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	不同光照下的异构人脸图像的融合识别方法学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2008 许力收藏 \| 浏览/下载：73/0 \| 提交时间：2015/09/02 图像处理融合人脸识别异构图像近红外 Image Processing Fusion Face Recognition Different Imagery Infrared
	A new spaceborne compression approach for remote sensing imagery (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China 作者: Xu S. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 After analyzing advantages and disadvantages of these typical encoding methods: SPIHT and VQ a "DWT+MRLE" approach for spaceborne data compression was proposed in this paper. This approach includes two steps: Discrete Wavelet Transform (DWT) and Modified Run Length Encoding (MRLE). The former used CDF9/7 biorthogonal wavelet filters to powerfully get rid of correlation between pixels in imagery. The later put enlightening information into the lowest bit of some key-position transform coefficients. Consequently CDF9/7 and MRLE together make hardware platform remain high real-time capability and help reconstructed images keep good fidelity with PSNR being about 40dB compared with the original ones. Comparison between experimentations on SPOT4's low-spatial-resolution (10m) imagery and Ikonos2's high-spatial-resolution (1m) imagery shows this "DWT+MRLE" method having better performance for remote-sensed imagery especially those of higher resolution. Although inferior to 8:1 Compression Ration (CR) here near 5:1 is greater than France SPOT5's 3:1 and American Ikonos2's 11:2.6 on-board data compression. More important this method having less computational amount is good for spaceborne capability of real time. The consumed time of different image size is also presented in this paper based on TI TMSC6416 DSP chip with 600MHz CPU cycle clock.
	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：42/0 \| 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.