Flow field visualization is a new visualization technology for intuitive analysis of flow field data. And Oriented Line Integral Convolution （OLIC） algorithm， as a classic texture visualization method， can be used to clearly observe the evolution of flow in the direction of flow field. To optimize the visualization effect， an OLIC algorithm based on information entropy was proposed. Firstly， sparse noise based on information entropy was generated on the basis of the flow field vector data. Then， the slope convolution kernel function was used to convolute the input texture. Finally， the final texture image of OLIC was obtained by calculating the gray value of each pixel in the output texture image. In the proposed algorithm， streamlines were able to be generated in the critical point region and non-critical point region according to the entropy value adaptively. As the critical point region contained important information of the flow field， the dense drawing was selected， while in the non-critical point region sparse drawling was selected. By drawing streamlines with different densities in different regions， the algorithm can save computational cost， and the drawing speed of the proposed algorithm is increased by 18.6% at least compared with that of the ordinary OLIC algorithm. In terms of visualization effect， the proposed algorithm is superior to the ordinary global drawing， and can be used to observe feature regions more carefully.

Fuzzy C-Means (FCM) clustering algorithm is very sensitive to image noise when it is used to image segmentation. As an improvement of FCM, Possibility FCM (PFCM) clustering algorithm can reduce the influence of image noise on image segmentation to some extent. However, since no spatial information of the image is taken into consideration, PFCM can not perform well when the image contains much noise. In order to further improve the segmentation accuracy of PFCM when much noise is present in the image, a new Spatial PFCM (SPFCM) algorithm was proposed by incorporating the spatial information of each pixel into the traditional PFCM algorithm in this paper. Both synthetic and IKONOS images with different kinds of noise were applied, and the segmentation results show that the proposed SPFCM clustering prevails over the FCM, PFCM, FCM-S1 and FCM-S2 visually and quantitatively. When dealing with different image noise, its average segmentation rate is as high as 99.71%, which shows the effectiveness of the proposed algorithm.