[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromat...[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromatic research on the plant pathological characteristics. The color and texture were taken as the plant disease image characteristic parameter to extract the perimeter, area and the shape of the lesion image, thus carrying out the classification judgment on the disease image. [ Result] C IE1976H IS chorma percentage histogram method was adopted to extract chromaticity characteristic parameters, the process was simple and effective with fast operation speed, eliminating the effect of leaf size and shape. The statistical characteristic parameter of chorma histogram was analyzed to obtain chroma skewness, which could significantly distinguish different symptoms of disease. [ Conclusion] The study suggested that chroma skewness could be adopted as the characteristic parameter to distinguish spotted disease with angular leaf spot.展开更多
In this paper, a new contour following approach is proposed to deal with the broken edge gaps in the binarized images. The new approach makes it possible to skip the gaps by means of taking adaptive steps. Hence the r...In this paper, a new contour following approach is proposed to deal with the broken edge gaps in the binarized images. The new approach makes it possible to skip the gaps by means of taking adaptive steps. Hence the real contours can be found. In this approach, the boundaries are output in forms of polygons which have the advantage that density of sample points is closely related to the curvature of boundaries.展开更多
基金Supported by Natural Science Foundation in Education Department of Henan Province(2008B210001)~~
文摘[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromatic research on the plant pathological characteristics. The color and texture were taken as the plant disease image characteristic parameter to extract the perimeter, area and the shape of the lesion image, thus carrying out the classification judgment on the disease image. [ Result] C IE1976H IS chorma percentage histogram method was adopted to extract chromaticity characteristic parameters, the process was simple and effective with fast operation speed, eliminating the effect of leaf size and shape. The statistical characteristic parameter of chorma histogram was analyzed to obtain chroma skewness, which could significantly distinguish different symptoms of disease. [ Conclusion] The study suggested that chroma skewness could be adopted as the characteristic parameter to distinguish spotted disease with angular leaf spot.
文摘In this paper, a new contour following approach is proposed to deal with the broken edge gaps in the binarized images. The new approach makes it possible to skip the gaps by means of taking adaptive steps. Hence the real contours can be found. In this approach, the boundaries are output in forms of polygons which have the advantage that density of sample points is closely related to the curvature of boundaries.
