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浏览/检索结果: 共4条，第1-4条 帮助 限定条件 内容类型：会议论文     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 P2 Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation 会议论文 意大利米兰, 2021年1月10日 - 2021年1月15日 作者:  Hou, Luanxuan;  Cao, Jie;  Zhao, Yuan;  Shen, Haifeng;  Tang, Jian 收藏  |  浏览/下载：16/0  |  提交时间：2021/06/16 Progressive Bi-C3D Pose Grammar for Human Pose Estimation 会议论文 纽约, 2.07-2.12 作者:  Zhou Lu;  Chen Yingying;  Wang Jinqiao;  Lu Hanqing 收藏  |  浏览/下载：10/0  |  提交时间：2021/06/15 Progressive simplification and transmission of building polygons based on Triangle Meshes 会议论文 Proceedings of SPIE-The International Society for Optical Engineering Li H. S.; Wang Y. J.; Guo Q. S.; Han J. F. 收藏  |  浏览/下载：22/0  |  提交时间：2012/06/30 Progressive  Generalization  Transmission  Building  Mesh  Simplification   Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 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.; Liu H. 收藏  |  浏览/下载：28/0  |  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.