8 October 2010 Application research of the synthetic image segmentation algorithm on the multi-lens video logging
Author Affiliations +
Current video logging system adopts a method by placing camera on the bottom of well to acquire the clear bottom hole image, but which can not obtain the clear image because the lens is placed along the hole axis direction.The Multi-lens video logging system which presented by the paper authors obtains image by means of placing multi grin lens along the radius, and only one along the axial. This paper presents an integerated image segmentation algorithm, which can extract useful image information of curtain angle and well depth, and make prepareation for forming the video well logging information fusion map, and supply evidence for logging data interpretation. First, to obtain the approximate boundaries of the image for processing, analysis begins with the image using edge detection algorithms and Canny operator; then aiming at the specification of the image on the bottom hole is primarily a circular region, meanwhile the margin of it is so long, then search the circle edge ;dilation operation is applied to convert it to continuous data, and connect the data together, fill up the edge slot. The edge search function is used to obtain characteristic parameters of extracting image. Finally, using least squares fitting algorithm obtain the circle center and radius, and take the maximum one as the image radius.The standard net mesh is used to calibrate the image, which is acquired through the len of axial direction on the analog well logging device, the integrated image processing method described above is used to process the acquired image of oil well.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Huiqin Jia, Zhouli Li, Weiguang Zhang, "Application research of the synthetic image segmentation algorithm on the multi-lens video logging", Proc. SPIE 7659, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Smart Structures and Materials in Manufacturing and Testing, 765906 (8 October 2010); doi: 10.1117/12.867964; https://doi.org/10.1117/12.867964

Back to Top