Michael J. Jones

Michael J. Jones
  • Biography

    Mike's main areas of interest are computer vision, machine learning and data mining. He has focused on algorithms for detecting and analyzing people in images and video including face detection and recognition and pedestrian detection. He is a co-inventor of the popular Viola-Jones face detection method. Mike has been awarded the Marr Prize at ICCV and the Longuet-Higgins Prize at CVPR.

  • Recent News & Events

    •  NEWS    MERL researchers presenting four papers and organizing the VLAR-SMART101 Workshop at ICCV 2023
      Date: October 2, 2023 - October 6, 2023
      Where: Paris/France
      MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting 4 papers and organizing the VLAR-SMART-101 workshop at the ICCV 2023 conference, which will be held in Paris, France October 2-6. ICCV is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.

        1. Conference paper: “Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis,” by Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit, Ye Wang, Toshiaki Koike-Akino, Vishal Patel, and Tim K. Marks

        Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in plug-and-play generation, i.e., using a pre-defined model to guide the generative process. In this paper, we introduce Steered Diffusion, a generalized framework for fine-grained photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation. The key idea is to steer the image generation of the diffusion model during inference via designing a loss using a pre-trained inverse model that characterizes the conditional task. Our model shows clear qualitative and quantitative improvements over state-of-the-art diffusion-based plug-and-play models, while adding negligible computational cost.

        2. Conference paper: "BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus," by Valter Piedade and Pedro Miraldo

        We derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. Our method outperforms the baselines in accuracy while needing less computational time.

        3. Conference paper: "Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes," by Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller

        We present a novel approach to estimating camera rotation in crowded, real-world scenes captured using a handheld monocular video camera. Our method uses a novel generalization of the Hough transform on SO3 to efficiently find the camera rotation most compatible with the optical flow. Because the setting is not addressed well by other data sets, we provide a new dataset and benchmark, with high-accuracy and rigorously annotated ground truth on 17 video sequences. Our method is more accurate by almost 40 percent than the next best method.

        4. Workshop paper: "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection" by Manish Sharma*, Moitreya Chatterjee*, Kuan-Chuan Peng, Suhas Lohit, and Michael Jones

        While state-of-the-art object detection methods for RGB images have reached some level of maturity, the same is not true for Infrared (IR) images. The primary bottleneck towards bridging this gap is the lack of sufficient labeled training data in the IR images. Towards addressing this issue, we present TensorFact, a novel tensor decomposition method which splits the convolution kernels of a CNN into low-rank factor matrices with fewer parameters. This compressed network is first pre-trained on RGB images and then augmented with only a few parameters. This augmented network is then trained on IR images, while freezing the weights trained on RGB. This prevents it from over-fitting, allowing it to generalize better. Experiments show that our method outperforms state-of-the-art.

        5. “Vision-and-Language Algorithmic Reasoning (VLAR) Workshop and SMART-101 Challenge” by Anoop Cherian,  Kuan-Chuan Peng, Suhas Lohit, Tim K. Marks, Ram Ramrakhya, Honglu Zhou, Kevin A. Smith, Joanna Matthiesen, and Joshua B. Tenenbaum

        MERL researchers along with researchers from MIT, GeorgiaTech, Math Kangaroo USA, and Rutgers University are jointly organizing a workshop on vision-and-language algorithmic reasoning at ICCV 2023 and conducting a challenge based on the SMART-101 puzzles described in the paper: Are Deep Neural Networks SMARTer than Second Graders?. A focus of this workshop is to bring together outstanding faculty/researchers working at the intersections of vision, language, and cognition to provide their opinions on the recent breakthroughs in large language models and artificial general intelligence, as well as showcase their cutting edge research that could inspire the audience to search for the missing pieces in our quest towards solving the puzzle of artificial intelligence.

        Workshop link: https://wvlar.github.io/iccv23/
    •  
    •  NEWS    MERL researchers presenting four papers and co-organizing a workshop at CVPR 2023
      Date: June 18, 2023 - June 22, 2023
      Where: Vancouver/Canada
      MERL Contacts: Anoop Cherian; Michael J. Jones; Suhas Lohit; Kuan-Chuan Peng
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting 4 papers and co-organizing a workshop at the CVPR 2023 conference, which will be held in Vancouver, Canada June 18-22. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.

