[image 02176] リマインダー： 講演会２(11/28)のご案内

[Akihiro Sugimoto]

皆さま

すでにお知らせしてある下記の講演会が来週の月曜と迫りましたので、リマインダーを差し上げます。

15:30 - 16:30   Ming-Hsuan Yang on  Recent Results on Image Editing and Learning Filters
16:30 - 17:30   Tomas Pajdla on Degereneracies in Rolling Shutter SfM

です。

杉本

> 皆さま
> 
> 下記の講演会を開催しますので、ふるってご参加ください。
> 参加登録、参加費などは不要です。なお、先日ご案内差し上げ
> ましたが、本講演に引き続き、Tomas Pajdla (チェコ工科大）の
> 講演がありますので、両方にご参加いただけますと幸いです。
> 
> よろしくお願いします。
> 
> 杉本
> ------------------------------------------------------------
> 日時:   11月28日(月）　15:30 - 16:30  
> 場所:   国立情報学研究所　　2005 号室(20F)
> 　　　（東京都千代田区一ツ橋2-1-2）
> 　　　　http://www.nii.ac.jp/about/access/　
> 
> 講演者:   Ming-Hsuan Yang (UC Merced, USA)       
>                https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=ja
> 
> タイトル:    Recent Results on Image Editing and Learning Filters
> 
> アブスト:
> In the first part of this talk, I will present recent results on
> sematic-aware image editing. Skies are common backgrounds in photos but are
> often less interesting due to the time of photographing. Professional
> photographers correct this by using sophisticated tools with painstaking
> efforts that are beyond the command of ordinary users. In this work, we
> propose an automatic background replacement algorithm that can generate
> realistic, artifact-free images with diverse styles of skies. The key idea
> of our algorithm is to utilize visual semantics to guide the entire process
> including sky segmentation, search and replacement. First we train a deep
> convolutional neural network for semantic scene parsing, which is used as
> visual prior to segment sky regions in a coarse-to-fine manner. Second, in
> order to find proper skies for replacement, we propose a data-driven sky
> search scheme based on semantic layout of the input image. Finally, to
> re-compose the stylized sky with the original foreground naturally, an
> appearance transfer method is developed to match statistics locally and
> semantically. We show that the proposed algorithm can automatically
> generate a set of visually pleasing results. In addition, we demonstrate
> the effectiveness of the proposed algorithm with extensive user studies.
> 
> In the second part, I will present recent results on learning image filters
> for low-level vision. We formulate numerous low-level vision problems
> (e.g., edge preserving filtering and denoising) as recursive image
> filtering via a hybrid neural network. The network contains several
> spatially variant recurrent neural networks (RNN) as equivalents of a group
> of distinct recursive filters for each pixel, and a deep convolutional
> neural network (CNN) that learns the weights of the RNNs. The deep CNN can
> learn regulations of recurrent propagation for various tasks and
> effectively guides recurrent propagation over an entire image. The proposed
> model does not need large number of convolutional channels nor big kernels
> to learn features for low-level vision filters. It is much smaller and
> faster compared to a deep CNN based image filter. Experimental results show
> that many low-level vision tasks can be effectively learned and carried out
> in real-time by the proposed algorithm.
> 
> 
> 略歴: 
> Ming-Hsuan Yang is an associate professor in Electrical Engineering
> and Computer Science at University of California, Merced. He received the
> PhD degree in Computer Science from the University of Illinois at
> Urbana-Champaign in 2000. He serves as an area chair for several
> conferences including IEEE Conference on Computer Vision and Pattern
> Recognition, IEEE International Conference on Computer Vision, European
> Conference on Computer Vision, Asian Conference on Computer Vision, AAAI National
> Conference on Artificial Intelligence, and IEEE International Conference on
> Automatic Face and Gesture Recognition. He serves as a program co-chair for
> IEEE International Conference on Computer Vision in 2019 as well as Asian
> Conference on Computer Vision in 2014, and general co-chair for Asian
> Conference on Computer Vision in 2016. He serves as an associate editor of
> the IEEE Transactions on Pattern Analysis and Machine Intelligence (2007 to
> 2011), International Journal of Computer Vision, Computer Vision and Image
> Understanding, Image and Vision Computing, and Journal of Artificial
> Intelligence Research. Yang received the Google faculty award in 2009, and
> the Distinguished Early Career Research award from the UC Merced senate in
> 2011, the Faculty Early Career Development (CAREER) award from the National
> Science Foundation in 2012, and the Distinguished Research Award from UC
> Merced Senate in 2015.
> 
> ------------------
> 
> ------------------------------------------------------------
> 日時:   11月28日(月）　16:30 - 17:30  2
> 場所:   国立情報学研究所　　2005 号室(20F)
> 　　　（東京都千代田区一ツ橋2-1-2）
> 　　　　http://www.nii.ac.jp/about/access/　　　
> 
> 講演者:   Tomas Pajdla (Czech Technical University in Prague)
>                https://scholar.google.com/citations?user=gnR4zf8AAAAJ&hl=ja        
> 
> タイトル:    Degereneracies in Rolling Shutter SfM
> 
> アブスト:
> We present the problem of degeneracies in Structure from Motion (SfM) with 
> rolling shutter cameras.  We first show that many common camera configurations, 
> e.g. cameras with parallel readout directions, become critical and allow for a large 
> class of ambiguities in multi-view reconstruction.  Then, we provide mathematical 
> analysis of some multi-view cases and related synthetic experiments and show that 
> bundle adjustment with rolling shutter cameras, which are close to critical configurations, 
> may still produce drastically deformed reconstructions.  Finally, we provide practical 
> recipes how to photograph with rolling shutter cameras to avoid scene deformations 
> in SfM.
>
> ------------------