6533b833fe1ef96bd129c049
RESEARCH PRODUCT
SVM approximation for real-time image segmentation by using an improved hyperrectangles-based method
Johel MiteranS. BouillantEl-bay Bourennanesubject
Computer Science::Machine LearningComputer sciencebusiness.industryGaussianCombination algorithmImage processingPattern recognitionImage segmentationDecision ruleMachine learningcomputer.software_genreSupport vector machinesymbols.namesakeSignal ProcessingsymbolsComputer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringField-programmable gate arraybusinesscomputerIndustrial inspectiondescription
A real-time implementation of an approximation of the support vector machine (SVM) decision rule is proposed. This method is based on an improvement of a supervised classification method using hyperrectangles, which is useful for real-time image segmentation. The final decision combines the accuracy of the SVM learning algorithm and the speed of a hyperrectangles-based method. We review the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present the combination algorithm, which consists of rejecting ambiguities in the learning set using SVM decision, before using the learning step of the hyperrectangles-based method. We present results obtained using Gaussian distribution and give an example of image segmentation from an industrial inspection problem. The results are evaluated regarding hardware cost as well as classification performances.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2003-06-01 | Real-Time Imaging |