Automated recognition of oil drops in images of multiphase dispersions via gradient direction pattern
A novel feature designed to enable the automated recognition of oil drops from other circular objects found in images obtained in-situ of a multiphase dispersion model (oil in water) is described. The conditions inside a stirring tank are highly dynamic; this is reflected in the complexity of the images obtained, which in turn makes the problem of automating the recognition of objects of interest a very difficult one. To the best of our knowledge this is the first reported attempt to achieve fullyautomated recognition of oil drops in this type of images. The proposed feature synthesizes local gradient orientation patterns that are characteristic of the boundary of the oil drops. The feature was tested as part of a supervised recognition framework based on a Bayesian classifier and employing the Hough transform for circles as a pre-selector of objects with circular shapes. By using the proposed feature alone, the classifier obtained a sensitivity value of 85％ and a false-positive reduction of 33％ (in this context a false-positive is an imageartifact with an approximately circular shape that was detected by the Hough transform but that does not correspond to an oil drop).
Image segmentation circle recognition shape recognition pattern classification drop-distribution multiphase dispersion Hough transform
Alfonso Rojas DomInguez Gabriel Corkidi
Laboratorio de Imageries y Vision por Computadora, Instituto de Biotecnologia Universidad Nacional Autonoma de Mexico (UNAM), Apdo. Postal 510-3, Cuernavaca, 62250 Morelos, Mexico
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1223-1227
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）