应计算机学院认知与智能计算研究所邀请，香港浸会大学的杨灿博士将来我处进行学术交流，并做学术报告。欢迎广大研究生和教师参加！学术报告具体安排如下：

 

时间：2014/10/17 （周五） 上午10点

地点：12教205教室

 

报告题目：Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

 

报告摘要：

Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios. The software is publicly available at https://sites.google.com/site/eeyangc/software/decolor.

 

报告人：杨灿博士

报告人简介：

杨灿，于2003年和2006年在浙江大学先后获得理学学士学位和硕士学位；2011年博士毕业于香港科技大学电子与计算机工程系；2011-2014为耶鲁大学博士后；现为香港浸会大学助理教授。杨灿博士的主要研究方向在于机器学习和统计理论基础，具有多年的基因数据挖掘与统计分析的经验。曾在国际顶级期刊发表多篇优秀论文；包括American Journal of Human Genetics (影响因子10.9)，PLoS Genetics (影响因子8.1)，IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) and Bioinformatics等。