Application of Chaos and Fractals to Computer Vision by Michael E. Farmer

By Michael E. Farmer

This publication offers a radical research of the applying of chaos conception and fractal research to laptop imaginative and prescient. the sector of chaos conception has been studied in dynamical actual platforms, and has been very winning in offering computational versions for terribly complicated difficulties starting from climate structures to neural pathway sign propagation. desktop imaginative and prescient researchers have derived motivation for his or her algorithms from biology and physics for a few years as witnessed by way of the optical circulation set of rules, the oscillator version underlying graphical cuts and naturally neural networks. those algorithms are very useful for a vast variety of computing device imaginative and prescient difficulties like movement segmentation, texture research and alter detection.
The contents of this publication contain chapters in organic imaginative and prescient platforms, foundations of chaos and fractals, habit of pictures and picture sequences in part area, mathematical measures for interpreting section area, purposes to pre-attentive imaginative and prescient and functions to post-attentive vision.
This e-book is meant for graduate scholars, top department undergraduates, researchers and practitioners in picture processing and machine imaginative and prescient. The readers will advance a superior realizing of the innovations of chaos thought and their program to computing device imaginative and prescient. Readers should be brought to a brand new state of mind approximately laptop imaginative and prescient difficulties from the viewpoint of advanced dynamical structures. This new method will offer them a deeper realizing of a few of the phenomena found in advanced photograph scenes.

Show description

Read or Download Application of Chaos and Fractals to Computer Vision PDF

Similar computer vision & pattern recognition books

Markov Models for Pattern Recognition: From Theory to Applications

Markov versions are used to resolve tough development acceptance difficulties at the foundation of sequential information as, e. g. , automated speech or handwriting acceptance. This complete creation to the Markov modeling framework describes either the underlying theoretical suggestions of Markov types - overlaying Hidden Markov versions and Markov chain versions - as used for sequential info and offers the ideas essential to construct winning platforms for functional purposes.

Cognitive Systems

Layout of cognitive structures for tips to humans poses an incredible problem to the fields of robotics and synthetic intelligence. The Cognitive platforms for Cognitive guidance (CoSy) undertaking was once prepared to handle the problems of i) theoretical development on layout of cognitive structures ii) equipment for implementation of platforms and iii) empirical stories to additional comprehend the use and interplay with such platforms.

Motion History Images for Action Recognition and Understanding

Human motion research and popularity is a comparatively mature box, but one that is frequently now not good understood by means of scholars and researchers. the massive variety of attainable diversifications in human movement and visual appeal, digicam perspective, and setting, current enormous demanding situations. a few very important and customary difficulties stay unsolved via the pc imaginative and prescient neighborhood.

Data Clustering: Theory, Algorithms, and Applications

Cluster research is an unmonitored procedure that divides a collection of gadgets into homogeneous teams. This booklet starts off with uncomplicated details on cluster research, together with the type of information and the corresponding similarity measures, by means of the presentation of over 50 clustering algorithms in teams in keeping with a few particular baseline methodologies comparable to hierarchical, center-based, and search-based tools.

Additional resources for Application of Chaos and Fractals to Computer Vision

Sample text

Past approaches in artificial intelligence, such as the subsumption architecture, advocated layered approaches to problem solving. Unfortunately, the emergence of high performance low cost computing resources has led more recent researchers to much more monolithic solutions to artificial vision systems [5,33]. It is this integration of both microscopic and macroscopic features of biological vision systems that is an approach uniquely adopted in this book. It is also the unique feature that allows multi-fractal analysis of the phase space trajectories of the image sequences to be applied at a variety of levels of the vision hierarchy to accomplish increasingly complex tasks.

One interesting result of this book is that we will present algorithms to solve a broad range of vision tasks all based on this common theme of chaos theory. Tasks that in the past have been analyzed using significantly different algorithms such as motion segmentation and texture analysis will be processed using the common approach of chaos theory. This text provides the first attempt to develop a fully unified view of computer vision tasks, with the various tasks being manifestations of temporal or spatial chaos, and a common toolset of algorithms are applicable across all of the tasks.

00 per copy per chapter is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers MA 01923, USA. com DEDICATION To my wife Shweta and my son Patrick for their amazing love and support Author Biography Michael Farmer as an Associate Professor in the Departments of Computer Science, Engineering Science and Physics at the University of Michigan in Flint. Prior to joining the University of Michigan, held numerous advanced engineering positions in the industry. S. S. S. D. degree from Michigan State University in Computer Science.

Download PDF sample

Rated 4.81 of 5 – based on 24 votes