Xu Lizhang

Multi-scale edge detection of rice internal damage based on computer vision

Damage is easy to occur during the process of harvesting, classification, packaging, transporting, processing, preservation and selling etc. and the detection of internal damage is a difficult problem. Rice kernels were classified as those with none, single, double or multiple stress cracks. An image processing algorithm was used to enhance the object and reduce noise in the acquired image. At the same time a machine -vision system was developed to detect different types of stress cracks in rice kernels. None and single stress cracks were the easiest to detect. Careful positioning of the kernel over the lighting aperture was necessary for accurate detection of double and multiple stress cracks. This system provided an average accuracy of approximately 96.5% to none crack, 93.4% to single crack, 84.2%to double cracks and 83.4% to multiple cracks compared to human inspection. The processing time was between 0.45 and 0.12 s/kernel.

Test method research of rape adhesion properties based on image technology

In this paper, the author puts forward a new adhesion properties test method, which define the adhesion rate by the residual adhesion area to measure interface adhesion properties when the traditional method measured by adhesive force cannot work. The residual adhesion area was calculated by matlab language with image technology. Adhesion properties test of the interface between rape mixture materials and screen matrix was carried out to help illuminate this new test method. The results show that the adhesion rate of the interface between rape mixture materials and screen matrix was 9.5% with error less than 2%. It is also show that this method can be used to test adhesion properties effectively.