image edge is obtained and the minimum of correlation function is regarded as the matching points to realize image stitching, the effect of which in computer simulation is good.

scholarly journals The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

2021 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
J. Gerard Wolff
Keyword(s):  
Pattern Recognition ◽  
Human Learning ◽  
Multiple Alignment ◽  
Scene Analysis ◽  
Syntactic Parsing ◽  
Information Compression ◽  
Levels Of Abstraction ◽  
Theory Of Intelligence ◽  
The Face ◽  
Multiple Levels

This paper aims to describe how pattern recognition and scene analysis may with advantage be viewed from the perspective of the SP system (meaning the SP theory of intelligence and its realisation in the SP computer model (SPCM), both described in an appendix), and the strengths and potential of the system in those areas. In keeping with evidence for the importance of information compression (IC) in human learning, perception, and cognition, IC is central in the structure and workings of the SPCM. Most of that IC is achieved via the powerful concept of SP-multiple-alignment, which is largely responsible for the AI-related versatility of the system. With examples from the SPCM, the paper describes: how syntactic parsing and pattern recognition may be achieved, with corresponding potential for visual parsing and scene analysis; how those processes are robust in the face of errors in input data; how in keeping with what people do, the SP system can “see” things in its data that are not objectively present; the system can recognise things at multiple levels of abstraction and via part-whole hierarchies, and via an integration of the two; the system also has potential for the creation of a 3D construct from pictures of a 3D object from different viewpoints, and for the recognition of 3D entities.

Evaluation of a Fourier-Transform-Based Pattern-Recognition Algorithm for Two-Dimensional Fluorescence Data

1987 ◽  
Vol 41 (3) ◽  
pp. 496-502 ◽  
Author(s):  
Chou-Pong Pau ◽  
Isiah M. Warner
Keyword(s):  
Pattern Recognition ◽  
Correlation Function ◽  
Frequency Domain ◽  
Fourier Transforms ◽  
Recognition Algorithm ◽  
Two Dimensional ◽  
Pattern Recognition Algorithm ◽  
Absolute Value ◽  
The Absolute ◽  
Matching Criteria

A pattern-recognition algorithm for two-dimensional fluorescence data previously reported is critically evaluated. The three spectral matching criteria—sum of the absolute value of the imaginary coefficients of the frequency-domain correlation function, sum of the absolute value of the real-negative coefficients of the frequency-domain correlation function, and the intervector distance between the abbreviated Fourier transforms of two spectra—are calculated. Spectra simulated with a computer as well as data acquired with a video fluorometer are examined. Results indicate that all three parameters are sensitive to changes in peak position, peak width, relative peak height, and intensity of background noises.

Sound Pattern Recognition Using Optical Methods

1977 ◽  
Vol 24 (8) ◽  
pp. 811-825 ◽  
Author(s):  
Fanny Viénot ◽  
Claudine Bainier ◽  
Bernard Carquille ◽  
Michel Guignard
Keyword(s):  
Pattern Recognition ◽  
Optical Methods ◽  
Sound Pattern ◽  
Sound Pattern Recognition
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