- 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.
-
- Date: October 15, 2018 - October 19, 2018
Where: CEATEC'18, Makuhari Messe, Tokyo
MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
Brief - MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
-
- Date: February 14, 2018
Where: Tokyo, Japan
MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Optimization, Computer Vision
Brief - New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
-
- Date: June 2, 2018 - June 4, 2018
Where: Newton, Massachusetts (USA)
Research Areas: Control, Computer Vision, Dynamical Systems, Machine Learning, Data Analytics
Brief - Dr. Andrew Knyazev of MERL has accepted an invitation to participate at the 2018 MathWorks Research Summit. The objective of the Research Summit is to provide a forum for leading researchers in academia and industry to explore the latest research and technology results and directions in computation and its use in technology, engineering, and science. The event aims to foster discussion among scientists, engineers, and research faculty about challenges and research opportunities for the respective communities with a particular interest in exploring cross-disciplinary research avenues.
-
- Date: February 15, 2018
Brief - University faculty members are invited to spend part or all of their sabbaticals at MERL, pursuing projects of their own choosing in collaboration with MERL researchers.
To apply, a candidate should identify and contact one or more MERL researchers with whom they would like to collaborate. The applicant and a MERL researcher will jointly prepare a proposal that the researcher will champion internally. Please visit the visiting faculty web page for further details: http://www.merl.com/employment/visiting-faculty.php.
The application deadline for positions starting in Summer/Fall 2018 is February 15, 2018.
-
- Date: October 28, 2017
Where: Venice, Italy
MERL Contact: Tim K. Marks
Research Area: Machine Learning
Brief - MERL Senior Principal Research Scientist Tim K. Marks will give an invited keynote talk at the 2017 IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2017). The workshop will take place On October 28, 2017, at the International Conference on Computer Vision (ICCV 2017) in Venice, Italy.
-
- Date: September 17, 2017 - September 20, 2017
Where: Beijing, China
MERL Contacts: Petros T. Boufounos; Dehong Liu; Hassan Mansour; Huifang Sun; Anthony Vetro
Research Areas: Computer Vision, Computational Sensing, Digital Video
Brief - MERL presented 5 papers at the IEEE International Conference on Image Processing (ICIP), which was held in Beijing, China from September 17-20, 2017. ICIP is a flagship conference of the IEEE Signal Processing Society and approximately 1300 people attended the event. Anthony Vetro served as General Co-chair for the conference.
-
- Date: October 4, 2017 - October 6, 2017
Where: Orange County Convention Center, Orlando, FL
MERL Contacts: Elizabeth Phillips; Jinyun Zhang Brief - Every year, women technologists and the best minds in computing convene to highlight the contributions of women to computing. The Anita Borg Institute co-presents GHC with the Association of Computing Machinery (ACM).
The conference results in collaborative proposals, networking and mentoring for our attendees. Conference presenters are leaders in their respective fields, representing industry, academia and government.
-
- Date: April 27, 2017
Where: Lincoln Laboratory, Massachusetts Institute of Technology
MERL Contact: Tim K. Marks
Research Area: Machine Learning
Brief - MERL researcher Tim K. Marks presented an invited talk as part of the MIT Lincoln Laboratory CORE Seminar Series on Biometrics. The talk was entitled "Robust Real-Time 2D Face Alignment and 3D Head Pose Estimation."
Abstract: Head pose estimation and facial landmark localization are key technologies, with widespread application areas including biometrics and human-computer interfaces. This talk describes two different robust real-time face-processing methods, each using a different modality of input image. The first part of the talk describes our system for 3D head pose estimation and facial landmark localization using a commodity depth sensor. The method is based on a novel 3D Triangular Surface Patch (TSP) descriptor, which is viewpoint-invariant as well as robust to noise and to variations in the data resolution. This descriptor, combined with fast nearest-neighbor lookup and a joint voting scheme, enable our system to handle arbitrary head pose and significant occlusions. The second part of the talk describes our method for face alignment, which is the localization of a set of facial landmark points in a 2D image or video of a face. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a Mixture of Invariant eXperts (MIX), where each expert learns a regression model that is specialized to a different subset of the joint space of pose and expressions. We also present a method to include deformation constraints within the discriminative alignment framework, which makes the algorithm more robust. Both our 3D head pose and 2D face alignment methods outperform the previous results on standard datasets. If permitted, I plan to end the talk with a live demonstration.
-
- Date: April 10, 2017
Where: University of Utah School of Computing
MERL Contact: Tim K. Marks
Research Area: Machine Learning
Brief - MERL researcher Tim K. Marks presented an invited talk at the University of Utah School of Computing, entitled "Action Detection from Video and Robust Real-Time 2D Face Alignment."
Abstract: The first part of the talk describes our multi-stream bi-directional recurrent neural network for action detection from video. In addition to a two-stream convolutional neural network (CNN) on full-frame appearance (images) and motion (optical flow), our system trains two additional streams on appearance and motion that have been cropped to a bounding box from a person tracker. To model long-term temporal dynamics within and between actions, the multi-stream CNN is followed by a bi-directional Long Short-Term Memory (LSTM) layer. Our method outperforms the previous state of the art on two action detection datasets: the MPII Cooking 2 Dataset, and a new MERL Shopping Dataset that we have made available to the community. The second part of the talk describes our method for face alignment, which is the localization of a set of facial landmark points in a 2D image or video of a face. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a Mixture of Invariant eXperts (MIX), where each expert learns a regression model that is specialized to a different subset of the joint space of pose and expressions. We also present a method to include deformation constraints within the discriminative alignment framework, which makes the algorithm more robust. Our face alignment system outperforms the previous results on standard datasets. The talk will end with a live demo of our face alignment system.
