Matthew Brand
- Phone: 617-621-7500
- Email:
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Position:
Research / Technical Staff
MERL Fellow -
Education:
Ph.D., Northwestern University, 1994 -
Research Areas:
Matt's Quick Links
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Biography
Matt develops and analyzes optimization algorithms for problems in logistics, control, perception, data-mining, and learning. Notable results include methods for parallel solution of quadratic programs, recomposing photos by re-arranging pixels, nonlinear dimensionality reduction, online singular value decomposition, 3D shape-from-video, and learning concise models of data. In addition to academic "best paper" awards, this work has garnered several industrial awards for commercialized technologies.
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Recent News & Events
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NEWS MERL Papers and Workshops at CVPR 2024 Date: June 17, 2024 - June 21, 2024
Where: Seattle, WA
MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & AudioBrief- MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.
CVPR Conference Papers:
1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks
This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.
Paper: https://www.merl.com/publications/TR2024-059
Project page: https://merl.com/research/highlights/TI2V-Zero
2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos
This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.
Paper: https://www.merl.com/publications/TR2024-040
3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee
This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.
Paper: https://www.merl.com/publications/TR2024-042
4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi
Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.
Paper: https://www.merl.com/publications/TR2024-041
5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan
We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.
Paper: https://www.merl.com/publications/TR2024-043
CVPR Workshop Papers:
1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks
This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.
Paper: https://www.merl.com/publications/TR2024-045
2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller
This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.
Paper: https://www.merl.com/publications/TR2024-057
MERL co-organized workshops:
1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum
Workshop link: https://marworkshop.github.io/cvpr24/index.html
2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.
Workshop link: https://fadetrcv.github.io/2024/
3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino
This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.
Paper: https://www.merl.com/publications/TR2024-062
- MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.
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NEWS MERL researchers presenting workshop papers at NeurIPS 2022 Date: December 2, 2022 - December 8, 2022
MERL Contacts: Matthew Brand; Toshiaki Koike-Akino; Jing Liu; Saviz Mowlavi; Kieran Parsons; Ye Wang
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Signal ProcessingBrief- In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.
- “Optimal control of PDEs using physics-informed neural networks” by Saviz Mowlavi and Saleh Nabi
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems, for which the governing PDE is fully known and the goal is to find a control variable that minimizes a desired cost objective. We validate the performance of the PINN framework by comparing it to state-of-the-art adjoint-based optimization, which performs gradient descent on the discretized control variable while satisfying the discretized PDE.
- “Learning with noisy labels using low-dimensional model trajectory” by Vasu Singla, Shuchin Aeron, Toshiaki Koike-Akino, Matthew E. Brand, Kieran Parsons, Ye Wang
Noisy annotations in real-world datasets pose a challenge for training deep neural networks (DNNs), detrimentally impacting generalization performance as incorrect labels may be memorized. In this work, we probe the observations that early stopping and low-dimensional subspace learning can help address this issue. First, we show that a prior method is sensitive to the early stopping hyper-parameter. Second, we investigate the effectiveness of PCA, for approximating the optimization trajectory under noisy label information. We propose to estimate the low-rank subspace through robust and structured variants of PCA, namely Robust PCA, and Sparse PCA. We find that the subspace estimated through these variants can be less sensitive to early stopping, and can outperform PCA to achieve better test error when trained on noisy labels.
- In addition, new MERL researcher, Jing Liu, also presented a paper entitled “CoPur: Certifiably Robust Collaborative Inference via Feature Purification" based on his previous work before joining MERL. His paper was elected as a spotlight paper to be highlighted in lightening talks and featured paper panel.
- In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.
