Tim K. Marks

Tim K. Marks
Tim's Quick Links

  • Biography

    Prior to joining MERL's Imaging Group in 2008, Tim did postdoctoral research in robotic Simultaneous Localization and Mapping in collaboration with NASA's Jet Propulsion Laboratory. His research at MERL spans a variety of areas in computer vision and machine learning, including face recognition under variations in pose and lighting, and robotic vision and touch-based registration for industrial automation.

  • Recent News & Events

    •  NEWS    MERL Papers, Workshops, and Talks at ICCV 2025
      Date: October 19, 2025 - October 23, 2025
      Where: Honolulu, HI, USA
      MERL Contacts: Petros T. Boufounos; Anoop Cherian; Toshiaki Koike-Akino; Hassan Mansour; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Pu (Perry) Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing
      Brief
      • MERL researchers presented 3 conference papers and 3 workshop papers, co-organized 2 workshops, and delivered 2 invited talks at the IEEE International Conference on Computer Vision (ICCV) 2025, which was held in Honolulu, HI, USA from October 19-23, 2025. ICCV is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity" by V. Piedade, C. Sidhartha, J. Gaspar, V. M. Govindu, and P. Miraldo. (Highlight Paper)
        Paper: https://www.merl.com/publications/TR2025-146

        2. "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts" by C.-A. Yang, K.-C. Peng, and R. A. Yeh.
        Paper: https://www.merl.com/publications/TR2025-124

        3. "Manual-PA: Learning 3D Part Assembly from Instruction Diagrams" by J. Zhang, A. Cherian, C. Rodriguez-Opazo, W. Deng, and S. Gould.
        Paper: https://www.merl.com/publications/TR2025-139


        MERL Co-Organized Workshops:

        1. "The Workshop on Anomaly Detection with Foundation Models (ADFM)" by K.-C. Peng, Y. Zhao, and A. Aich.
        Workshop link: https://adfmw.github.io/iccv25/

        2. "The 8th International Workshop on Computer Vision for Physiological Measurement (CVPM)" by D. McDuff, W. Wang, S. Stuijk, T. Marks, H. Mansour, V. R. Shenoy.
        Workshop link: https://sstuijk.estue.nl/cvpm/cvpm25/


        MERL Keynote Talks at Workshops:

        1. Tim K. Marks, Keynote Speaker at the Workshop on Computer Vision for Physiological Measurement (CVPM).
        Workshop website: https://vineetrshenoy.github.io/cvpmSeptember2025/

        2. Tim K. Marks, Keynote Speaker at the Workshop on Analysis and Modeling of Faces and Gestures (AMFG).
        Workshop website: https://fulab.sites.northeastern.edu/amfg2025/


        Workshop Papers:

        1. "Joint Training of Image Generator and Detector for Road Defect Detection" by K.-C. Peng.
        paper: https://www.merl.com/publications/TR2025-149

        2. "Radar-Conditioned 3D Bounding Box Diffusion for Indoor Human Perception" by R. Yataka, P. Wang, P.T. Boufounos, and R. Takahashi.
        paper: https://www.merl.com/publications/TR2025-154

        3. "L-GGSC: Learnable Graph-based Gaussian Splatting Compression" by S. Kato, T. Koike-Akino, and T. Fujihashi.
        paper: https://www.merl.com/publications/TR2025-148
    •  
    •  NEWS    MERL Papers and Workshops at CVPR 2025
      Date: June 11, 2025 - June 15, 2025
      Where: Nashville, TN, USA
      MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee

        This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.

        Paper: https://www.merl.com/publications/TR2025-072

        2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.

        This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.

        paper: https://www.merl.com/publications/TR2025-077


        MERL Co-Organized Workshops:

        1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.

        Workshop link: https://marworkshop.github.io/cvpr25/

        2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.

        Workshop link: https://fadetrcv.github.io/2025/


        Workshop Papers:

        1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)

        With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.

        paper: https://www.merl.com/publications/TR2025-074

        2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.

        LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.

        paper: https://www.merl.com/publications/TR2025-078

        3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.

        This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.

        paper: https://www.merl.com/publications/TR2025-058

        4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.

        This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.

        paper: https://www.merl.com/publications/TR2025-076

        5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian

        Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.

        paper: https://www.merl.com/publications/TR2025-073

        6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.

        paper: https://www.merl.com/publications/TR2025-075

        7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

        paper: https://www.merl.com/publications/TR2025-079
    •  

    See All News & Events for Tim

  • Awards

    •  AWARD    MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision
      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.
    •  
    See All Awards for MERL

  • Research Highlights

  • MERL Publications

    •  Shenoy, V., Wu, S., Comas, A., Lohit, S., Mansour, H., Marks, T.K., "Time-Series U-Net with Recurrence for Noise-Robust Imaging Photoplethysmography", IEEE Access, October 2025.
      BibTeX TR2025-145 PDF
      • @article{Shenoy2025oct,
      • author = {Shenoy, Vineet and Wu, Shaoju and Comas, Armand and Lohit, Suhas and Mansour, Hassan and Marks, Tim K.},
      • title = {{Time-Series U-Net with Recurrence for Noise-Robust Imaging Photoplethysmography}},
      • journal = {IEEE Access},
      • year = 2025,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2025-145}
      • }
    •  Hu, Y., Lohit, S., Kamilov, U., Marks, T.K., "Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/​TGRS.2025.3604654, Vol. 63, September 2025.
      BibTeX TR2025-138 PDF
      • @article{Hu2025sep2,
      • author = {Hu, Yuyang and Lohit, Suhas and Kamilov, Ulugbek and Marks, Tim K.},
      • title = {{Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal}},
      • journal = {IEEE Transactions on Geoscience and Remote Sensing},
      • year = 2025,
      • volume = 63,
      • month = sep,
      • doi = {10.1109/TGRS.2025.3604654},
      • issn = {1558-0644},
      • url = {https://www.merl.com/publications/TR2025-138}
      • }
    •  Sawada, N., Miraldo, P., Lohit, S., Marks, T.K., Chatterjee, M., "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations", IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR), DOI: 10.1109/​CVPRW67362.2025.00041, June 2025, pp. 369-379.
      BibTeX TR2025-074 PDF
      • @inproceedings{Sawada2025jun,
      • author = {Sawada, Naoko and Miraldo, Pedro and Lohit, Suhas and Marks, Tim K. and Chatterjee, Moitreya},
      • title = {{FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR)},
      • year = 2025,
      • pages = {369--379},
      • month = jun,
      • doi = {10.1109/CVPRW67362.2025.00041},
      • url = {https://www.merl.com/publications/TR2025-074}
      • }
    •  Shenoy, V., Wu, S., Comas, A., Marks, T.K., Lohit, S., Mansour, H., "Time-Series U-Net with Recurrence for Noise-Robust Imaging Photoplethysmography", arXiv, March 2025.
      BibTeX arXiv
      • @article{Shenoy2025mar2,
      • author = {Shenoy, Vineet and Wu, Shaoju and Comas, Armand and Marks, Tim K. and Lohit, Suhas and Mansour, Hassan},
      • title = {{Time-Series U-Net with Recurrence for Noise-Robust Imaging Photoplethysmography}},
      • journal = {arXiv},
      • year = 2025,
      • month = mar,
      • url = {https://arxiv.org/abs/2503.17351}
      • }
    •  Shenoy, V., Lohit, S., Mansour, H., Chellappa, R., Marks, T.K., "Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models", arXiv, March 2025.
      BibTeX arXiv
      • @article{Shenoy2025mar,
      • author = {Shenoy, Vineet and Lohit, Suhas and Mansour, Hassan and Chellappa, Rama and Marks, Tim K.},
      • title = {{Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models}},
      • journal = {arXiv},
      • year = 2025,
      • month = mar,
      • url = {https://arxiv.org/abs/2503.17269}
      • }
    See All MERL Publications for Tim

