Soft Computing Techniques in Vision Science (inbunden)
Inbunden (Hardback)
Antal sidor
2012 ed.
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Patnaik, Srikanta (ed.), Yang, Yeon-Mo (ed.)
50 schwarz-weiße und 46 farbige Abbildungen
XIV, 222 p.
234 x 156 x 14 mm
508 g
Antal komponenter
1 Hardback
Soft Computing Techniques in Vision Science (inbunden)

Soft Computing Techniques in Vision Science

Inbunden Engelska, 2012-02-12
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This Special Edited Volume is a unique approach towards Computational solution for the upcoming field of study called Vision Science. From a scientific firmament Optics, Ophthalmology, and Optical Science has surpassed an Odyssey of optimizing configurations of Optical systems, Surveillance Cameras and other Nano optical devices with the metaphor of Nano Science and Technology. Still these systems are falling short of its computational aspect to achieve the pinnacle of human vision system. In this edited volume much attention has been given to address the coupling issues Computational Science and Vision Studies. It is a comprehensive collection of research works addressing various related areas of Vision Science like Visual Perception and Visual system, Cognitive Psychology, Neuroscience, Psychophysics and Ophthalmology, linguistic relativity, color vision etc. This issue carries some latest developments in the form of research articles and presentations. The volume is rich of contents with technical tools for convenient experimentation in Vision Science. There are 18 research papers having significance in an array of application areas. The volume claims to be an effective compendium of computing developments like Frequent Pattern Mining, Genetic Algorithm, Gabor Filter, Support Vector Machine, Region Based Mask Filter, 4D stereo camera systems, Principal Component Analysis etc. The detailed analysis of the papers can immensely benefit to the researchers of this domain. It can be an Endeavour in the pursuit of adding value in the existing stock of knowledge in Vision Science.
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From the content: Genetic Algorithm Based Fuzzy Frequent Pattern Mining from Gene Expression Data.- Prediction of Protein Tertiary Structure Using Genetic Algorithm.- Hybrid Image mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions.- Handwritten Script Recognition using DCT, Gabor Filter and Wavelet Features at Line Level.- Character Recognition using 2D View and Support Vector Machine.- Automatic localization of pupil using histogram thresholding and region based mask filter.