Teng Qizhi

Triplet circular Hough transform for circle detection

A new method, triplet circular Hough transform, is proposed for circle detection in image processing and pattern recognition. In the method, a curve in an image is first detected. Next, a sequence of three points on the curve are selected, a sequence of parameters (a, b, r) corresponding to the three points are calculated by solving the circle equation of the curve, and two 2-D accumulators A(a, b) and R(a, b) are accumulated with 1 and r, respectively. Then the parameters {(a, b, r)} of the circles fitting the curve are determined from A(a, b) and R(a, b) by searching for the local maximum over A(a, b). Because no computation loops over center (a, b) and/or radius r are needed, the method is faster than the basic and directional gradient methods. It needs also much smaller memory for accumulation.

Learning-based super-dimension (SD) reconstruction of porous media from a single two-dimensional image

The three-dimensional (3D) microscopic pore structure of Reservoir rock directly affects its seepage characteristics and physical properties. A 3D microscopic pore structure can be reconstructed from a single two-dimensional (2D) training image (TI) by using mathematical modeling methods. In this paper, we introduce the concepts of blocks, dictionary and learning into the reconstruction of 3D porous media from the area of example-based super-resolution (SR) reconstruction, and put forward the concept of super-dimension (SD) reconstruction: study the corresponding relations between 2D images and 3D images of real microscopic pore structure of reservoir rock, and use these relations as guidance for the reconstructions of a new 2D image. According to the concept of SD reconstruction, we put forward a new learning-based super-dimension (LBSD) reconstruction algorithm whose basic steps are as follows: (1) Select the training set; (2) build the dictionary; (3) reconstruction. Based on these steps, we did experiments on reconstruction of porous media from a single two-dimensional image. Comprehensive tests show that the reconstructed 3D structure consists with the 3D Micro-CT core sample where the 2D TI is selected from both in morphological characteristics and Statistical characteristics.