-
- Date: March 5, 2017 - March 9, 2017
Where: New Orleans
MERL Contacts: Petros T. Boufounos; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Anthony Vetro; Ye Wang
Research Areas: Computer Vision, Computational Sensing, Digital Video, Information Security, Speech & Audio
Brief - MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
-
- Date: June 27, 2016 - June 30, 2016
Where: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV
MERL Contacts: Michael J. Jones; Tim K. Marks
Research Area: Machine Learning
Brief - MERL researchers in the Computer Vision group presented three papers at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), which had a paper acceptance rate of 29.9%.
-
- Date: December 14, 2015 - December 16, 2015
Where: Las Vegas, NV, USA
Research Area: Machine Learning
Brief - MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.
The talk abstract was: For over four years, at MERL, we have worked on the robot "bin-picking" problem: using a 2D or 3D camera to look into a bin of parts and determine the pose, 3D rotation and translation, of a good candidate to pick up. We have solved the problem several different ways with several different sensors. I will briefly describe the sensors and the algorithms. In the first half of the talk, I will describe the Multi-Flash camera, a 2D camera with 8 flashes, and explain how this inexpensive camera design is used to extract robust geometric features, depth edges and specular edges, from the parts in a cluttered bin. I will present two pose estimation algorithms, (1) Fast directional chamfer matching--a sub-linear time line matching algorithm and (2) specular line reconstruction, for fast and robust pose estimation of parts with different surface characteristics. In the second half of the talk, I will present a voting-based pose estimation algorithm applicable to 3D sensors. We represent three-dimensional objects using a set of oriented point pair features: surface points with normals and boundary points with directions. I will describe a max-margin learning framework to identify discriminative features on the surface of the objects. The algorithm selects and ranks features according to their importance for the specified task which leads to improved accuracy and reduced computational cost.
-
- Date: December 15, 2015
Where: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
MERL Contact: Hassan Mansour
Research Area: Machine Learning
Brief - MERL researcher Andrew Knyazev gave 3 talks at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). The papers were published in IEEE conference proceedings.
-
- Date: October 25, 2015
Where: Large Data Analysis and Visualization (LDAV)
Research Area: Computer Vision
Brief - Teng-Yok Lee served as the poster co-chair for the Large Data Analysis and Visualization (LDAV) workshop at IEEEVis 2015 in Chicago, Oct. 25-30. At IEEEVis there were over 2000 attendees and three highly competitive main subconferences (SciVis, InfoVis, and Visual Analytics and Technology (VAST)).
-
- 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?".
-
- Date: July 13, 2015 - July 17, 2015
Research Area: Machine Learning
Brief - SA group members (M. Liu, S. Lin (intern), S. Ramalingam, O. Tuzel) presented a paper at the Robotics Science and Systems Conference in Rome July 13-17 called 'Layered Interpretation of Street View Images'. The results they reported are now listed as the leader of the benchmark competition sponsored by Daimler. [Note that at that URL ref 2 is from collaboration with Daimler and it uses a FPGA for high speed, whereas MERL result is obtained with desktop computer and GPU.].
-
- Date: February 20, 2015
Research Area: Computer Vision
-
- Date: July 11, 2014
Where: R&D Magazine
Research Area: Computer Vision
Brief - A team with members from MERL, ATC, and Meiden received an R&D 100 award for its work on Mitsubishi Electric's MELFA-3D Vision system for industrial robot arms. This system completely automates bin picking a task for picking up parts that are randomly placed in a bin and aligning their poses for assembly processes.
-
- Date: February 13, 2014
MERL Contact: Matthew Brand Brief - Mitsubishi Electric Corporation announced its development of advanced optimization algorithms and high-speed calculation methods aimed at optimizing the performance of three practical systems: laser-processing machines for high-speed cutting of sheet metal using the shortest possible trajectories, moon probes achieved with minimized fuel consumption, and particle beam therapies for prompt medical treatments.
-
- Date: June 29, 2013
Where: International Conference on 3DTV-Conference
Research Area: Computer Vision
Brief - The paper "Model-Based Vehicle Pose Estimation and Tracking in Videos Using Random Forests" by Hodlmoser, M., Micusik, B., Pollegeys, M., Liu, M-Y. and Kampel, M. was presented at the International Conference on 3DTV-Conference.
-
- Date: June 23, 2013
Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Research Area: Computer Vision
Brief - The papers "Single Image Calibration of Multi-Axial Imaging Systems" by Agrawal, A. and Ramalingam, S., "Joint Geodesic Upsampling of Depth Images" by Liu, M-Y, Tuzel, O. and Taguchi, Y. and "Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes" by Ramalingam, S., Pillai, J.K., Jain, A. and Taguchi, Y. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
-
- Date: June 13, 2013
Where: CIRP Conference on Modeling of Machining Operations (CIRP CMMO)
Research Area: Computer Vision
Brief - The paper "Cutter Workpiece Engagement Calculations for Five-axis Milling using Composite Adaptively Sampled Distance Fields" by Erdim, H. and Sullivan, A. was presented at the CIRP Conference on Modeling of Machining Operations (CIRP CMMO).
-
- Date: May 14, 2013
Where: IEEE International Conference on Robotics & Automation (ICRA)
Research Area: Computer Vision
Brief - The paper "Point-Plane SLAM for Hand-Held 3D Sensors" by Taguchi, Y., Jian, Y-D, Ramalingam, S. and Feng, C. was presented at the IEEE International Conference on Robotics & Automation (ICRA).
-
- Date: April 1, 2013
Where: IEEE Transactions on Pattern Analysis and Machine Intelligence
Research Area: Computer Vision
Brief - The article "Support Vector Shape: A Classifier Based Shape Representation" by Nguyen, H. V. and Porikli, F. was published in IEEE Transactions on Pattern Analysis and Machine Intelligence.
-