讲座摘要：
Salient object detection, which aims to identify the most important and conspicuous object regions in an image, has received increasingly more interest in recent years. Salient object detection methods can be categorized as bottom-up stimuli-driven and top-down task-driven approaches. Bottom-up methods are usually based on low-level visual information and are more effective in detecting fine details. In contrast, top-down saliency models are able to detect objects of certain sizes and categories based on more representative features from training samples. In this talk, I would like to introduce Bayesian saliency, Manifold-ranking saliency and Markov-chain saliency which are all Contrast-based methods from the Bottom-up perspective and Bootstrap Learning saliency, Deep Learning saliency which are all Supervised-based methods from the Top-down perspective.
演讲者简介：
Huchuan Lu received both the B. Eng. and M. Eng. degrees in Electronic Engineering from the Department of Electronic Engineering, Dalian University of Technology(DUT), China, in 1995 and 1998 respectively, and the PhD degree in System Engineering, Dalian University of Technology(DUT), China, in 2008. He joined School of Electronic and Information Engineering as faculty member at DUT in 1998, and He has been a Vice Dean and a Professor since 2009 and 2012 respectively, with the School of Information and Communication Engineering, DUT, China. His major research interests include image processing, machine learning, pattern recognition, computer vision. He has widely published at highly-ranked international journals such as IEEE TIP, IEEE TCSVT, IEEE TSMCB, PR, SP, IVC，and leading international conferences such as CVPR, ICCV. He has obtained several honors and awards such as the Most Remembered Poster (ICCV2011), Best Student Paper Award Finalist (ICIP2012) and Best Paper (IET Image Processing 2014). He is serving with more than 20 major international journals and international conferences and workshops. He is an associate editor of IEEE Transactions on Cybernetics, He is a Senior Member of IEEE and Member of ACM.
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