- 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: June 1, 2013
Awarded to: Yuuki Tachioka, Shinji Watanabe, Jonathan Le Roux and John R. Hershey
Awarded for: "Discriminative Methods for Noise Robust Speech Recognition: A CHiME Challenge Benchmark"
Awarded by: International Workshop on Machine Listening in Multisource Environments (CHiME)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The results of the 2nd 'CHiME' Speech Separation and Recognition Challenge are out! The team formed by MELCO researcher Yuuki Tachioka and MERL Speech & Audio team researchers Shinji Watanabe, Jonathan Le Roux and John Hershey obtained the best results in the continuous speech recognition task (Track 2). This very challenging task consisted in recognizing speech corrupted by highly non-stationary noises recorded in a real living room. Our proposal, which also included a simple yet extremely efficient denoising front-end, focused on investigating and developing state-of-the-art automatic speech recognition back-end techniques: feature transformation methods, as well as discriminative training methods for acoustic and language modeling. Our system significantly outperformed other participants. Our code has since been released as an improved baseline for the community to use.
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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: February 1, 2010
Awarded to: Hideya Shibata, Mamoru Kato, Mitsunori Kori and William Yerazunis
Awarded for: "An Automatic Training Data Collection Method for Confidential E-mail Detection"
Awarded by: The Forum on Data Engineering and Information Management (DEIM)
MERL Contact: William S. Yerazunis
Research Area: Data Analytics
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- Date: March 16, 2007
Awarded to: Michael Seltzer
Awarded for: "Likelihood-Maximizing Beamforming for Robust Hands-free Speech Recognition"
Awarded by: IEEE Signal Processing Society
Research Area: Speech & Audio
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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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