MERL - Mitsubishi Electric Research Laboratories

Michael J. Jones

Michael J. Jones
Mike's Quick Links

  • 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 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.
    •  
    •  NEWS    MERL researchers presenting five papers at NeurIPS 2022
      Date: November 29, 2022 - December 9, 2022
      Where: NeurIPS 2022
      MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Suhas Lohit
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • MERL researchers are presenting 5 papers at the NeurIPS Conference, which will be held in New Orleans from Nov 29-Dec 1st, with virtual presentations in the following week. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.

        MERL papers in NeurIPS 2022:

        1. “AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments” by Sudipta Paul, Amit Roy-Chowdhary, and Anoop Cherian

        This work proposes a unified multimodal task for audio-visual embodied navigation where the navigating agent can also interact and seek help from a human/oracle in natural language when it is uncertain of its navigation actions. We propose a multimodal deep hierarchical reinforcement learning framework for solving this challenging task that allows the agent to learn when to seek help and how to use the language instructions. AVLEN agents can interact anywhere in the 3D navigation space and demonstrate state-of-the-art performances when the audio-goal is sporadic or when distractor sounds are present.

        2. “Learning Partial Equivariances From Data” by David W. Romero and Suhas Lohit

        Group equivariance serves as a good prior improving data efficiency and generalization for deep neural networks, especially in settings with data or memory constraints. However, if the symmetry groups are misspecified, equivariance can be overly restrictive and lead to bad performance. This paper shows how to build partial group convolutional neural networks that learn to adapt the equivariance levels at each layer that are suitable for the task at hand directly from data. This improves performance while retaining equivariance properties approximately.

        3. “Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation” by Moitreya Chatterjee, Narendra Ahuja, and Anoop Cherian

        There often exist strong correlations between the 3D motion dynamics of a sounding source and its sound being heard, especially when the source is moving towards or away from the microphone. In this paper, we propose an audio-visual scene-graph that learns and leverages such correlations for improved visually-guided audio separation from an audio mixture, while also allowing predicting the direction of motion of the sound source.

        4. “What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective” by Huan Wang, Suhas Lohit, Michael Jones, and Yun Fu

        This paper presents theoretical and practical results for understanding what makes a particular data augmentation technique (DA) suitable for knowledge distillation (KD). We design a simple metric that works very well in practice to predict the effectiveness of DA for KD. Based on this metric, we also propose a new data augmentation technique that outperforms other methods for knowledge distillation in image recognition networks.

        5. “FeLMi : Few shot Learning with hard Mixup” by Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, and Rama Chellappa

        Learning from only a few examples is a fundamental challenge in machine learning. Recent approaches show benefits by learning a feature extractor on the abundant and labeled base examples and transferring these to the fewer novel examples. However, the latter stage is often prone to overfitting due to the small size of few-shot datasets. In this paper, we propose a novel uncertainty-based criteria to synthetically produce “hard” and useful data by mixing up real data samples. Our approach leads to state-of-the-art results on various computer vision few-shot benchmarks.
    •  

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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

