6533b81ffe1ef96bd1278f0f

RESEARCH PRODUCT

Unsupervised low-key image segmentation using curve evolution approach

Jiangyuan MeiHamid Reza KarimiHuijun GaoYulin Si

subject

Active contour modelbusiness.industrySegmentation-based object categorizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationInitializationPattern recognitionImage segmentationImage textureComputer Science::Computer Vision and Pattern RecognitionCurve fittingGamma distributionComputer visionArtificial intelligencebusinessMathematics

description

Low-key images widely exist in imaging-based systems such as space telescopes, medical imaging equipment, machine vision systems. Unsupervised low-key image segmentation is an important process for image analysis or digital measurement in these applications. In this paper, a novel active contour model with the probability density function (PDF) of gamma distribution for image segmentation is proposed. The flexible gamma distribution is used to describe both of the heterogeneous foreground and dark background in a low-key image. Besides, an unsupervised curve initialization method is also designed in this paper, which helps to accelerate the convergence speed of curve evolution. The effectiveness of the proposed algorithm is demonstrated through comparison with the CV model. Finally, an industrial application based on proposed approach is described in this paper.

yearjournalcountryeditionlanguage
2013-02-012013 IEEE International Conference on Mechatronics (ICM)
https://doi.org/10.1109/icmech.2013.6518535