In this paper, a kind of computer microscopic urine sediment analyzer is introduced with industry computer as image processor and controller. The system categorizes and recognizes the visible urine sediment components based on the technology of image processing and support vector machine (SVM). Firstly, microscope enlarges the visible components in the urine sediment quantitative analysis board. Then, light signals is transformed as video electrical signals by CCD camera and the image sampling board samples and saves it as files. The system preprocessing the sampled image using different methods including color image transformed gray image, filtering, image sharpening, image enhancing, segmenting visible component, edge tracking and repairing and so on. Moreover, sampled image feature is extracted, trained and classified. Using support vector machine method classifies and counts the urine sediment visible components and gets the number in the unit volume. The system not only realizes urine sediment visible components classifying and recognition, but also describes its feature from morphology. The SVM trains those features and cross validation in order to get the optimal SVM kernel function and parameters. In the end, it classifies tested image according to the model. Experimental results show that this method is provided with the characteristics of method directness, strong robustness and good stability.
Proc. SPIE. 6027, ICO20: Optical Information Processing
KEYWORDS: Target detection, Digital signal processing, Detection and tracking algorithms, Image processing, Field programmable gate arrays, Image analysis, Data processing, Signal processing, Target recognition, Data communications
Template matching is the process of searching the present and the location of a reference image or an object in a scene image. Template matching is a classical problem in a scene analysis: given a reference image of an object, decide whether that object exists in a scene image under analysis, and find its location if it does. The template matching process involves cross-correlating the template with the scene image and computing a measure of similarity between
them to determine the displacement. The conventional matching method used the spatial cross-correlation process which is computationally expensive. Some algorithms are proposed for this speed problem, such as pyramid algorithm, but it still can't reach the real-time for bigger model image. Moreover, the cross-correlation algorithm can't be effective when the object in the image is rotated. Therefore, the conventional algorithms can't be used for practical purpose. In
this paper, an algorithm for a rotation invariant template matching method based on different value circular projection target tracking algorithm is proposed. This algorithm projects the model image as circular and gets the radius and the sum of the same radius pixel value. The sum of the same radius pixel value is invariable for the same image and the any rotated angle image. Therefore, this algorithm has the rotation invariant property. In order to improve the matching speed and get the illumination invariance, the different value method is combined with circular projection algorithm. This method computes the different value between model image radius pixel sum and the scene image radius pixel sum so that it gets the matching result. The pyramid algorithm also is been applied in order to improve the matching speed. The high speed hardware system also is been design in order to meet the real time requirement of target tracking system. The results show that this system has the good rotate invariance and real-time property.