See All News & Events for Matt -
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Research Highlights
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MERL Publications
- "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation", Advances in Neural Information Processing Systems (NeurIPS), December 2024.BibTeX TR2024-157 PDF
- @inproceedings{Chen2024dec,
- author = {Chen, Xiangyu and Wang, Ye and Brand, Matthew and Wang, Pu and Liu, Jing and Koike-Akino, Toshiaki}},
- title = {Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-157}
- }
, - "SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models", British Machine Vision Conference (BMVC), November 2024.BibTeX TR2024-156 PDF
- @inproceedings{Chen2024nov,
- author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew and Wang, Guanghui and Koike-Akino, Toshiaki}},
- title = {SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models},
- booktitle = {British Machine Vision Conference (BMVC)},
- year = 2024,
- month = nov,
- url = {https://www.merl.com/publications/TR2024-156}
- }
, - "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/CVPRW63382.2024.00804, June 2024, pp. 8050-8055.BibTeX TR2024-062 PDF
- @inproceedings{Chen2024jun,
- author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew and Wang, Guanghui and Koike-Akino, Toshiaki}},
- title = {SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2024,
- pages = {8050--8055},
- month = jun,
- publisher = {IEEE},
- doi = {10.1109/CVPRW63382.2024.00804},
- url = {https://www.merl.com/publications/TR2024-062}
- }
, - "SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules", arXiv, March 2024.BibTeX arXiv
- @article{Chen2024mar,
- author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew and Wang, Guanghui and Koike-Akino, Toshiaki},
- title = {SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules},
- journal = {arXiv},
- year = 2024,
- month = mar,
- url = {https://arxiv.org/abs/2403.11887}
- }
, - "G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups", arXiv, February 2024. ,
- "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
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Software & Data Downloads
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Videos
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MERL Issued Patents
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Title: "System and Method for Generating Optimal Lattice Tool Paths"
Inventors: Brand, Matthew E.
Patent No.: 11,392,105
Issue Date: Jul 19, 2022 -
Title: "Machine Learning via Double Layer Optimization"
Inventors: Zhang, Ziming; Brand, Matthew E.
Patent No.: 11,170,301
Issue Date: Nov 9, 2021 -
Title: "Uniform-irradiance extended-source freeforms"
Inventors: Brand, Matthew E.; Birch, Daniel
Patent No.: 10,995,932
Issue Date: May 4, 2021 -
Title: "Methods and Systems for Freeform Irradiance Tailoring for Light Fields"
Inventors: Brand, Matthew E.; Birch, Daniel
Patent No.: 10,837,621
Issue Date: Nov 17, 2020 -
Title: "Compound Optics with Freeform Optical Surface"
Inventors: Brand, Matthew E.
Patent No.: 10,234,689
Issue Date: Mar 19, 2019 -
Title: "Freeform Optical Surface for Producing Sharp-Edged Irradiance Patterns"
Inventors: Brand, Matthew E.
Patent No.: 10,119,679
Issue Date: Nov 6, 2018 -
Title: "Tailored Freeform Optical Surface"
Inventors: Brand, Matthew E.; Aksoylar, Aydan
Patent No.: 9,869,866
Issue Date: Jan 16, 2018 -
Title: "Method for Determining a Sequence for Drilling Holes According to a Pattern using Global and Local Optimization"
Inventors: Garaas, Tyler W; Brand, Matthew E.
Patent No.: 9,703,915
Issue Date: Jul 11, 2017 -
Title: "MPC controller using parallel quadratic programming"
Inventors: Di Cairano, Stefano; Brand, Matthew E.
Patent No.: 9,618,912
Issue Date: Apr 11, 2017 -
Title: "Method for Generating Representations Polylines Using Piecewise Fitted Geometric Primitives"
Inventors: Brand, Matthew E.; Marks, Tim; MV, Rohith
Patent No.: 9,613,443
Issue Date: Apr 4, 2017 -
Title: "Method for Generating Trajectory for Numerical Control Process"
Inventors: Brand, Matthew E.; Agrawal, Amit K.; Erdim, Huseyin
Patent No.: 9,513,623
Issue Date: Dec 6, 2016 -
Title: "System and Method for Planning a Radiation Therapy Treatment"
Inventors: Brand, Matthew E.
Patent No.: 9,251,302
Issue Date: Feb 2, 2016 -
Title: "Method and System for Cutting Features From Sheet Materials With a Laser Cutter According to a Pattern"
Inventors: Garaas, Tyler W.; Brand, Matthew E.; Josef, Cibulka
Patent No.: 9,248,525
Issue Date: Feb 2, 2016 -
Title: "Method for Reconstructing 3D Lines from 2D Lines in an Image"
Inventors: Ramalingam, Srikumar; Brand, Matthew E.
Patent No.: 9,183,635
Issue Date: Nov 10, 2015 -
Title: "Determining Trajectories of Redundant Actuators Jointly Tracking Reference Trajectory"
Inventors: Shilpiekandula, Vijay; Brand, Matthew E.; Srikanth, Manohar; Bortoff, Scott A.
Patent No.: 9,170,580
Issue Date: Oct 27, 2015 -
Title: "System and Method for Controlling Machines According to Pattern of Contours"
Inventors: Brand, Matthew E.
