Empirical Evaluation Methods in Computer Vision by Henrik I. Christensen, Jonathon Phillips, H. I. Christensen,

By Henrik I. Christensen, Jonathon Phillips, H. I. Christensen, P. Jonathon Phillips

This article presents accomplished assurance of tools for the empirical assessment of machine imaginative and prescient strategies. the sensible use of computing device imaginative and prescient calls for empirical evaluate to make sure that the final method has a assured functionality. The paintings comprises articles that conceal the layout of experiments for review, diversity photo segmentation, the overview of face acceptance and diffusion equipment, picture matching utilizing correlation tools, and the functionality of scientific snapshot processing algorithms.

Show description

Read Online or Download Empirical Evaluation Methods in Computer Vision PDF

Similar computer vision & pattern recognition books

Pattern Recognition in Soft Computing Paradigm

Development acceptance (PR) contains 3 very important projects: function research, clustering and class. picture research is usually considered as a PR activity. characteristic research is a vital step in designing any necessary PR procedure simply because its effectiveness relies seriously at the set of positive aspects used to gain the method.

Digital Image Processing: PIKS Scientific Inside

A newly up-to-date and revised variation of the vintage creation to electronic picture processingThe Fourth version of electronic photograph Processing presents an entire advent to the sector and contains new info that updates the state-of-the-art. The textual content deals insurance of recent themes and comprises interactive desktop show imaging examples and machine programming routines that illustrate the theoretical content material of the publication.

Emotion Recognition A Pattern Analysis Approach

A well timed booklet containing foundations and present study instructions on emotion popularity through facial features, voice, gesture and biopotential signalsThis ebook offers a finished exam of the study method of alternative modalities of emotion acceptance. Key issues of dialogue contain facial features, voice and biopotential signal-based emotion acceptance.

Extra info for Empirical Evaluation Methods in Computer Vision

Sample text

R. Jain and T. Binford, Ignorance, myopia, and naivite in computer vision systems, CVGIP: Image Understanding, 53(1): 112-117, 1991. 16. A. Jain and D. Zongker, Feature selection - evaluation, application, and small sample performance, IEEE Trans, on PAMI, 19(2): 153-158, 1997. 17. J. R. Thrift, Generating image filters for target recognition by genetic learning IEEE Trans, on PAMI, 16(9): 906-910, 1994. 18. S. Kirkpatrick, C D . Vecchi, Optimization by simulated annealing, Science, 220: 671-680, 1983.

Basic to genetic search is the idea of maintaining a population of chromosomes representing possible solutions to the discrete optimization problem at hand. A cost function, termed the fitness, is associated with the solution candidates. Given an initial population, genetic algorithms use genetic operators to alter chromosomes in the population and create a new generation. The genetic operator crossover involves selecting pairs of solution candidates randomly from the current population and interchanging the solutions at selected configuration sites.

One of the unique features of genetic search is given by the crossover operator. It effectively provides a means of combining locally consistent subsolutions to generate a globally consistent solution. In addition, although discrete gradient-ascent optimization with multiple random starts shares the idea of maintaining a population of alternative solutions, it is the genetic operators that ensure a higher likelihood of global convergence. For these reasons we resort to a genetic search strategy in this work.

Download PDF sample

Rated 4.85 of 5 – based on 34 votes