Pattern Recognition and Color Modularity in Mathematics and Art

2020 ◽  
Vol 11 (1) ◽  
pp. 59-70
Author(s):  
Jean Constant
Keyword(s):  
Pattern Recognition ◽  
Visual Communication ◽  
Complex Objects ◽  
Mathematical Visualization ◽  
Pointed Star

Pattern recognition is a useful tool for mathematics, mathematical visualization, and art. After a brief description of Bongard methodology in the field of pattern recognition, the author combines consequential elements of this technique and principles of color modularity to transform a five- to a ten-pointed star polygon, and in doing so attains a rich, persuasive visualization. This process that can be repeated for more complex objects brought to light additional valuable implication for mathematics visualization, visual communication, and pattern recognition practices.

scholarly journals Processing of data from complex objects through pattern recognition methods

2018 ◽  
Vol 2 (1) ◽  
pp. 30-38
Author(s):  
Mariya Ivanova Konsulova - Bakalova
Keyword(s):  
Pattern Recognition ◽  
Complex Objects ◽  
Pattern Recognition Methods ◽  
Unified Method

 In the description of complex objects, we need methods which could reflect the complex interconnections between components and sift out if possible those of them which are substantial for the specific application. It is offered in this publication the pattern recognition methods should be used as a unified method for processing of data from complex objects. The proposed algorithm may be used in the recognition of the condition of objects of various nature. The indicated examples prove the practical applicability of the methodology as they represent the solution of specific practical problems.

scholarly journals Nature and culture in visual communication: Japanese variations on Ludus Naturae

Semiotica ◽  
2016 ◽  
Vol 2016 (213) ◽  
pp. 213-245 ◽  
Author(s):  
Massimo Leone
Keyword(s):  
Pattern Recognition ◽  
Seventeenth Century ◽  
Visual Communication ◽  
Visual Recognition ◽  
Cross Cultural ◽  
Human Beings ◽  
The West ◽  
Visual Patterns ◽  
Nature And Culture ◽  
Ontological Framework

AbstractThe neurophysiology of vision and cognition shapes the way in which human beings visually “read” the environment. A biological instinct, probably selected as adaptive through evolution, pushes them to recognize coherent shapes in chaotic visual patterns and to impute the creation of these shapes to an anthropomorphic agency. In the west as in the east, in Italy as in Japan, human beings have identified faces, bodies, and landscapes in the bizarre chromatic, eidetic, and topologic configurations of stones, clouds, and other natural elements, as though invisible painters and sculptors had depicted the former in the latter. However, culture-specific visual ideologies immediately and deeply mold such cross-cultural instinct of pattern recognition and agency attribution. Giants and mythical monsters are seen in clouds in the west as in the east; both the Italian seventeenth-century naturalist and the Japanese seventeenth-century painter identify figures of animals and plants in stones. And yet, the ways in which they articulate the semantics of this visual recognition, identify its icons, determine its agency, and categorize it in relation to an ontological framework diverge profoundly, according to such exquisitely paths of differentiation that only the study of culture, together with that of nature, can account for.

Tests of a Theory of Human Image Understanding: Part I the Perception of Colored and Partial Objects

1986 ◽  
Vol 30 (3) ◽  
pp. 297-300
Author(s):  
Ginny Ju ◽  
Irving Biederman
Keyword(s):  
Pattern Recognition ◽  
Surface Characteristics ◽  
Image Understanding ◽  
Rapid Identification ◽  
Complex Objects ◽  
Object A ◽  
Line Drawings ◽  
Full Color ◽  
Full Complement ◽  
Human Image

Object recognition can be conceptualized as a process in which the perceptual input is successfully matched with a stored representation of the object. A theory of pattern recognition, Recognition by Components(RBC) assumes that objects are represented as simple volumetric primatives (e.g., bricks, cylinders, etc.) in specifed relations to each other. According to RBC, speeded recognition should be possible from only a few components, as long as those components uniquely identify an object. Neither the full complement of an object's components, nor the object's surface characteristics (e.g., color and texture) need be present for rapid identification. The results from two experiments on the perception of briefly presented objects are offered for supporting the sufficiency of the theory. Line drawings are identified about as rapidly and as accurately as full color slides. Partial objects could be rapidly (though not optimally) identified. Complex objects are more readily identified than simple objects.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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