中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共6条，第1-6条

帮助

条数/页：排序方式：
	DPF-Net: A Dual-Path Progressive Fusion Network for Retinal Vessel Segmentation 期刊论文 OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 卷号: 72, 页码: 17 作者: Li, Jianyong; Gao, Ge; Yang, Lei; Bian, Guibin; Liu, Yanhong \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2023/11/17 Feature extraction Image segmentation Retinal vessels Biomedical imaging Blood vessels Convolutional neural networks Task analysis Deep network progressive fusion strategy retinal vessel segmentation semantic segmentation U-shape network
	Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria 期刊论文 OAI收割 TALANTA, 2022, 卷号: 244, 页码: 6 作者: Liu, Bo; Liu, Kunxiang; Wang, Nan; Ta, Kaiwen; Liang, Peng \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2022/05/26 Progressive generative adversarial network Residual network Raman spectroscopy Optical tweezers Classification Deep-sea microorganism
	Progressive polarization based reflection removal via realistic training data generation 期刊论文 OAI收割 PATTERN RECOGNITION, 2022, 卷号: 124, 页码: 13 作者: Pang, Youxin; Yuan, Mengke; Fu, Qiang; Ren, Peiran; Yan, Dong-Ming \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2022/02/16 Deep learning Reflection removal Polarization Progressive network Convolutional neural networks
	Quantitative characterization of cracking process in oil shale using micro-CT imaging 期刊论文 OAI收割 ARABIAN JOURNAL OF GEOSCIENCES, 2022, 卷号: 15, 期号: 3, 页码: 9 作者: Lin, Chong; He, Jianming; Liu, Yadong; Mao, Jincheng; Li, Xiao \| 收藏 \| 浏览/下载：40/0 \| 提交时间：2022/07/05 Quantitative characterization Micro-CT imaging Progressive cracking Oil shale Fracture network volume
	priority linear coding based opportunistic routing for video streaming in ad hoc networks 会议论文 OAI收割 53rd IEEE Global Communications Conference, GLOBECOM 2010, Miami, FL, 40883 Zhi Li; Limin Sun; Xinyun Zhou; Liqun Li \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2011/03/31 H.264 video streaming OR-PLC mechanism error propagation error protection priorities multihop ad hoc networks priority linear coding-based opportunistic routing progressive decoding progressive encoding video packets video quality ad hoc networks linear codes telecommunication network routing video coding video streaming
	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：41/0 \| 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.