报告题目：Visual Information Capture and Encoding
地点：紫金港校区图书信息大楼B楼402室
时间：9月14日下午3点
演讲者： Klaus Mueller
Klaus Mueller教授是美国纽约州立石溪分校计算机系副教授，长期从事可视化、医学图像处理、计算机图形学的研究工作，发表过数十篇IEEE Transactions和IEEE Visualization论文，亦是IEEE Visualization 2009年会大会主席。他的课题组吸引了大批中国学生加入，欢迎有兴趣的老师和同学前来交流。
摘要：The power of visualization is driven by the fact that humans are able to detect patterns much better with visual representations than with textual ones, enabling more complex reasoning processes. However, currently the degree of semantics encoded in these visual representations is quite limited. To this effect, I will discuss our current work on the derivation and use of expressive icons as a form of information encoding. This process involves a dual-domain categorization of textual and visual depictions of objects and phenomena, as can be obtained from large internet databases.
Abstraction in both domains can then generate generalizations in these visual renditions. Further, similar analytical concepts can also be employed to categorize 2-4 dimensional image and volumetric objects via a rich set of feature vectors sufficiently sensitive to detect critical variations. The categorization hierarchy so created represents the foundation of our emerging Knowledge-Assisted Visualization system, KAV-DB.
地点：紫金港校区图书信息大楼B楼402室
时间：9月14日下午3点
演讲者： Klaus Mueller
Klaus Mueller教授是美国纽约州立石溪分校计算机系副教授，长期从事可视化、医学图像处理、计算机图形学的研究工作，发表过数十篇IEEE Transactions和IEEE Visualization论文，亦是IEEE Visualization 2009年会大会主席。他的课题组吸引了大批中国学生加入，欢迎有兴趣的老师和同学前来交流。
摘要：The power of visualization is driven by the fact that humans are able to detect patterns much better with visual representations than with textual ones, enabling more complex reasoning processes. However, currently the degree of semantics encoded in these visual representations is quite limited. To this effect, I will discuss our current work on the derivation and use of expressive icons as a form of information encoding. This process involves a dual-domain categorization of textual and visual depictions of objects and phenomena, as can be obtained from large internet databases.
Abstraction in both domains can then generate generalizations in these visual renditions. Further, similar analytical concepts can also be employed to categorize 2-4 dimensional image and volumetric objects via a rich set of feature vectors sufficiently sensitive to detect critical variations. The categorization hierarchy so created represents the foundation of our emerging Knowledge-Assisted Visualization system, KAV-DB.
