Application Research On Technology Of Image Processing For Optical Microscope

With the combination of optical microscope and computer technology, the development trend of optical microscope is automation and intelligentizing which greatly enhance its performance and effectiveness and has an important significance to optical microscope's development and application.According to the future development tendency of optical microscope and the characteristics of microscope's observation,the observed object can be divided into two categories: fixed shape object and non-fixed shape object. The key technologies of image recognition about two categories object are studied in this dissertation. The main contents of this dissertation includes: a multi-focus image fusion algorithm which can extend the high power microscope's depth of field, an single pixel wide enclosing image edge extraction algorithm, an automatic identification and measurement algorithm for fixed shape microscopic image objective and a classification algorithm base on real-time learning pattern which can be applied to identify non-fixed shape microscopic image objective. The effectiveness and applicability of these algorithms was validated through the contrast and performance experiments.The major innovations of the dissertation are summarized as follows:1. A multi-focus image fusion algorithm based on noise adaptive filter and the nearest neighbor's weight is developed to extend the high power microscope's depth of field. This algorithm can effectively reduce the influence of the noise, and has very good performance in different magnifying power objective lens.2. An algorithm based on Canny and grayscale contour line to extract single pixel wide enclosing image edge is proposed. Acquiring the initial edge which have single edge effect by using Canny algorithm. The initial edge's threshold was automatically calculated to reduce false edge and obtain the basic edge for next steps. According to the grayscale neighborhood of the basic edge's end points, the gray value of grayscale contour line was calculated. On the fusion condition of basic edge and grayscale contour line, the closing edge can be create from the endpoints of basic edge. Experimental results indicate that this algorithm can generate enclosing edge efficaciously which can be used to obtain enclosing region and edge measurement.3. Technology of fixed shape object recognition base on SVM-based is studied and realized. According to the fixed shape of objects, using the SVM to training the variety of features to obtain key feature subset and feature parameters for automatic detection's matching template. With the application of matching template to the flatness automatic detection system for Integrated Circuit pins, the accuracy is over 93% and false-acceptance error rate is 0.4. Technology of non-fixed shape object recognition base on real-time learning pattern is studied and realized. The analysis system extracted the features of region which surrounded by enclosing edge. Through the human-computer interaction, it learned the definition of specific enclosing region selected by the user to form classification rule about features and according to these rules to classify the other region. With the application of the metallographic structure contents qualitative analysis system, experimental results indicate that the measurement error of the polyphase structure's contents about metallographic is less than 1% and suitable for different shape metallographic structure.