基于网络视像的合作学习策略研究

合作学习是一种高效的学习策略。在网络视像中，为了进一步提升合作学习的效果，在合作学习实施前后及实施过程中，必须从内部优化合作学习机制，简化方法程序，从外部创建适合其运作的环境氛囤，并加以逐步推进。本文拟从网络视像合作学习的准备、实施和评价等方面进行系统探讨。

用数码相机和网络视像器拍摄天文照片

从前学习天文摄影，总是从固定摄影开始，拍摄星座、银河，然后学习使用赤道仪，到熟悉了自己的器材后，才拍摄高倍的行星、月球。可是数码摄影渐趋普及以后，情况有了很大的改变。不少人用数码相机和摄像头，拍摄的第一张天文照片竟然是高倍的行星、月球。于是遇到了一连串的问题：怎样才能准确对焦?该怎样设定光圈、快门?怎样叠合照片?怎样做图像处理?哪里可以学习相关的技术?本文会尝试为以上的问题提供答案，并重点介绍使用数码相机和摄像头拍摄高倍行星照片的技术要点，以及有关电脑软件的使用方法等。

用数码相机和网络视像器拍摄天文照片

底片、数码相机、冷冻CCD拍摄的是定格照片，网络视像器拍摄的则是一段AVI格式的短片。它每秒可拍5格、10格或15格等．曝光则约由数十分之一秒到千分之一秒。做影像处理时．会先用软件把AVI短片转作一幅幅BMP格式的定格照片。

3-D visual tracking based on CMAC neural network and Kalman filter

In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. According to the fundamentals of image-based visual servoing(IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.

Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.

Robustness Design for CNN Templates with Performance of Extracting Closed Domain

The cellular neural/nonlinear network （CNN） is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.

Vision-based behavior prediction of ball carrier in basketball matches

A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.

Uncalibrated visual servoing design for competitive networked robots

In this paper,a method with an eye-in-hand configuration is developed to hit targets during visual tracking for the TLS(Tele-Light Saber) game.It is not necessary to calibrate camera parameters and predict the trajectory of the moving object.Firstly,the expression of the image Jacobian matrix for the eye-in-hand configuration is proposed,and then an update law is designed to estimate the image Jacobian online.Furthermore,a control scheme is presented and the Lyapunov method is employed to prove asymptotic convergence of image errors.No assumption for the moving objects is needed.Finally,both simulation and experimental results are shown to support the approach in this paper.