        1. “Are Deep Neural Networks SMARTer than Second Graders,” by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin Smith, and Joshua B. Tenenbaum

        We present SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed for children in the 6-8 age group. Our experiments using SMART-101 reveal that powerful deep models are not better than random accuracy when analyzed for generalization. We also evaluate large language models (including ChatGPT) on a subset of SMART-101 and find that while these models show convincing reasoning abilities, their answers are often incorrect.

        Paper: https://arxiv.org/abs/2212.09993

        2. “EVAL: Explainable Video Anomaly Localization,” by Ashish Singh, Michael J. Jones, and Erik Learned-Miller

        This work presents a method for detecting unusual activities in videos by building a high-level model of activities found in nominal videos of a scene. The high-level features used in the model are human understandable and include attributes such as the object class and the directions and speeds of motion. Such high-level features allow our method to not only detect anomalous activity but also to provide explanations for why it is anomalous.

        Paper: https://arxiv.org/abs/2212.07900

        3. "Aligning Step-by-Step Instructional Diagrams to Video Demonstrations," by Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, and Stephen Gould

        The rise of do-it-yourself (DIY) videos on the web has made it possible even for an unskilled person (or a skilled robot) to imitate and follow instructions to complete complex real world tasks. In this paper, we consider the novel problem of aligning instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) with video segments from in-the-wild videos. We present a new dataset: Ikea Assembly in the Wild (IAW) and propose a contrastive learning framework for aligning instruction diagrams with video clips.

        Paper: https://arxiv.org/pdf/2303.13800.pdf

        4. "HaLP: Hallucinating Latent Positives for Skeleton-Based Self-Supervised Learning of Actions," by Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra, David Jacobs, Anoop Cherian, and Rama Chellappa

        In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels. Our key contribution is a simple module, HaLP: Hallucinating Latent Positives for contrastive learning. HaLP explores the latent space of poses in suitable directions to generate new positives. Our experiments using HaLP demonstrates strong empirical improvements.

        Paper: https://arxiv.org/abs/2304.00387

        The 4th Workshop on Fair, Data-Efficient, and Trusted Computer Vision

        MERL researcher Kuan-Chuan Peng is co-organizing the fourth Workshop on Fair, Data-Efficient, and Trusted Computer Vision (https://fadetrcv.github.io/2023/) in conjunction with CVPR 2023 on June 18, 2023. This workshop provides a focused venue for discussing and disseminating research in the areas of fairness, bias, and trust in computer vision, as well as adjacent domains such as computational social science and public policy.
    •  

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  • Awards

    •  AWARD    CVPR 2011 Longuet-Higgins Prize
      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).
    •  
    See All Awards for MERL
  • Research Highlights

  • Internships with Mike

    • CV2070: Open-World Object Detection

      MERL is looking for a highly motivated intern to work on an original research project in open-world object detection. A strong background in computer vision and deep learning is required. Experience in the latest advances in object detection, incremental learning, and open-world object detection is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible but are expected to last for at least 3 months. This internship is preferred to be onsite at MERL’s office in Cambridge, MA

