- Date: January 12, 2023
Awarded to: William T. Freeman, Thouis R. Jones, and Egon C. Pasztor
Awarded by: IEEE Computer Society
Research Areas: Computer Vision, Machine Learning
Brief - The MERL paper entitled, "Example-Based Super-Resolution" by William T. Freeman, Thouis R. Jones, and Egon C. Pasztor, published in a 2002 issue of IEEE Computer Graphics and Applications, has been awarded a 2021 Test of Time Award by the IEEE Computer Society. This work was done while the principal investigator, Prof. Freeman, was a research scientist at MERL; he is now a Professor of Electrical Engineering and Computer Science at MIT.
This best paper award recognizes regular or special issue papers published by the magazine that have made profound and long-lasting research impacts in bridging the theory and practice of computer graphics. "This paper is an early example of using learning for a low-level vision task and we are very proud of the pioneering work that MERL has done in this area prior to the deep learning revolution," says Anthony Vetro, VP & Director at MERL.
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- Date: January 6, 2021
Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
MERL Contact: Suhas Lohit
Research Areas: Computational Sensing, Computer Vision, Machine Learning
Brief - A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".
The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
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- Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim K. Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
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- Date: April 23, 2019
Awarded to: Teng-yok Lee
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Machine Learning
Brief - MERL researcher Teng-yok Lee has won the Best Visualization Note Award at the PacificVis 2019 conference held in Bangkok Thailand, from April 23-26, 2019. The paper entitled "Space-Time Slicing: Visualizing Object Detector Performance in Driving Video Sequences" presents a visualization method called Space-Time Slicing to assist a human developer in the development of object detectors for driving applications without requiring labeled data. Space-Time Slicing reveals patterns in the detection data that can suggest the presence of false positives and false negatives.
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- Date: November 16, 2018
Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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- Date: September 30, 2015
Awarded to: Mitsubishi Electric Corp.
Research Area: Computer Vision
Brief - Mitsubishi Electric Corp. (MELCO) advertisements based on 3D reconstruction received a Gold medal and a Bronze medal in the Fujisankei Newspaper. "Will I fit?", "He'll fit just fine.", and "Oops, did you think in 3D?".
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- Date: September 2, 2011
Awarded to: Fatih Porikli and Huseyin Ozkan.
Awarded for: "Data Driven Frequency Mapping for Computationally Scalable Object Detection"
Awarded by: IEEE Advanced Video and Signal Based Surveillance (AVSS)
Research Area: Machine Learning
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- Date: June 25, 2011
Awarded to: Paul A. Viola and Michael J. Jones
Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
MERL Contact: Michael J. Jones
Research Area: Machine Learning
Brief - Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).
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- Date: June 1, 2010
Awarded to: Vijay Venkataraman and Fatih Porikli
Awarded for: "RelCom: Relational Combinatorics Features for Rapid Object Detection"
Awarded by: IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS)
Research Area: Machine Learning
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- Date: January 1, 2007
Awarded to: Yuri Ivanov, Christopher Wren, Alexander Sorokin and Ishwinder Kaur
Awarded for: "Visualizing the History of Living Spaces"
Awarded by: IEEE Transactions on Visualization and Computer Graphics, IEEE Visualization Conference (VIS)
Research Area: Computer Vision
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- Date: January 1, 2007
Awarded to: Oncel Tuzel, Fatih Porikli and Peter Meer
Awarded for: "Human Detection via Classification of Riemannian Manifolds"
Awarded by: IEEE Computer Vision and Pattern Recognition (CVPR)
Research Area: Machine Learning
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- Date: January 1, 2006
Awarded to: MERL Staff
Awarded for: The Dome Projector
Awarded by: Nikkan Kogyo Shimbun
Research Area: Computer Vision
Brief - The Dome Projector, which was developed by MERL staff and productized by Mitsubishi Electric, was recognized by Nikkan Kogyo Simbun's Best 10 New Products Prize.
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- Date: September 1, 2004
Awarded to: Ramesh Raskar
Awarded for: Large computer display systems that seamlessly combine images from multiple projectors.
Awarded by: MIT Technology Review
Research Area: Computer Vision
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