ACADEMIC BACKGROUND Ph.D., Mathematics, August 2003 University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Dissertation Title: Existence and Stability of Standing Pulses in Neural Networks. Ph. D. advisor: Carson Chow M.A., Mathematics, April 2000 University of Pittsburgh, Pittsburgh, Pennsylvania, USA. B.S., Mathematics, July 1990 Heilongjiang University, Harbin, Heilongjiang Province, P. R. China. Summer Program on Methods in Computational Neuroscience Marine Biological Laboratory (MBL), Woods Hole, MA, July 31 – August 27, 2005. Directors of summer program: Prof. G. Bard Ermentrout and Prof. John White.

I. personal data

Image neural style transfer: A review

Due to sharp depth transition, big holes may be found in the novel view that is synthesized by depth-image-based rendering (DIBR). A hole-filling method based on disparity map is proposed. One important aspect of the method is that the disparity map of destination image is used for hole-filling, instead of the depth image of reference image. Firstly, the big hole detection based on disparity map is conducted, and the start point and the end point of the hole are recorded. Then foreground pixels and background pixels are distinguished for hole-dilating according to disparity map, so that areas with matching errors can be determined and eliminated. In addition, parallaxes of pixels in the area with holes and matching errors are changed to new values. Finally, holes are filled with background pixels from reference image according to these new parallaxes. Experimental results show that the quality of the new view after hole-filling is quite well; and geometric distortions are avoided in destination image, in contrast to the virtual view generated by depth-smoothing methods and image inpainting methods. Moreover, this method is easy for hardware implementation.

Hole-filling Based on Disparity Map for DIBR

Estimation of F-Matrix and image rectification by double quaternion

Wide bandwidth is usually required to transmit depth-image-based three-dimensional (3-D) video. An effective way to reduce the bandwidth requirement is converting the progressive 3-D video to an interlaced one. Since the interlaced video cannot be displayed on an end directly, the video must be deinterlaced. A deinterlacing algorithm for depth-image-based 3-D video is proposed. The algorithm first performs deinterlacing on an interlaced reference image and its interlaced depth map by an intrafield interpolation algorithm based on direction. The interpolation algorithm works well in eliminating jagged effect by adaptively selecting the direction of interpolation; it also provides higher peak SNR in comparison with other commonly used algorithms on interpolating interlaced depth map. Then, 3-D image warping is applied to the progressive reference image and its depth map to generate a progressive destination image. Finally, hole-filling is performed on the destination image to get the virtual view. A method to modify the algorithm to facilitate hardware implementation is also discussed. Experimental results show that the proposed deinterlacing algorithm can provide progressive virtual views with quite good image quality and the performance is robust.

Deinterlacing of depth-image-based three-dimensional video for a depth-image-based rendering system

Stereo effect of image converted from planar

It's significant to convert a planar image into a stereo one. In this paper we used a method which needs to segment an image into sub-blocks to realize this kind of conversion; parameters such as random variables were used to control the procedure. We presented two quantitative criterions, cross-entropy and root-mean-square error, to evaluate the stereo effect. Furthermore, the stereo effect that the random variables take on was thoroughly discussed. The results of the experiment show that, (i) when each random variable has the same distribution, different values of these random variables affect little the stereo effect; (ii) when different distributions are applied to the random variables, the cross-entropies or root-mean-square errors are slightly different, which indicate different distributions have a little influence on the stereo effect. Commonly, we recommend normal distribution for better stereo effect.

Image Conversion from Planar into Stereo: Random Variables

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged, and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT (Style Transfer via Transformers) for image style transfer, and an edge loss function that can enhance the content details and avoid generating blurred results due to the excessive rendering of style features. Extensive qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer approaches while alleviating the content leak problem.

pdf/edge-enhanced-image-style-transfer-via-transformers-3lwzenje.pdf

Edge Enhanced Image Style Transfer via Transformers

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Stereo effect of image converted from planar

Image Conversion from Planar into Stereo: Random Variables

Edge Enhanced Image Style Transfer via Transformers