    See All Internships at MERL
  • MERL Publications

    •  Carmichael, Z., Jones, L.S., Cherian, A., Michael J., , Scheirer, W., "Pixel-Grounded Prototypical Part Networks", IEEE Winter Conference on Applications of Computer Vision (WACV), January 2024.
      BibTeX TR2024-002 PDF Presentation
      • @inproceedings{Carmichael2024jan,
      • author = {Carmichael, Zachariah and Jones, Lohit, Suhas and Cherian, Anoop and Michael J. and Scheirer, Walter},
      • title = {Pixel-Grounded Prototypical Part Networks},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2024,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2024-002}
      • }
    •  Delattre, F., Dirnfeld, D., Nguyen, P., Scarano, S., Jones, M.J., Miraldo, P., Learned-Miller, E., "Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes", IEEE International Conference on Computer Vision (ICCV), DOI: 10.1109/​ICCV51070.2023.00894, October 2023, pp. 3715-3724.
      BibTeX TR2023-123 PDF Video
      • @inproceedings{Delattre2023oct,
      • author = {Delattre, Fabien and Dirnfeld, David and Nguyen, Phat and Scarano, Stephen and Jones, Michael J. and Miraldo, Pedro and Learned-Miller, Erik},
      • title = {Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes},
      • booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      • year = 2023,
      • pages = {3715--3724},
      • month = oct,
      • publisher = {IEEE/CVF},
      • doi = {10.1109/ICCV51070.2023.00894},
      • issn = {2380-7504},
      • isbn = {979-8-3503-0718-4},
      • url = {https://www.merl.com/publications/TR2023-123}
      • }
    •  Sharma, M., Chatterjee, M., Peng, K.-C., Lohit, S., Jones, M.J., "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection", IEEE International Conference on Computer Vision Workshops (ICCV), October 2023, pp. 924-932.
      BibTeX TR2023-125 PDF Presentation
      • @inproceedings{Sharma2023oct,
      • author = {Sharma, Manish and Chatterjee, Moitreya and Peng, Kuan-Chuan and Lohit, Suhas and Jones, Michael J.},
      • title = {Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection},
      • booktitle = {IEEE International Conference on Computer Vision Workshops (ICCV)},
      • year = 2023,
      • pages = {924--932},
      • month = oct,
      • url = {https://www.merl.com/publications/TR2023-125}
      • }
    •  Singh, A., Jones, M.J., Learned-Miller, E., "EVAL: Explainable Video Anomaly Localization", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/​CVPR52729.2023.01795, June 2023.
      BibTeX TR2023-071 PDF Video Presentation
      • @inproceedings{Singh2023jun,
      • author = {Singh, Ashish and Jones, Michael J. and Learned-Miller, Erik},
      • title = {EVAL: Explainable Video Anomaly Localization},
      • booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2023,
      • month = jun,
      • doi = {10.1109/CVPR52729.2023.01795},
      • url = {https://www.merl.com/publications/TR2023-071}
      • }
    •  Wang, H., Lohit, S., Jones, M.J., Fu, R., "What Makes a “Good” Data Augmentation in Knowledge Distillation – A Statistical Perspective", Advances in Neural Information Processing Systems (NeurIPS), S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh, Eds., November 2022, pp. 13456-13469.
      BibTeX TR2022-147 PDF
      • @inproceedings{Wang2022nov,
      • author = {Wang, Huan and Lohit, Suhas and Jones, Michael J. and Fu, Raymond},
      • title = {What Makes a “Good” Data Augmentation in Knowledge Distillation – A Statistical Perspective},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2022,
      • editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
      • pages = {13456--13469},
      • month = nov,
      • url = {https://www.merl.com/publications/TR2022-147}
      • }
    See All MERL Publications for Mike
  • Other Publications

