Papers by Keyword: Facet Model

Abstract: The imaging measurement system must be calibrated before application. In calibration procedure, sub-pixel center extraction is the crucial step for the final accuracy. In this paper, a new sub-pixel extraction method is proposed. Edge detection at pixel level is obtained using LOG operator, then sub-pixel edge detection is realized using the Facet model. Finally, the least squares ellipse fitting is operated on the sub-pixel edge to determine the sub-pixel center. Experiment on a series of simulated images indicates that this algorithm is able to realize the center location accuracy of 0.01-0.02 pixel, and meets the requirement of sub-pixel level.

Abstract: Due to tool wear and making errors, calculating the tool path based on the tool theory model has a great deal of influence on the accuracy of surface finishing machining. In order to solve the above problem, a photogrammetry method is used to extract the rotary cutter profile, and then the tool surface is get by letting the fitting profile curve revolves about the cutter axis, finally the cutter contact path is obtained based on the minimal orientation-distance algorithm. Experiments prove the cutter contact path is just on the theory surface of the workpiece. Therefore the accuracy of surface finishing machining will be greatly improved by the method.

Abstract: An efficient and robust star acquisition algorithm based on facet fitting is presented to improve the performance of star sensors. The location of star central pixels can be determined by searching extremum intensity pixels among the point spread function (PSF) of stars, which is well fitted by the cubic facet model. According to extremum theory, the second derivative operators are pre-calculated and the searching process can be completed using convolution operations thrice. Simultaneously, cluster formation is also a time consuming routine, which is accomplished using specific maximum and minimum threshold to speed up it. A variety of experiments are carried out to validate the performance of proposed algorithm, moreover, the performance evaluation index M is presented. The results clearly show that the proposed algorithm makes a great progress than the vector method in time expense and accuracy under intense noise conditions.

Abstract: Circle, line and circular arc are the common basic elements in industrial computed tomography (ICT) image. The algorithm of recognizing such elements is the key to industrial CT image precise vectorization. An industrial CT image vectorization system has been studied, including different recognition methods for these elements. Firstly, based on facet model, the sub-pixel edge of an industrial CT image is extracted. Then, the circles are recognized by an improved algorithm based on probability of existence map, while the lines are recognized with the set intersection algorithm of fitting a straight line, and the circular arcs are recognized by the combination of the perpendicular bisector tracing algorithm and least squares function. Finally, the element parameters are measured according to recognition results, and the drawing exchange file (DXF) is produced and transmitted into the computer aided design (CAD) system to be edited and consummated. The experimental results show that these methods are capable of recognizing graphic elements in industrial CT image with an excellent accuracy, besides, the absolute errors of circles are less than 0.1 mm, the relative errors are less than 0.5%. It can satisfy the industrial CT vectorization requirements of higher precision, rapid speed and non-contact.

Abstract: To improve the edge detecting effect of the pressed character, a novel method based on facet model and topographic structures is proposed. Firstly, the discrete gray scale image is approximated by a bivariate cubic function, then, the concerned features of the continuous function are computed to describe the discrete image. After every pixel in the image has been defined to Peak, Ridge, Saddle, Flat, Ravine, Pit or Hillside, the edge of the character is detected. To reduce the time in computing the coefficients of cubic function, an efficient separable algorithm is presented. In addition, an image enhancement method prior to edge detection is adopted to improve the detecting effect. The tests show that the proposed method is more suitable for complex edge detecting problems than common methods.