Application research of the synthetic image segmentation algorithm on the multi-lens video logging

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.

[1]  Nie Zaiping,et al.  The development of electromagnetic pulse well logging system and its experiment studies , 2001, IEEE Antennas and Propagation Society International Symposium. 2001 Digest. Held in conjunction with: USNC/URSI National Radio Science Meeting (Cat. No.01CH37229).

[2]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Sankar K. Pal,et al.  Image Model, Poisson Distribution and Object Extraction , 1991, Int. J. Pattern Recognit. Artif. Intell..

[4]  Nabih N. Abdelmalek,et al.  Maximum likelihood thresholding based on population mixture models , 1992, Pattern Recognit..

[5]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[6]  P. Daly,et al.  Production and Video Logging In Horizontal Low Permeability Gas Wells , 2007 .

[7]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.