    •  G.B. Huang, M.J. Jones, E. Learned-Miller and others, "Lfw results using a combined nowak plus merl recognizer", 2008.
      BibTeX
      • @Article{huang2008lfw,
      • author = {Huang, G.B. and Jones, M.J. and Learned-Miller, E. and others},
      • title = {Lfw results using a combined nowak plus merl recognizer},
      • year = 2008
      • }
    •  T.J. Cham, S. Krishnamoorthy and M. Jones, "Analogous view transfer for gaze correction in video sequences", Control, Automation, Robotics and Vision, 2002. ICARCV 2002. 7th International Conference on, 2002, vol. 3, pp. 1415-1420.
      BibTeX
      • @Inproceedings{cham2002analogous,
      • author = {Cham, T.J. and Krishnamoorthy, S. and Jones, M.},
      • title = {Analogous view transfer for gaze correction in video sequences},
      • booktitle = {Control, Automation, Robotics and Vision, 2002. ICARCV 2002. 7th International Conference on},
      • year = 2002,
      • volume = 3,
      • pages = {1415--1420},
      • organization = {IEEE}
      • }
    •  M.J. Jones and J.M. Rehg, "Statistical color models with application to skin detection", International Journal of Computer Vision, Vol. 46, No. 1, pp. 81-96, 2002.
      BibTeX
      • @Article{jones2002statistical,
      • author = {Jones, M.J. and Rehg, J.M.},
      • title = {Statistical color models with application to skin detection},
      • journal = {International Journal of Computer Vision},
      • year = 2002,
      • volume = 46,
      • number = 1,
      • pages = {81--96},
      • publisher = {Springer}
      • }
    •  S.B. Kang and M. Jones, "Appearance-based structure from motion using linear classes of 3-d models", International Journal of Computer Vision, Vol. 49, No. 1, pp. 5-22, 2002.
      BibTeX
      • @Article{kang2002appearance,
      • author = {Kang, S.B. and Jones, M.},
      • title = {Appearance-based structure from motion using linear classes of 3-d models},
      • journal = {International Journal of Computer Vision},
      • year = 2002,
      • volume = 49,
      • number = 1,
      • pages = {5--22},
      • publisher = {Springer}
      • }
    •  C.M. Procopiuc, M. Jones, P.K. Agarwal and TM Murali, "A Monte Carlo algorithm for fast projective clustering", Proceedings of the 2002 ACM SIGMOD international conference on Management of data, 2002, pp. 418-427.
      BibTeX
      • @Inproceedings{procopiuc2002monte,
      • author = {Procopiuc, C.M. and Jones, M. and Agarwal, P.K. and Murali, TM},
      • title = {A Monte Carlo algorithm for fast projective clustering},
      • booktitle = {Proceedings of the 2002 ACM SIGMOD international conference on Management of data},
      • year = 2002,
      • pages = {418--427},
      • organization = {ACM}
      • }
    •  P. Viola and M. Jones, "Fast and robust classification using asymmetric adaboost and a detector cascade", Proc. of NIPS01, 2001.
      BibTeX
      • @Article{viola2001fast,
      • author = {Viola, P. and Jones, M.},
      • title = {Fast and robust classification using asymmetric adaboost and a detector cascade},
      • journal = {Proc. of NIPS01},
      • year = 2001
      • }
    •  M.J. Jones and J.M. Rehg, "Statistical color models with application to skin detection", Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., 1999, vol. 1.
      BibTeX
      • @Inproceedings{jones1999statistical,
      • author = {Jones, M.J. and Rehg, J.M.},
      • title = {Statistical color models with application to skin detection},
      • booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
      • year = 1999,
      • volume = 1,
      • organization = {IEEE}
      • }
    •  T.D. Rikert, M.J. Jones and P. Viola, "A cluster-based statistical model for object detection", Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1999, vol. 2, pp. 1046-1053.
      BibTeX
      • @Inproceedings{rikert1999cluster,
      • author = {Rikert, T.D. and Jones, M.J. and Viola, P.},
      • title = {A cluster-based statistical model for object detection},
      • booktitle = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on},
      • year = 1999,
      • volume = 2,
      • pages = {1046--1053},
      • organization = {IEEE}
      • }
    •  M.J. Jones and T. Poggio, "Hierarchical morphable models", Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, 1998, pp. 820-826.
      BibTeX
      • @Inproceedings{jones1998hierarchical,
      • author = {Jones, M.J. and Poggio, T.},
      • title = {Hierarchical morphable models},
      • booktitle = {Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on},
      • year = 1998,
      • pages = {820--826},
      • organization = {IEEE}
      • }
    •  M.J. Jones and T. Poggio, "Multidimensional morphable models: A framework for representing and matching object classes", International Journal of Computer Vision, Vol. 29, No. 2, pp. 107-131, 1998.
      BibTeX
      • @Article{jones1998multidimensional,
      • author = {Jones, M.J. and Poggio, T.},
      • title = {Multidimensional morphable models: A framework for representing and matching object classes},
      • journal = {International Journal of Computer Vision},
      • year = 1998,
      • volume = 29,
      • number = 2,
      • pages = {107--131},
      • publisher = {Springer}
      • }
    •  T.D. Rikert and M.J. Jones, "Gaze estimation using morphable models", Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, 1998, pp. 436-441.
      BibTeX
      • @Inproceedings{rikert1998gaze,
      • author = {Rikert, T.D. and Jones, M.J.},
      • title = {Gaze estimation using morphable models},
      • booktitle = {Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on},
      • year = 1998,
      • pages = {436--441},
      • organization = {IEEE}
      • }