Patent No.: 9,104,192
Issue Date: Aug 11, 2015 -
Title: "Method and System for Detouring Around Features Cut From Sheet Materials with a Laser Cutter According to a Pattern"
Inventors: Garaas, Tyler W.; Brand, Matthew E.
Patent No.: 9,046,888
Issue Date: Jun 2, 2015 -
Title: "Method for Scheduling Cars in Elevator Systems to Minimizes Round-Trip Times"
Inventors: Brand, Matthew E.
Patent No.: 8,950,555
Issue Date: Feb 10, 2015 -
Title: "Method for Performing Image Processing Applications Using Quadratic Programming"
Inventors: Brand, Matthew E.; Chen, Dongui
Patent No.: 8,761,533
Issue Date: Jun 24, 2014 -
Title: "Method for Solving Control Problems"
Inventors: Brand, Matthew E.; Yao, Chen; Shilpiekandula, Vijay
Patent No.: 8,554,343
Issue Date: Oct 8, 2013 -
Title: "Method for Optimization Radiotherapy Particle Beams"
Inventors: Brand, Matthew E.
Patent No.: 8,492,735
Issue Date: Jul 23, 2013 -
Title: "Motion Planning for Elevator Cars Moving Independently in One Elevator Shaft"
Inventors: Brand, Matthew E.
Patent No.: 8,424,651
Issue Date: Apr 23, 2013 -
Title: "Motion Planning for Elevator Cars Moving Independently in One Elevator Shaft"
Inventors: Brand, Matthew E.
Patent No.: 8,424,650
Issue Date: Apr 23, 2013 -
Title: "Content Aware Resizing of Images and Videos"
Inventors: Brand, Matthew E.; Shamir, Ariel; Rubinstein, Michael; Avidan, Shmuel
Patent No.: 8,380,010
Issue Date: Feb 19, 2013 -
Title: "Method and System for Localizing in Urban Environments From Omni-Direction Skyline Images"
Inventors: Ramalingam, Srikumar; Brand, Matthew E.
Patent No.: 8,311,285
Issue Date: Nov 13, 2012 -
Title: "Method for Temporally Editing Video"
Inventors: Brand, Matthew E.
Patent No.: 8,290,298
Issue Date: Oct 16, 2012 -
Title: "Method for Editing Images and Videos"
Inventors: Brand, Matthew E.
Patent No.: 8,290,297
Issue Date: Oct 16, 2012 -
Title: "Method for Determining a Location From Images Acquired of an Environment with an Omni-Directional Camera"
Inventors: Ramalingam, Srikumar; Brand, Matthew E.; Bouaziz, Sofien
Patent No.: 8,249,302
Issue Date: Aug 21, 2012 -
Title: "Method and Apparatus for Touching-Up Images"
Inventors: Brand, Matthew E.; Pletscher, Patrick A.
Patent No.: 8,160,396
Issue Date: Apr 17, 2012 -
Title: "Resource Allocation for Rateless Transmissions"
Inventors: Brand, Matthew E.
Patent No.: 8,155,048
Issue Date: Apr 10, 2012 -
Title: "Method for Routing Packets in Wireless Ad-Hoc Networks withProbabilistic Delay Guarantees"
Inventors: Molisch, Andreas F.; Brand, Matthew E.; Maymounkov, Petar B.
Patent No.: 8,040,810
Issue Date: Oct 18, 2011 -
Title: "Method for Routing Packets in Ad-Hoc Networks with Partial Channel State Information"
Inventors: Molisch, Andreas F.; Brand, Matthew E.
Patent No.: 7,822,029
Issue Date: Oct 26, 2010 -
Title: "Method for Finding Minimal Cost Paths under Uncertainty"
Inventors: Nikolova, Evdokia V.; Brand, Matthew E.
Patent No.: 7,756,021
Issue Date: Jul 13, 2010 -
Title: "Method and System for Determining Instantaneous Peak Power Consumption in Elevator Banks"
Inventors: Brand, Matthew E.; Nikovski, Daniel N.
Patent No.: 7,743,890
Issue Date: Jun 29, 2010 -
Title: "Method for Finding Optimal Paths Using a Stochastic NetworkModel"
Inventors: Mitzenmacher, Michael D.; Brand, Matthew E.; Nikolova, Evdokia V.
