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	Reality Sim: A realistic environment for robot simulation platform of humanoid robot (EI CONFERENCE) 会议论文 OAI收割 5th International Conference on Automation, Robotics and Applications, ICARA 2011, December 6, 2011 - December 8, 2011, Wellington, New zealand 作者: Fu Y. 收藏 \| 浏览/下载：38/0 \| 提交时间：2013/03/25 As a virtual training testing and evaluating environment simulation platform becomes a significant component in Soccer Robot project. Nevertheless the simulated environment in a simulation platform usually has a big gap with the realistic world. In order to solve this issue we demonstrate a more realistic simulation system which is called Reality Sim with numerous real images. By this system the computer vision code could be easily tested on simulation platform. For this purpose previously an image database with a large quantity of images recorded by camera pose is built. Furthermore if the camera pose of an image is not included in the database an interpolation algorithm is used to reconstruct a brand-new realistic image of that pose such that a realistic image could be provided on every robot camera pose. Our results show this system effectively simulates a more realistic environment for simulation platform. 2011 IEEE.
	A MLP-PNN neural network for CCD image super-resolution in wavelet packet domain (EI CONFERENCE) 会议论文 OAI收割 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12, 2008 - October 14, 2008, Dalian, China Zhao X.; Fu D.; Zhai L. 收藏 \| 浏览/下载：68/0 \| 提交时间：2013/03/25 Image super-resolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First decompose and reconstruct the image by wavelet packet. Before constructing the image use neural network in place of other rebuilding method to reconstruct the coefficients in the wavelet packet domain. Second probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data in the wavelet packet domain. The network kernel function is optimally determined for this problem by a MLP-PNN (Multi Layer Perceptron - Probabilistic Neural Network) trained on synthetic data. Network parameters dependent on the sequence noise level. This super-sampled image is spatially Altered to correct finite pixel size effects to yield the final high-resolution estimate. This method can decrease the calculation cost and get perfect PSNR. Results are presented showing the quality of the proposed method. 2008 IEEE.
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：44/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.
	Wavelet packet and neural network basis medical image compression (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China Zhao X.; Wei J.; Zhai L. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 It is difficult to get high compression ratio and good reconstructed image by conventional methods we give a new method of compression on medical image. It is to decompose and reconstruct the medical image by wavelet packet. Before the construction the image use neural network in place of other coding method to code the coefficients in the wavelet packet domain. By using the Kohonen's neural network algorithm not only for its vector quantization feature but also for its topological property. This property allows an increase of about 80% for the compression rate. Compared to the JPEG standard this compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30. This method can get big compression ratio and perfect PSNR. Results show that the image can be compressed greatly and the original image can be recovered well. In addition the approach can be realized easily by hardware.
	An image reconstruction algorithm for electrical resistance tomography (ERT) based on regularized general inverse method. 会议论文 OAI收割 2nd International Conference on Image and Graphics, HEFEI, China, August 16-18, 2002 作者: Wei Y(魏颖); Yu HB(于海斌); Wang S(王师) 收藏 \| 浏览/下载：19/0 \| 提交时间：2012/06/06 electrical resistance tomography image reconstruct algorithm regularization general inverse