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, blur...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.展开更多
文摘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.
