Evaluating RLWT Rutting Test of Asphalt Mixtures Based on Industrial Computerized Tomography

To eliminate the effects of image’s light and shade difference when separating and distinguishing the material composition, a method is put forward, namely, ring-type and partitions threshold segmentation. It means setting up different segment threshold for different areas of the same image and then combining these different areas into one image. Furthermore, by analyzing the CT image before and after the RLWT rutting test for the drilling specimen and Marshall specimen and taking the volume of air voids and the angle (alpha) between max main axis and X axis, the differences of two kinds of specimens’ macrotest results were discussed from internal structure distribution. Here, we show that there are differences between macrotest results of two kinds of specimens because of internal air voids and aggregate distribution, which should be considered for compliance testing.

The Volumetric Clipping for Industrial Computerized Tomography Based on CUDA

The fast development of the Graphics Processing Unit’s progammability and parallel computing power provides new solutions for visualization technology. As the carrier of volume date, a cubic box is selected as the bounding box in the improved ray-casting algorithm, whose diagonal vertex coordinates are stored in boxMin and boxMax respectively. By adjusting the two parameters we can change the shape of the bounding box and then choose the interesting region of the object, a CUDA based interactive volumetric clipping tool is finally realised. By changing the shape of bounding box, the axial volumetric clipping and arbitrary cross-section clipping are important ways to help to get the object's internal information. Experiments show this method is simple, efficient and useful especially for the irregular shape defect. In addition, even for the larger volume of data, we can get a high quality, cross-sectional images on a fast speed.

Industrial Computerized Tomography Images Segmentation Based on Cellular Neural Networks

In this paper, we aimed at segmenting industrial computerized tomography images grounded in edge information. Most researchers focused on the edges of binary images using cellular neural network. We have expanded the scope to gray level images. Thus two groups of cellular neural network were designed to obtain closed edges. One was used to convert the gray level images to binary ones, the other to extract edges. In addition, we combined with contrastenhancement. Subsequently, tracing the obtained edges accomplished segmentation. Simulation experiments on a series of engine images demonstrate our methods can not only batch process threshold segmentation without choice threshold, but also extract the fine edges .A comparison to state-of-the-art methods show ours are easy to follow with good results.