Patent No.: 7,573,866
Issue Date: Aug 11, 2009 -
Title: "System and Method for Scheduling Elevator Cars Using Pairwise Delay Minimization"
Inventors: Nikovski, Daniel N.; Brand, Matthew E.; Ebner, Dietmar
Patent No.: 7,546,905
Issue Date: Jun 16, 2009 -
Title: "System and Method for Scheduling Elevator Cars Using Branch-and-Bound"
Inventors: Brand, Matthew E.; Nikovski, Daniel N.; Ebner, Dietmar
Patent No.: 7,484,597
Issue Date: Feb 3, 2009 -
Title: "On-Line Recommender System"
Inventors: Brand, Matthew E.
Patent No.: 7,475,027
Issue Date: Jan 6, 2009 -
Title: "Method for Generating a Low-Dimensional Representation of High-Dimensional Data"
Inventors: Brand, Matthew E.
Patent No.: 7,412,098
Issue Date: Aug 12, 2008 -
Title: "Incremental Singular Value Decomposition of Incomplete Data"
Inventors: Brand, Matthew E.
Patent No.: 7,359,550
Issue Date: Apr 15, 2008 -
Title: "Variable Multilinear Models for Facial Synthesis"
Inventors: Brand, Matthew E.
Patent No.: 7,133,048
Issue Date: Nov 7, 2006 -
Title: "Method and System for Scheduling Cars in Elevator Systems Considering Existing and Future Passengers"
Inventors: Brand, Matthew E.; Nikovski, Daniel N.
Patent No.: 7,014,015
Issue Date: Mar 21, 2006 -
Title: "Method for Determining Poses of Sensors"
Inventors: Brand, Matthew E.
Patent No.: 7,006,944
Issue Date: Feb 28, 2006 -
Title: "Modeling Shapes, Motions, Flexions and Textures of Non-Rigid 3D Objects Directly from Video"
Inventors: Brand, Matthew E.
Patent No.: 7,006,683
Issue Date: Feb 28, 2006 -
Title: "Method for Mapping High-Dimensional Samples to Reduced-Dimensional Manifolds"
Inventors: Brand, Matthew E.
Patent No.: 6,947,042
Issue Date: Sep 20, 2005 -
Title: "Rendering Deformable 3D Models Recovered from Videos"
Inventors: Brand, Matthew E.
Patent No.: 6,873,724
Issue Date: Mar 29, 2005 -
Title: "Analysis, Synthesis and Control of Data Signals with Temporal Textures Using a Linear Dynamic System"
Inventors: Brand, Matthew E.
Patent No.: 6,864,897
Issue Date: Mar 8, 2005 -
Title: "Optimal Parking of Free Cars in Elevator Group Control"
Inventors: Brand, Matthew E.; Nikovski, Daniel N.
Patent No.: 6,808,049
Issue Date: Oct 26, 2004 -
Title: "Method for Generating Realistic Facial Animation Directly from Speech Utilizing Hidden Markov Models"
Inventors: Brand, Matthew E.
Patent No.: 6,735,566
Issue Date: May 11, 2004 -
Title: "Method and System for Dynamic Programming of Elevators for Optimal Group Elevator Control"
Inventors: Brand, Matthew E.; Nikovski, Daniel N.
Patent No.: 6,672,431
Issue Date: Jan 6, 2004 -
Title: "Method for Acquiring Static and Dynamic Super-Resolution Texture Maps from Video"
Inventors: Brand, Matthew E.
Patent No.: 6,650,335
Issue Date: Nov 18, 2003 -
Title: "Method for Designing Optimal Single Pointer Predictive Keyboards and Apparatus Therefore"
Inventors: Brand, Matthew E.
Patent No.: 6,646,572
Issue Date: Nov 11, 2003 -
Title: "Method for Predicting Keystroke Characters on Single Pointer Keyboards and Apparatus Therefore"
Inventors: Brand, Matthew E.
Patent No.: 6,621,424
Issue Date: Sep 16, 2003 -
Title: "Method for Inferring Target Paths from Related Cue Paths"
Inventors: Brand, Matthew E.
Patent No.: 6,459,808
Issue Date: Oct 1, 2002 -
Title: "System for Having Concise Models from a Signal Utilizing a Hidden Markov Model"
Inventors: Brand, Matthew E.
Patent No.: 6,212,510
Issue Date: Apr 3, 2001 -
Title: "Markov Model Discriminator Using Negative Examples"
Inventors: Brand, Matthew E.
Patent No.: 6,112,021
Issue Date: Aug 29, 2000
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Title: "System and Method for Generating Optimal Lattice Tool Paths"