已为您找到该学者16条结果 成果回收站
【期刊论文】Image quantification of high-throughput tissue microarray
董军宇, Jiahua Wu*, Junyu Dong, Huiyu Zhou
,-0001,():
-1年11月30日
Tissue microarray (TMA) technology allows rapid visualization of molecular targets in thousands of tissue specimens at a time and provides valuable information on expression of proteins within tissues at a cellular and sub-cellular level. TMA technology overcomes the bottleneck of traditional tissue analysis and allows it to catch up with the rapid advances in lead discovery. Studies using TMA on immunohistochemistry (IHC) can produce a large amount of images for interpretation within a very short time. Manual interpretation does not allow accurate quantitative analysis of staining to be undertaken. Automatic image capture and analysis has been shown to be superior to manual interpretation. The aims of this work is to develop a truly high-throughput and fully automated image capture and analysis system. We develop a robust colour segmentation algorithm using hue-saturation-intensity (HSI) colour space to provide quantification of signal intensity and partitioning of staining on high-throughput TMA. Initial segmentation results and quantification data have been achieved on 16,000 TMA colour images over 23 different tissue types.
image quantification,, colour segmentation,, HSI colour space,, tissue microarray,, immunohistochemistry
-
30浏览
-
0点赞
-
0收藏
-
0分享
-
85下载
-
0
-
引用
董军宇, Peng Jia, Junyu Dong, Lin Qi, Florent Autrusseau
,-0001,():
-1年11月30日
This paper presents a new approach to measure texture directions and estimate illumination tilt angle of 3D surface textures by using mojette transform. Feature vectors are generated from variances of 72 mojette transform projections with different projection angles. The measured texture directions are compared with human perceptual judgement. Furthermore, we estimate illumination tilt angles by minimizing the Euclidean distance of the feature vector between the test image and the training sets. Experimental results show the effectiveness and accuracy of our proposed approach.
-
32浏览
-
0点赞
-
0收藏
-
0分享
-
71下载
-
0
-
引用
【期刊论文】Texture Synthesis by Support Vector Machines
董军宇, Junyu Dong, Yuanxu Duan, Guimei Sun, Lin Qi
,-0001,():
-1年11月30日
We introduce a simple texture synthesis method based on Support Vector Machines (SVM). Although SVM has been effectively used for various pattern recognition tasks, there is no report available on directly applying SVM for texture synthesis. The advantage of using SVM is that the sample can be simply modeled by a linear model and is not required during the synthesis stage. In addition, the method can be further extended to synthesize 3D surface texture or Bidirectional Texture Functions. Our experimental results show that the method can successfully model and synthesize semi or highly structured textures, which can be difficult subjects for previous texture synthesis methods based on parametric models.
-
45浏览
-
0点赞
-
0收藏
-
0分享
-
69下载
-
0
-
引用
董军宇, 刘栓
计算机工程,2007,33(6):253~255,-0001,():
-1年11月30日
从路口视频图像中实时地构建道路背景图像是基于背景减法的车流量检测的前提。由于路口车流信息复杂,图像受外界环境因素影响大,现有的背景估计算法都有不足之处。该文提出了一种改进的频度背景估计算法,通过离散样本图像,实时地构建背景。该方法不仅算法简单、实时性高,而且增强了在背景估计时对光线变化的适应性,提高了背景建模的准确度,可适用于基于视频的路口信号灯的智能控制系统。
背景估计, 智能交通, 平均估计, 频度估计, SVM
-
66浏览
-
0点赞
-
0收藏
-
0分享
-
68下载
-
0
-
引用
【期刊论文】Deformable Template Combining Alignable and Non-alignable Sketches
董军宇, Linjie Zhang, , Haifeng Gong, Tianfu Wu and Junyu Dong
,-0001,():
-1年11月30日
This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations in different images. For slight translations, Wu et al [13] proposed active basis model to capture them, where each sketch is allowed to shift in position and orientation. For larger translations of sketches, [13] assumed that they follow the same distribution as sketches of natural image ensembles, which need not be explicitly modeled. But in fact, for a specified object class, the unaligned sketches follow a totally different distribution from those of natural images. We summarize these sketches by their means in the foreground mask. We treat the mean value in each direction as independent features and fit their marginal distributions on object ensemble and natural image ensemble using Gaussian distribution. The marginal distributions are combined with Active Basis into a joint probability ratio to distinguish foreground object from natural background. Experiments are conducted on 14 object classes, most of which show considerable improvement in ROC.
-
69浏览
-
0点赞
-
0收藏
-
0分享
-
61下载
-
0
-
引用