  • Other Publications

    •  Tim K Marks, Andrew Howard, Max Bajracharya, Garrison W Cottrell and Larry H Matthies, "Gamma-SLAM: Visual SLAM in unstructured environments using variance grid maps", Journal of Field Robotics, Vol. 26, No. 1, pp. 26-51, 2009.
      BibTeX
      • @Article{marks2009gamma,
      • author = {Marks, Tim K and Howard, Andrew and Bajracharya, Max and Cottrell, Garrison W and Matthies, Larry H},
      • title = {Gamma-SLAM: Visual SLAM in unstructured environments using variance grid maps},
      • journal = {Journal of Field Robotics},
      • year = 2009,
      • volume = 26,
      • number = 1,
      • pages = {26--51},
      • publisher = {Wiley Online Library}
      • }
    •  Luke Barrington, Tim K Marks, Janet Hui-wen Hsiao and Garrison W Cottrell, "NIMBLE: A kernel density model of saccade-based visual memory", Journal of Vision, Vol. 8, No. 14, 2008.
      BibTeX
      • @Article{barrington2008nimble,
      • author = {Barrington, Luke and Marks, Tim K and Hsiao, Janet Hui-wen and Cottrell, Garrison W},
      • title = {NIMBLE: A kernel density model of saccade-based visual memory},
      • journal = {Journal of Vision},
      • year = 2008,
      • volume = 8,
      • number = 14,
      • publisher = {Association for Research in Vision and Ophthalmology}
      • }
    •  Tim K Marks, Andrew Howard, Max Bajracharya, Garrison W Cottrell and Larry Matthies, "Gamma-SLAM: Using stereo vision and variance grid maps for SLAM in unstructured environments", Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, 2008, pp. 3717-3724.
      BibTeX
      • @Inproceedings{marks2008gamma,
      • author = {Marks, Tim K and Howard, Andrew and Bajracharya, Max and Cottrell, Garrison W and Matthies, Larry},
      • title = {Gamma-SLAM: Using stereo vision and variance grid maps for SLAM in unstructured environments},
      • booktitle = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on},
      • year = 2008,
      • pages = {3717--3724},
      • organization = {IEEE}
      • }
    •  Lingyun Zhang, Matthew H Tong, Tim K Marks, Honghao Shan and Garrison W Cottrell, "SUN: A Bayesian framework for saliency using natural statistics", Journal of Vision, Vol. 8, No. 7, 2008.
      BibTeX
      • @Article{zhang2008sun,
      • author = {Zhang, Lingyun and Tong, Matthew H and Marks, Tim K and Shan, Honghao and Cottrell, Garrison W},
      • title = {SUN: A Bayesian framework for saliency using natural statistics},
      • journal = {Journal of Vision},
      • year = 2008,
      • volume = 8,
      • number = 7,
      • publisher = {Association for Research in Vision and Ophthalmology}
      • }
    •  Tim K Marks, Andrew Howard, Max Bajracharya, Garrison W Cottrell and Larry Matthies, "Gamma-SLAM: Stereo visual SLAM in unstructured environments using variance grid maps", IROS visual SLAM workshop, 2007.
      BibTeX
      • @Article{marks2007gamma,
      • author = {Marks, Tim K and Howard, Andrew and Bajracharya, Max and Cottrell, Garrison W and Matthies, Larry},
      • title = {Gamma-SLAM: Stereo visual SLAM in unstructured environments using variance grid maps},
      • journal = {IROS visual SLAM workshop},
      • year = 2007,
      • publisher = {Citeseer}
      • }
    •  Tim K Marks, John Hershey, J Cooper Roddey and Javier R Movellan, "Joint tracking of pose, expression, and texture using conditionally Gaussian filters", Advances in neural information processing systems, Vol. 17, pp. 889-896, 2005.
      BibTeX
      • @Article{marks2005joint,
      • author = {Marks, Tim K and Hershey, John and Roddey, J Cooper and Movellan, Javier R},
      • title = {Joint tracking of pose, expression, and texture using conditionally Gaussian filters},
      • journal = {Advances in neural information processing systems},
      • year = 2005,
      • volume = 17,
      • pages = {889--896}
      • }
    •  Tim K Marks, John Hershey, J Cooper Roddey and Javier R Movellan, "3d tracking of morphable objects using conditionally gaussian nonlinear filters", Computer Vision and Pattern Recognition Workshop, 2004. CVPRW'04. Conference on, 2004, pp. 190-190.
      BibTeX
      • @Inproceedings{marks20043d,
      • author = {Marks, Tim K and Hershey, John and Roddey, J Cooper and Movellan, Javier R},
      • title = {3d tracking of morphable objects using conditionally gaussian nonlinear filters},
      • booktitle = {Computer Vision and Pattern Recognition Workshop, 2004. CVPRW'04. Conference on},
      • year = 2004,
      • pages = {190--190},
      • organization = {IEEE}
      • }
    •  Tim K Marks and Javier R Movellan, "Diffusion networks, products of experts, and factor analysis", Proc. Int. Conf. on Independent Component Analysis, pp. 481-485, 2001.
      BibTeX
      • @Article{marks2001diffusion,
      • author = {Marks, Tim K and Movellan, Javier R},
      • title = {Diffusion networks, products of experts, and factor analysis},
      • journal = {Proc. Int. Conf. on Independent Component Analysis},
      • year = 2001,
      • pages = {481--485},
      • publisher = {Citeseer}
      • }