  • MERL Publications

    •  Singh, A., Jones, M.J., Learned-Miller, E., "EVAL: Explainable Video Anomaly Localization", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 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 Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2023,
      • month = jun,
      • 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}
      • }
    •  Ahmed, S.M., Lohit, S., Peng, K.-C., Jones, M.J., Roy Chowdhury, A.K., "Cross-Modal Knowledge Transfer Without Task-Relevant Source Data", European Conference on Computer Vision (ECCV), Avidan, S and Brostow, G and Cisse M and Farinella, G.M. and Hassner T., Eds., DOI: 10.1007/​978-3-031-19830-4_7, October 2022, pp. 111-127.
      BibTeX TR2022-135 PDF Video Software Presentation
      • @inproceedings{Ahmed2022oct,
      • author = {Ahmed, Sk Miraj and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Roy Chowdhury, Amit K},
      • title = {Cross-Modal Knowledge Transfer Without Task-Relevant Source Data},
      • booktitle = {Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXXIV},
      • year = 2022,
      • editor = {Avidan, S and Brostow, G and Cisse M and Farinella, G.M. and Hassner T.},
      • pages = {111--127},
      • month = oct,
      • publisher = {Springer},
      • doi = {10.1007/978-3-031-19830-4_7},
      • isbn = {978-3-031-19830-4},
      • url = {https://www.merl.com/publications/TR2022-135}
      • }
    •  Rambhatla, S., Jones, M.J., Chellappa, R., "An Empirical Analysis of Boosting Deep Networks", International Joint Conference on Neural Networks (IJCNN), DOI: 10.1109/​IJCNN55064.2022.9892204, July 2022.
      BibTeX TR2022-075 PDF Presentation
      • @inproceedings{Rambhatla2022jul,
      • author = {Rambhatla, Sai and Jones, Michael J. and Chellappa, Rama},
      • title = {An Empirical Analysis of Boosting Deep Networks},
      • booktitle = {International Joint Conference on Neural Networks (IJCNN)},
      • year = 2022,
      • month = jul,
      • doi = {10.1109/IJCNN55064.2022.9892204},
      • url = {https://www.merl.com/publications/TR2022-075}
      • }
    •  Lohit, S., Jones, M.J., "Model Compression Using Optimal Transport", IEEE Winter Conference on Applications of Computer Vision (WACV), January 2022.
      BibTeX TR2022-006 PDF Presentation
      • @inproceedings{Lohit2022jan,
      • author = {Lohit, Suhas and Jones, Michael J.},
      • title = {Model Compression Using Optimal Transport},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2022,
      • month = jan,
      • publisher = {CVF OpenAccess},
      • url = {https://www.merl.com/publications/TR2022-006}
      • }
    See All 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
      • }

  • 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"
      Inventors: Jones, Michael J.; Nikovski, Daniel N.; Geng, Yanfeng
      Patent No.: 9,122,987
      Issue Date: Sep 1, 2015
    • Title: "Method for Detecting Anomalies in Multivariate Time Series Data"
      Inventors: Nikovski, Daniel N.; Jones, Michael J.
      Patent No.: 9,075,713
      Issue Date: Jul 7, 2015
    • Title: "Camera-Based 3D Climate Control"
      Inventors: Marks, Tim; Jones, Michael J.
      Patent No.: 8,929,592
      Issue Date: Jan 6, 2015
    • Title: "Object Detection in Depth Images"
      Inventors: Jones, Michael J.; Tuzel, C. Oncel; Si, Weiguang
      Patent No.: 8,406,470
      Issue Date: Mar 26, 2013
    • Title: "Method for identifying Faces in Images with Improved Accuracy Using Compressed Feature Vectors"
      Inventors: Thornton, Jay E.; Jones, Michael J.
      Patent No.: 8,213,691
      Issue Date: Jul 3, 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
    • Title: "Method for Localizing Irises in Images Using Gradients and Textures"
      Inventors: Jones, Michael J.; Guo, Guo Dong
      Patent No.: 7,583,823
      Issue Date: Sep 1, 2009
    • Title: "Detecting Pedestrians Using Patterns of Motion and Appearance in Videos"
      Inventors: Jones, Michael J.; Viola, Paul A.
      Patent No.: 7,212,651
      Issue Date: May 1, 2007
    • Title: "Detecting Arbitrarily Oriented Objects in Images"
      Inventors: Viola, Paul A.; Jones, Michael J.
      Patent No.: 7,197,186
      Issue Date: Mar 27, 2007
    • Title: "Object Recognition System"
      Inventors: Viola, Paul A.; Jones, Michael J.
      Patent No.: 7,031,499
      Issue Date: Apr 18, 2006
    • Title: "System and Method for Detecting Objects in Images"
      Inventors: Viola, Paul A.; Jones, Michael J.
      Patent No.: 7,020,337
      Issue Date: Mar 28, 2006
    See All Patents for MERL