Edge and texture detection of metal image under high temperature and dynamic solidification condition

The zinc casting is a complicated process with high temperature, high dust content and dynamic solidification. To accurately detect the edge and texture of metal image under this condition, a sub-pixel detection based on gradient entropy and adaptive four-order cubic convolution interpolation （GEAF-CCI） algorithm is proposed. This method mainly involves three procedures. Firstly, the gradient image is generated from the grey images by using gradient operator. Then, a dynamic threshold based on the maximum local gradient entropy （DTMLGE） algorithm is applied to distinguishing the edge and texture pixels from gradient images. Finally, the adaptive four-order cubic convolution interpolation （AF-CCI） algorithm is proposed for interpolating calculation of the target edges and textures according to their variation differences in different directions. The experimental result shows that the proposed algorithm can remove the jag and blur of the edges and textures, improve the edge positioning precision and reduce the false or missing detection rate.

Mean shift texture surface detection based on WT and COM feature image selection

Mean shift is a widely used clustering algorithm in image segmentation. However, the segmenting results are not so good as expected when dealing with the texture surface due to the influence of the textures. Therefore, an approach based on wavelet transform (WT), co-occurrence matrix (COM) and mean shift is proposed in this paper. First, WT and COM are employed to extract the optimal resolution approximation of the original image as feature image. Then, mean shift is successfully used to obtain better detection results. Finally, experiments are done to show this approach is effective.

Identification of Chinese Materia Medicas in Microscopic Powder Images

This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blurry boundaries, overlapping objects, and messy background. Therefore, the object detection must segment the significant microscopic structures from the complex image. The objects are detected in these images using an adaptable interactive method. After identifying the significant microscopic structures, the system identifies 14 features belonging to three main characteristics. These features form a 14-dimensional vector that represents the microscopic structures. The multi-dimensional vector is then analyzed using a feature assignment algorithm that picks the most notable features to construct a decision tree with thresholds. The identification system consists of a coarse classifier based on the decision tree and a fine classifier using similarity measurements to rank the possible results. Tests on 528 images from 24 different kinds of microscopic structures show the system effectiveness and applicability.