  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "Discriminative 3D Shape Modeling for Few-Shot Instance Segmentation"
      Inventors: Cherian, Anoop; Sullivan, Alan; Marks, Tim
      Patent No.: 12,406,374
      Issue Date: Sep 2, 2025
    • Title: "System and Method for Remote Measurements of Vital Signs of a Person in a Volatile Environment"
      Inventors: Marks, Tim; Mansour, Hassan; Nowara, Ewa; Nakamura, Yudai; Veeraraghavan, Ashok N.
      Patent No.: 12,056,879
      Issue Date: Aug 6, 2024
    • Title: "System and Method for Manipulating Two-Dimensional (2D) Images of Three-Dimensional (3D) Objects"
      Inventors: Marks, Tim; Medin, Safa; Cherian, Anoop; Wang, Ye
      Patent No.: 11,663,798
      Issue Date: May 30, 2023
    • Title: "InSeGAN: A Generative Approach to Instance Segmentation in Depth Images"
      Inventors: Cherian, Anoop; Pais, Goncalo; Marks, Tim; Sullivan, Alan
      Patent No.: 11,651,497
      Issue Date: May 16, 2023
    • Title: "Method and System for Scene-Aware Interaction"
      Inventors: Hori, Chiori; Cherian, Anoop; Chen, Siheng; Marks, Tim; Le Roux, Jonathan; Hori, Takaaki; Harsham, Bret A.; Vetro, Anthony; Sullivan, Alan
      Patent No.: 11,635,299
      Issue Date: Apr 25, 2023
    • Title: "Scene-Aware Video Encoder System and Method"
      Inventors: Cherian, Anoop; Hori, Chiori; Le Roux, Jonathan; Marks, Tim; Sullivan, Alan
      Patent No.: 11,582,485
      Issue Date: Feb 14, 2023
    • Title: "Low-latency Captioning System"
      Inventors: Hori, Chiori; Hori, Takaaki; Cherian, Anoop; Marks, Tim; Le Roux, Jonathan
      Patent No.: 11,445,267
      Issue Date: Sep 13, 2022
    • Title: "System and Method for a Dialogue Response Generation System"
      Inventors: Hori, Chiori; Cherian, Anoop; Marks, Tim; Hori, Takaaki
      Patent No.: 11,264,009
      Issue Date: Mar 1, 2022
    • Title: "System and Method for Remote Measurements of Vital Signs"
      Inventors: Marks, Tim; Mansour, Hassan; Nowara, Ewa; Nakamura, Yudai; Veeraraghavan, Ashok N.
      Patent No.: 11,259,710
      Issue Date: Mar 1, 2022
    • Title: "Image Processing System and Method for Landmark Location Estimation with Uncertainty"
      Inventors: Marks, Tim; Kumar, Abhinav; Mou, Wenxuan; Feng, Chen; Liu, Xiaoming
      Patent No.: 11,127,164
      Issue Date: Sep 21, 2021
    • Title: "Method and System for Determining 3D Object Poses and Landmark Points using Surface Patches"
      Inventors: Jones, Michael J.; Marks, Tim; Papazov, Chavdar
      Patent No.: 10,515,259
      Issue Date: Dec 24, 2019
    • Title: "Method and System for Multi-Modal Fusion Model"
