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| 1 | Color in machine vision and its application显示文摘Color is the phenomenon of human visual perception and the module of machine vision. Color information is widely used in the areas of virtual reality and human-computer interaction. Color is the product of a visual environment, illumination and the human brain. Research on color information representation and its processing is typically interdisciplinary. Based on our research work on human color perception and machine color vision and its application, we summarized the hotspots of color studies in recent developments and new approaches to color vision, including basic theories and the application of color information in virtual reality, content-based image retrieval, and face recognition. | Linmi Tao Guangyou Xu | 2001 | Chinese Science Bulletin2001，46，17： | 7 |
| 2 | Dual relations in physical and cyber space显示文摘With the rapid development of computer, communication, and sensing technology, our living space has been transformed from physical space into a space shared by physical space and cyberspace. In the light of this fact and based on analyzing the char- acteristics of physical and cyberspace, respectively, this paper proposed that there are dual relations be- tween physical space and cyberspace. Establishing dual relations is realized in the following two processes: the process of information extraction, analysis and structurization from physical space to cyberspace and the process of providing the information services from cyberspace to physical space by means of inferring the intention, state and demand of users, as well. HCI (Human Cyberspace Interaction) in dual space means to establish the dual relations, which embodied the human centered HCI, i.e. the interaction is carried out in the way accustomed to users and without distract- ing their attention. | XU Guangyou TAO Linmi ZHANG David SHI Yuanchun | 2006 | Chinese Science Bulletin2006，51，1： | 4 |
| 3 | Multiple Deep-Belief-Network-Based Spectral-Spatial Classification of Hyperspectral Images显示文摘A deep-learning-based feature extraction has recently been proposed for HyperSpectral Images(HSI)classification. A Deep Belief Network(DBN), as part of deep learning, has been used in HSI classification for deep and abstract feature extraction. However, DBN has to simultaneously deal with hundreds of features from the HSI hyper-cube, which results into complexity and leads to limited feature abstraction and performance in the presence of limited training data. Moreover, a dimensional-reduction-based solution to this issue results in the loss of valuable spectral information, thereby affecting classification performance. To address the issue, this paper presents a Spectral-Adaptive Segmented DBN(SAS-DBN) for spectral-spatial HSI classification that exploits the deep abstract features by segmenting the original spectral bands into small sets/groups of related spectral bands and processing each group separately by using local DBNs. Furthermore, spatial features are also incorporated by first applying hyper-segmentation on the HSI. These results improved data abstraction with reduced complexity and enhanced the performance of HSI classification. Local application of DBN-based feature extraction to each group of bands reduces the computational complexity and results in better feature extraction improving classification accuracy. In general, exploiting spectral features effectively through a segmented-DBN process and spatial features through hyper-segmentation and integration of spectral and spatial features for HSI classification has a major effect on the performance of HSI classification. Experimental evaluation of the proposed technique on well-known HSI standard data sets with different contexts and resolutions establishes the efficacy of the proposed techniques,wherein the results are comparable to several recently proposed HSI classification techniques. | Atif Mughees Linmi Tao | 2019 | Tsinghua Science and Technology2019，24，2： | 3 |
| 4 | Event based dynamic context model for group interaction 显示文摘 | Dai Peng Tao Linmi Xu Guangyou | 2008 | The Official Journal of the Biomedical Fuzzy Systems Association2008，13，2： | 1 |
| 5 | A Chan–Vese Model Based on the Markov Chain for Unsupervised Medical Image Segmentation显示文摘The accurate segmentation of medical images is crucial to medical care and research;however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan–Vese model based on the Markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan–Vese model, we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel level.Our method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is rigid.The performance of our method is assessed by using four Computerized Tomography(CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques. | Quanwei Huang Yuezhi Zhou Linmi Tao Weikang Yu Yaoxue Zhang Li Huo Zuoxiang He | 2021 | Tsinghua Science and Technology2021，26，6： | 1 |
| 6 | Application and the color problem in machine vision 显示文摘 | Tao Linmi Xu Guangyou | 2001 | Scientific Bulletin2001，46，3： | 1 |
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