Grain size of forged nickel alloy is an important feature for the mechanical properties of the material. For fully automatic grain size evaluation in images of micrographs it is necessary to detect the boundaries of each grain. This grain boundary detection is influenced directly by artifacts like scratches and twins. Twins can be seen as parallel lines inside one grain, whereas a scratch can be identified as a sequence of collinear line segments that can be spread over the whole image. Both kinds of artifacts introduce artificial boundaries inside grains. To avoid wrong grain size evaluation, it is necessary to remove these artifacts prior to the size evaluation process. For the generation of boundary images various algorithms have been tested. The most stable results were achieved by grayscale reconstruction and a subsequent watershed segmentation. A modified line Hough transform with a third dimension in the Hough accumulator space, describing the distance of the parallel lines, is used to directly detect twins. Scratch detection is done by applying the standard line Hough transform followed by a rule based segment detection along the found Hough lines. The results of these operations give a detection rate of more than 90 percent for twins and more than 50 percent for scratches.
A novel solution for automatic hardwood inspection is presented. A sophisticated multi sensor system is required for reliable results. Our system works on a data stream of more than 50 MByte/Sec in input and up to 100 MByte/Sec inside the processing queue. The algorithm is divided into multiple steps. Along a fixed grid the images are decomposed into small squares. 55 texture- and color features are computed for each square. A Maximum Likelihood classifier assigns each square to one out of 12 defect classes with a recognition rate better than 97%. Depending on the defect type a dedicated threshold operation is performed for segmentation. Threshold levels and the selection of the input channel (RGB + filtered images) is the result of the former classification step. A fast algorithm computes bounding rectangles from blobs. Defect type dependent rules are used to combine rectangles. Two additional fast high resolution 3D measurement systems add board shape and 3D defect information. All defect rectangles are passing an additional plausibility check in the last data fusion process before they are delivered to the optimization computer. To guarantee a short response time, image acquisition and image processing are performed in parallel on parallel computing hardware.