To improve the pertinence and accuracy of grating image barcode detection, a method based on Hough transform and morphological operation about grating image full-barcode correction and extraction is proposed. The basic principles of Canny edge detection, Hough transform and morphological operation are introduced. Non full-barcode can be eliminated after Canny detection and morphological operation. The level correction can be finished by using Hough transform to detect the full-barcode edge lines and get the correction angle. The full-barcode extraction can be achieved after the dilation of correction grating image and the reduction of free defects. The experimental results show that not only the non full-barcodes and free defects can be better filtered, but also we can accurately accomplish the correction and extraction of the full-barcodes.
The Polycrystalline Cubic Boron Nitride (PCBN) cutting tools has have been developed for high speed machining in modern automation manufacture. The machining surface roughness is regarded as an important criterion to assess PCBN cutting tools performance. There are too many problems in conventional detection method. In order to solve that problem, we present a new way that is based on image analysis of machining surface texture to assess surface roughness. The new method is consisted of three steps. It captures surface texture image when machining is finished or pauses. Firstly, RGB histogram is adopted to analyze image pixel information. This means takes advantage of histogram technique and provides more pixel distribution information than gray histogram. Secondly, unsupervised texture segmentation is used based on resonance algorithm. Thirdly, a new estimation parameter E that is the density of surface contour peak is put forward to estimate machining surface roughness.
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