      Inventors: Hori, Chiori; Hori, Takaaki; Hershey, John R.; Marks, Tim
      Patent No.: 10,417,498
      Issue Date: Sep 17, 2019
    • Title: "Method and System for Detecting Actions in Videos"
      Inventors: Jones, Michael J.; Marks, Tim; Tuzel, Oncel; Singh, Bharat
      Patent No.: 10,242,266
      Issue Date: Mar 26, 2019
    • Title: "Method and System for Detecting Actions in Videos using Contour Sequences"
      Inventors: Jones, Michael J.; Marks, Tim; Kulkarni, Kuldeep
      Patent No.: 10,210,391
      Issue Date: Feb 19, 2019
    • Title: "Method for Estimating Locations of Facial Landmarks in an Image of a Face using Globally Aligned Regression"
      Inventors: Tuzel, Oncel; Marks, Tim; Tambe, Salil
      Patent No.: 9,633,250
      Issue Date: Apr 25, 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 Determining Similarity of Objects Represented in Images"
      Inventors: Jones, Michael J.; Marks, Tim; Ahmed, Ejaz
      Patent No.: 9,436,895
      Issue Date: Sep 6, 2016
    • Title: "Method for Detecting 3D Geometric Boundaries in Images of Scenes Subject to Varying Lighting"
      Inventors: Marks, Tim; Tuzel, Oncel; Porikli, Fatih M.; Thornton, Jay E.; Ni, Jie
      Patent No.: 9,418,434
      Issue Date: Aug 16, 2016
    • Title: "Method for Factorizing Images of a Scene into Basis Images"
      Inventors: Tuzel, Oncel; Marks, Tim; Porikli, Fatih M.; Ni, Jie
      Patent No.: 9,384,553
      Issue Date: Jul 5, 2016
    • Title: "Method and System for Tracking People in Indoor Environments using a Visible Light Camera and a Low-Frame-Rate Infrared Sensor"
      Inventors: Marks, Tim; Jones, Michael J.; Kumar, Suren
      Patent No.: 9,245,196
      Issue Date: Jan 26, 2016
    • Title: "Method for Detecting and Tracking Objects in Image Sequences of Scenes Acquired by a Stationary Camera"
      Inventors: Marks, Tim; Jones, Michael J.; MV, Rohith
      Patent No.: 9,213,896
      Issue Date: Dec 15, 2015
    • Title: "Method and System for Segmenting Moving Objects from Images Using Foreground Extraction"
      Inventors: Veeraraghavan, Ashok N.; Marks, Tim; Taguchi, Yuichi
      Patent No.: 8,941,726
      Issue Date: Jan 27, 2015
    • Title: "Camera-Based 3D Climate Control"
      Inventors: Marks, Tim; Jones, Michael J.
      Patent No.: 8,929,592
      Issue Date: Jan 6, 2015
    • Title: "Method and System for Registering an Object with a Probe Using Entropy-Based Motion Selection and Rao-Blackwellized Particle Filtering"
      Inventors: Taguchi, Yuichi; Marks, Tim; Hershey, John R.
      Patent No.: 8,510,078
      Issue Date: Aug 13, 2013
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