Rochester Section – Western NY Image and SignalProcessing Workshop Nov 18, 2016

Date: 18 November 2016

Rochester, NY

 

The 19th annual Western New York Image and Signal Processing Workshop (WNYISPW) will be held on November 18th, 2016 at the SLA (078) building at Rochester Institute of Technology.  The WNYISPW is a venue for promoting image and signal processing research and for facilitating interaction between academic researchers, industry researchers, and students. The workshop comprises both oral and poster presentations (see attached call for papers).

 

For more information:

Workshop website: http://ewh.ieee.org/r1/rochester/sp/WNYISPW2016.html

Registration: https://meetings.vtools.ieee.org/meeting_registration/register/40768

Paper submission: https://cmt3.research.microsoft.com/WNYISPW2016/Submission/Index

 

Important dates:

Submission of paper/poster:         New deadline!  October 31, 2016 

Early bird registration ends:          November 4, 2016

Conference date:                           November 18, 2016

 

Keynote Speakers

Dr. Jiebo Luo, Department of Computer Science at University of Rochester, "Video and Language"

Abstract:

Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on understanding videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now striving to bridge videos with natural language, which can be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques, including video-language alignment, video captioning, and video emotion analysis.

Bio:

Professor Jiebo Luo joined the University of Rochester (UR) in 2011 after a prolific career of over fifteen years at Kodak Research Laboratories. His research spans computer vision, machine learning, data mining, social media, biomedical informatics, and ubiquitous computing. He has published extensively in these fields with 270+ peer-reviewed papers and 90+ granted US patents. He has been involved in numerous technical conferences, including serving as the program chair of ACM Multimedia 2010, IEEE CVPR 2012, and IEEE ICIP 2017. He has served on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Pattern Recognition, ACM Transactions on Intelligent Systems and Technology (TIST), Machine Vision and Applications, and Journal of Electronic Imaging. He is a Fellow of the SPIE, IEEE, and IAPR. He is a Data Science CoE Distinguished Researcher with the Georgen Institute for Data Science at UR.

Dr. Jason Yosinski, Geometric Intelligence, "A Deeper Understanding of Large Neural Networks".

Abstract:

Deep neural networks have recently been making a bit of a splash, enabling machines to learn to solve problems that had previously been easy for humans but hard for machines, like playing Atari games or identifying lions or jaguars in photos. But how do these neural nets actually work? What do they learn? This turns out to be a surprisingly tricky question to answer — surprising because we built the networks, but tricky because they are so large and have many millions of connections that effect complex computation which is hard to interpret. Trickiness notwithstanding, in this talk we’ll see what we can learn about neural nets by looking at a few examples of networks in action and experiments designed to elucidate network behavior. The combined experiments yield a better understanding of network behavior and capabilities and promise to bolster our ability to apply neural nets as components in real world computer vision systems.

Bio:

Jason Yosinski is a researcher at Geometric Intelligence, where he uses neural networks and machine learning to build better AI. He was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind.

His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. When not doing research, Mr. Yosinski enjoys tricking middle school students into learning math while they play with robots.

 

Invited Industry Partners

Allison Gray, NVIDIA, "Deep Learning with GPUs"; and

Ken Cleveland, Mathworks. "Deep Learning with Matlab".

 

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pdf icon WNYISPW_2016_CFP_extension.pdf

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