Zhixian Yan, Dipanjan Chakraborty, et al.
EDBT 2011
In this paper we deal with the problem of detecting and segmenting objects in textured dark-field digital imagery for automated visual-inspection applications. We first present a technique for correcting optical shading effects in conventional dark-field microscopy. After compensating for possible imperfections in the optical setting we address the problem of segmenting objects (defects) in textured dark-field images. The technique that we will follow is based on a sequential application of local operators, which serves the purpose of clustering the object and the background gray levels. This procedure can be considered an extension of average-thresholding-type techniques. Both algorithms for shading correction and object segmentation have fast implementations in general-purpose image-processing pipeline architectures, and therefore they are appealing to real-time computer vision applications. Computational examples showing the appropriateness of the shading-correction procedure as well as the effectiveness of the segmentation wil be discussed. © 1985 Optical Society of America.
Zhixian Yan, Dipanjan Chakraborty, et al.
EDBT 2011
N.C. Narendra, Umesh Bellur, et al.
Middleware 2005
David B. Mayer, Ashford W. Stalnaker
ACM SIGMIS CPR 1967
Eli Schwartz, Leonid Karlinsky, et al.
NeurIPS 2018