A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

Error-space estimation method and simplified algorithm for space target tracking.

To improve space target tracking precision and the stability of mobile optoelectronic tracking equipment, an error-space estimation method based on the Kalman filter is discussed, and a simplified algorithm is presented to reduce calculation cost. Based on an available measurement of a space target without sufficient validity and accuracy, the actual position related to the tracking equipment is decomposed to an earlier offline prediction of the kinetic model method and prediction errors. By regarding prediction errors as the motion of a weak maneuver target, the errors can be estimated more accurately in error space. By synthesizing estimation of the errors and offline prediction, the space target position is obtained with higher accuracy to improve tracking performance.

[Image processing applying in analysis of motion features of cultured cardiac myocyte in rat].

Study of mechanism of medicine actions, by quantitative analysis of cultured cardiac myocyte, is one of the cutting edge researches in myocyte dynamics and molecular biology. The characteristics of cardiac myocyte auto-beating without external stimulation make the research sense. Research of the morphology and cardiac myocyte motion using image analysis can reveal the fundamental mechanism of medical actions, increase the accuracy of medicine filtering, and design the optimal formula of medicine for best medical treatments. A system of hardware and software has been built with complete sets of functions including living cardiac myocyte image acquisition, image processing, motion image analysis, and image recognition. In this paper, theories and approaches are introduced for analysis of living cardiac myocyte motion images and implementing quantitative analysis of cardiac myocyte features. A motion estimation algorithm is used for motion vector detection of particular points and amplitude and frequency detection of a cardiac myocyte. Beatings of cardiac myocytes are sometimes very small. In such case, it is difficult to detect the motion vectors from the particular points in a time sequence of images. For this reason, an image correlation theory is employed to detect the beating frequencies. Active contour algorithm in terms of energy function is proposed to approximate the boundary and detect the changes of edge of myocyte.