Tim K. Marks

Tim K. Marks
  • 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.

  • News & Events

    •  NEWS   MERL Researcher Tim Marks presents an invited talk at MIT Lincoln Laboratory
      Date: April 27, 2017
      Where: Lincoln Laboratory, Massachusetts Institute of Technology
      MERL Contact: Tim K. Marks
      Research Areas: Computer Vision, 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.
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    •  NEWS   MERL researcher Tim Marks presents invited talk at University of Utah
      Date: April 10, 2017
      Where: University of Utah School of Computing
      MERL Contact: Tim K. Marks
      Research Areas: Computer Vision, 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.
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  • Research Highlights

  • Internships with Tim

    • CV1036: Real-Time Face Alignment

      MERL is seeking a highly motivated intern to conduct original research in the area of face alignment (determining the locations of multiple facial landmarks) from images and video. The successful candidate will collaborate with MERL researchers to derive and implement new models, conduct experiments, and prepare results for publication. This project will also focus on tailoring algorithms to a real-world product. The ideal candidate would be a senior PhD student in computer vision with experience in face alignment, tracking, 3D head pose estimation, and machine learning. Strong C/C++ programming and Matlab experience are expected. Duration: 3-6 months. Starting date can be as early as January 2017.

    See All Internships at MERL
  • MERL Publications

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  • Other Publications

    •  Marks, Tim K; Howard, Andrew; Bajracharya, Max; Cottrell, Garrison W; Matthies, Larry H, "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}
      • }
    •  Barrington, Luke; Marks, Tim K; Hsiao, Janet Hui-wen; Cottrell, Garrison W, "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}
      • }
    •  Marks, Tim K; Howard, Andrew; Bajracharya, Max; Cottrell, Garrison W; Matthies, Larry, "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}
      • }
    •  Zhang, Lingyun; Tong, Matthew H; Marks, Tim K; Shan, Honghao; Cottrell, Garrison W, "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}
      • }
    •  Marks, Tim K; Howard, Andrew; Bajracharya, Max; Cottrell, Garrison W; Matthies, Larry, "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}
      • }
    •  Marks, Tim K; Hershey, John; Roddey, J Cooper; Movellan, Javier R, "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}
      • }
    •  Marks, Tim K; Hershey, John; Roddey, J Cooper; Movellan, Javier R, "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}
      • }
    •  Marks, Tim K; Movellan, Javier R, "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}
      • }
  • Videos

  • MERL Issued Patents

    • 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
    • Title: "Localization in Industrial Robotics Using Rao-Blackwellized Particle Filtering"
      Inventors: Marks, Tim; Taguchi, Yuichi
      Patent No.: 8,219,352
      Issue Date: Jul 10, 2012
    • Title: "Method for Synthetically Images of Objects"
      Inventors: Jones, Michael J.; Marks, Tim; Kumar, Ritwik
      Patent No.: 8,194,072
      Issue Date: Jun 5, 2012
    See All Patents for MERL