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Mauricio Hess-Flores
Postdoctoral Researcher |
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I am a Postdoctoral Researcher working in the Institute for Data Analysis and Visualization (IDAV) at UCDavis. My original research area has involved large-scale 3D reconstruction from aerial images, which originally began as a joint project with Mark Duchaineau at Lawrence Livermore National Laboratory (now at Google, Inc.), and under the guidance of Prof. Ken Joy at UCDavis. I have continued to work on aerial reconstruction by means of the novel 'parallax paths' concept, which allows for more efficient feature matching, pose estimation and structure computation in such scenarios. More recent work includes the use of visualization techniques to aid in computer vision algorithm design and testing. Initial work began with Dr. Duchaineau and has continued with a number of publications involving IDAV PhD student Shawn Recker. I have also been working With Shawn and Jason Mak on fast and efficient multi-view triangulation, in order to compute scene structure more efficiently. I had previously graduated from the Universidad de Costa Rica with a BS and MS in Electrical Engineering. For my MS Thesis, I worked on facial feature extraction and face model adaptation for model-based coding, using image processing and computer vision techniques.
View All Publications Recent Publications
- Jason Mak, Mauricio Hess-Flores, Shawn Recker, John D. Owens, Kenneth I. Joy, "GPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction", in "WACV 2014: IEEE Winter Conference on Applications of Computer Vision", 2014
- Mikhail Shashkov, Connie Nguyen, Mario Yepez, Mauricio Hess-Flores, Kenneth I. Joy, "Semi-Autonomous Digitization of Real-World Environments", in "19th International Conference on Computer Games: AI, Animation, Interactive Multimedia, Virtual Worlds & Serious Games (C-GAMES USA 2014)", 2014
- Jason Mak, Mauricio Hess-Flores, Shawn Recker, John D. Owens, Kenneth I. Joy, "A Comparative Study of Recent GPU-Accelerated Multi-View Sequential Reconstruction Triangulation Methods for Large-Scale Scenes", in "ACCV 2014 Workshop: Big Data in 3D Computer Vision", 2014
