2017年3月17日(金)に，MAIS-Projectシンポジウムを開催いたします．興味をお持ちの方はご参加ください．

日時：2017年3月17日(金) 13:00〜16:40
場所：千葉大学西千葉キャンパス　工学系総合研究棟2　2F　コンファレンスルーム（地図）

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13:00	Opening
13:00-13:20	Introduction of MAIS-Project Prof. Yoshitsugu Manabe (Chiba University)
13:20-13:40	Pedestrian Navigation Application Prof. Makoto Ichikawa (Chiba University)
13:40-14:00	Environmental Information Collection System with IoT Associate Prof. Nobuyoshi Komuro (Chiba University)
14:10-15:10	Lab-forming Field and Field-forming Lab Dr. Takeshi Kurata (AIST)
15:20-16:35	Visual Perception and Adaptation in Natural and Unnatural Environments Prof. Michael A. Webster (University of Nevada, Reno)
16:35	Closing

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"Lab-forming Field and Field-forming Lab"
Dr. Takeshi Kurata (AIST)

I present the concept of Lab-Forming Fields (LFF) and Field-Forming Labs (FFL). LFF is to transform real service fields into lab-like places for bringing research methodologies in laboratories to real fields with IoT. FFL is to transform laboratories into real-field-like places for getting subjects’ behavior and experimental results closer and closer to the ones which are supposed to be obtained in the real service fields with VR. Next, I introduce indoor positioning technologies such as PDR (Pedestrian Dead Reckoning) as a key technology for human behavior sensing. Then I conclude this talk by briefly reporting on case studies of service kaizen in a restaurant and a warehouse respectively.


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“Visual perception and adaptation in natural and unnatural environments”
Prof. Michael A. Webster (University of Nevada, Reno)

Processes of adaptation continuously adjust visual sensitivity to match the characteristics of the current environment. These adjustments affect most aspects of perception, inducing large differences in visual experience when the same observer is exposed to different worlds, while similar experiences when different observers are exposed to the same world.
I will explore the consequences of these adaptation effects for understanding how perception and visual performance can vary within both the natural environments we evolved in, and the increasingly artificial and specialized technological environments to which humans are currently exposed. To the extent that we understand the statistics of the environment and how vision adapts to them, the perceptual consequences of adaptation can be modeled and potentially optimized by adapting images to match the observer.