    •  M.J. Jones, "Multidimensional morphable models: A framework for representing and matching object classes", 1997.
      BibTeX
      • @Phdthesis{jones1997multidimensional,
      • author = {Jones, M.J.},
      • title = {Multidimensional morphable models: A framework for representing and matching object classes},
      • year = 1997,
      • publisher = {Massachusetts Institute of Technology}
      • }
    •  M.J. Jones, P. Sinha, T. Vetter and T. Poggio, "Top-down learning of low-level vision tasks", Current Biology, Vol. 7, No. 12, pp. 991-994, 1997.
      BibTeX
      • @Article{jones1997top,
      • author = {Jones, M.J. and Sinha, P. and Vetter, T. and Poggio, T.},
      • title = {Top-down learning of low-level vision tasks},
      • journal = {Current Biology},
      • year = 1997,
      • volume = 7,
      • number = 12,
      • pages = {991--994},
      • publisher = {Elsevier}
      • }
    •  T. Vetter, M.J. Jones and T. Poggio, "A bootstrapping algorithm for learning linear models of object classes", Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 1997, pp. 40-46.
      BibTeX
      • @Inproceedings{vetter1997bootstrapping,
      • author = {Vetter, T. and Jones, M.J. and Poggio, T.},
      • title = {A bootstrapping algorithm for learning linear models of object classes},
      • booktitle = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on},
      • year = 1997,
      • pages = {40--46},
      • organization = {IEEE}
      • }
    •  F. Girosi, M. Jones and T. Poggio, "Regularization theory and neural networks architectures", Neural computation, Vol. 7, No. 2, pp. 219-269, 1995.
      BibTeX
      • @Article{girosi1995regularization,
      • author = {Girosi, F. and Jones, M. and Poggio, T.},
      • title = {Regularization theory and neural networks architectures},
      • journal = {Neural computation},
      • year = 1995,
      • volume = 7,
      • number = 2,
      • pages = {219--269},
      • publisher = {MIT Press}
      • }
    •  M.J. Jones and T. Poggio, "Model-based matching of line drawings by linear combinations of prototypes", Computer Vision, 1995. Proceedings., Fifth International Conference on, 1995, pp. 531-536.
      BibTeX
      • @Inproceedings{jones1995model,
      • author = {Jones, M.J. and Poggio, T.},
      • title = {Model-based matching of line drawings by linear combinations of prototypes},
      • booktitle = {Computer Vision, 1995. Proceedings., Fifth International Conference on},
      • year = 1995,
      • pages = {531--536},
      • organization = {IEEE}
      • }
    •  F. Girosi, M. Jones and T. Poggio, "Priors stabilizers and basis functions: From regularization to radial, tensor and additive splines", MIT AI Lab Memo 1430, 1993.
      BibTeX
      • @Article{girosi1993priors,
      • author = {Girosi, F. and Jones, M. and Poggio, T.},
      • title = {Priors stabilizers and basis functions: From regularization to radial, tensor and additive splines},
      • journal = {MIT AI Lab Memo 1430},
      • year = 1993
      • }
    •  T. Poggio, F. Girosi and M. Jones, "From regularization to radial, tensor and additive splines", Neural Networks, 1993. IJCNN'93-Nagoya. Proceedings of 1993 International Joint Conference on, 1993, vol. 1, pp. 223-227.
      BibTeX
      • @Inproceedings{poggio1993regularization,
      • author = {Poggio, T. and Girosi, F. and Jones, M.},
      • title = {From regularization to radial, tensor and additive splines},
      • booktitle = {Neural Networks, 1993. IJCNN'93-Nagoya. Proceedings of 1993 International Joint Conference on},
      • year = 1993,
      • volume = 1,
      • pages = {223--227},
      • organization = {IEEE}
      • }
    •  M.J. Jones, "Using recurrent networks for dimensionality reduction", 1992, Massachusetts Institute of Technology.
      BibTeX
      • @Mastersthesis{jones1992using,
      • author = {Jones, M.J.},
      • title = {Using recurrent networks for dimensionality reduction},
      • school = {Massachusetts Institute of Technology},
      • year = 1992
      • }
  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Detecting Objects in Video Sequences"
      Inventors: Jones, Michael J.; Broad, Alexander
      Patent No.: 11,164,003
      Issue Date: Nov 2, 2021
    • Title: "System and Method for Detecting Motion Anomalies in Video"
      Inventors: Jones, Michael J.
      Patent No.: 10,970,823
      Issue Date: Apr 6, 2021
    • Title: "System and Method for Detecting Anomalies in Video using a Similarity Function Trained by Machine Learning"
      Inventors: Jones, Michael J.
      Patent No.: 10,824,935
      Issue Date: Nov 3, 2020
    • 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: "System and Method for Image Comparison Based on Hyperplanes Similarity"
      Inventors: Jones, Michael J.
      Patent No.: 10,452,958
      Issue Date: Oct 22, 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 Anomaly Detection in Time Series Data Based on Spectral Partitioning"
      Inventors: Nikovski, Daniel N.; Kniazev, Andrei; Jones, Michael J.
      Patent No.: 9,984,334
      Issue Date: May 29, 2018
    • Title: "Method for Learning Exemplars for Anomaly Detection"
      Inventors: Jones, Michael J.; Nikovski, Daniel N.
      Patent No.: 9,779,361
      Issue Date: Oct 3, 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 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 for Detecting Anomalies in a Time Series Data with Trajectory and Stochastic Components"
      Inventors: Jones, Michael J.
      Patent No.: 9,146,800
      Issue Date: Sep 29, 2015
    • Title: "Method for Predicting Future Travel Time Using Geospatial Inference"
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    See All Patents for MERL