Focusing on the problem that the component contours taken by smart machine tool visual system always contain abnormal regions caused by the background interference,a component contour modification method based on image registration was proposed. Firstly,the component template feature point set and the matched feature point set were extracted from the component engineering drawing and the real image. Secondly,the parameters in the affine transformation model were decomposed and analyzed,and a criterion function was established based on area characteristics and edge structure characteristics of feature point sets of both images. Thirdly,an improved genetic algorithm was used to search for affine transformation parameters corresponding to the global maximum similarity between two images. After the image registration, the abnormal contour segments were detected and replaced by calculating the optimal migrated piecewise Hausdorff distance between the template contour point set and the matched contour point set. Experimental results show that the proposed method can detect the abnormal contour segments in matching contour point set with high accuracy and stability,its registration accuracy is 50% higher than that of Square Summation Joint Feature(SSJF)method,and the distance where the modified contour intersects is less than 3 pixels.

Feature representation and metric learning are fundamental problems in person re-identification. In the feature representation, the existing methods cannot describe the pedestrian well for massive variations in viewpoint. In order to solve this problem, the Color Name (CN) feature was combined with the color and texture features. To extract histograms for image features, the image was divided into zones and blocks. In the metric learning, the traditional kernel Local Fisher Discriminant Analysis (kLFDA) method mapped all query images into the same feature space, which disregards the importance of different regions of the query image. For this reason, the features were grouped by region based on the kLFDA, and the importance of different regions of the image was described by the method of Query-Adaptive Late Fusion (QALF). Experimental results on the VIPeR and iLIDS datasets show that the extracted features are superior to the original feature; meanwhile, the improved method of metric learning can effectively increase the accuracy of person re-identification.

How to store, retrieve and manage the remote sensing image data is a hotspot today. The two techniques of the second development of GIS and ArcSDE were analyzed, a solvable scheme using the GIS components based on COM to develop the client application, and using ArcSDE to develop the remote sensing image database was brought forward, and one development instance based on (VC++)6.0 and Oracle9i was presented.