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Blob Feature Extraction for Event Detection Cameras

Raffoul, Joseph Naim

Abstract Details

2020, Master of Science in Electrical Engineering, University of Dayton, Electrical and Computer Engineering.
Neuromorphic (a.k.a. event detection) cameras emerged out of biologically inspired visual perception. A key component of neuromorphic cameras is the dynamic vision sensor or DVS, which generates an asynchronous data stream reporting temporal log-intensity changes (or “events”) of the pixel-sized photodiodes. In this thesis a novel blob feature extraction technique for neuromorphic cameras is proposed. Using asynchronous 3D distance transform, we are able to track a blob, describe its size/shape/orientation, efficiently match the feature descriptors, and perform image correspondence across multiple neuromorphic cameras.
Keigo Hirakawa, Dr. (Committee Chair)
Tarek Taha, Dr. (Committee Member)
Ju Shen, Dr. (Committee Member)
39 p.

Recommended Citations

Citations

  • Raffoul, J. N. (2020). Blob Feature Extraction for Event Detection Cameras [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1590165017029087

    APA Style (7th edition)

  • Raffoul, Joseph. Blob Feature Extraction for Event Detection Cameras. 2020. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1590165017029087.

    MLA Style (8th edition)

  • Raffoul, Joseph. "Blob Feature Extraction for Event Detection Cameras." Master's thesis, University of Dayton, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1590165017029087

    Chicago Manual of Style (17th edition)