Physics-Based Feature Engineering

被引:0
|
作者
Jalali, Bahram [1 ,2 ,3 ,4 ]
Suthar, Madhuri [1 ]
Asghari, Mohammad [1 ,5 ]
Mahjoubfar, Ata [1 ,2 ]
机构
[1] Univ Calif Los Angeles, Dept Elect & Comp Engn, Los Angeles, CA 90032 USA
[2] Calif NanoSyst Inst, Los Angeles, CA USA
[3] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA USA
[4] Univ Calif Los Angeles, David Geffen Sch Med, Dept Surg, Los Angeles, CA 90095 USA
[5] Loyola Marymount Univ, Dept Elect Engn & Comp Sci, Los Angeles, CA 90045 USA
基金
美国国家卫生研究院;
关键词
TIME STRETCH;
D O I
10.1007/978-3-030-12692-6_12
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We describe a new paradigm in computational imaging for performing edge and texture recognition with a superior dynamic range compared to other methods. The algorithm has its origin in Photonic Time Stretch, a realtime instrumentation technology that has enabled observation of ultrafast, non-repetitive dynamics and discovery of new scientific phenomena. In this chapter, we introduce the mathematical foundation of this new transform and review its intrinsic properties including the built-in equalization property that leads to high performance in visually impaired images. The algorithm is spearheading the development of new methods for feature engineering from visually impaired images with unique and superior properties compared to conventional techniques. It also provides a new approach to the computation of mathematical derivatives via optical dispersion and diffraction. The algorithm is a reconfigurable mathematical operator for hyper-dimensional feature detection and signal classification. It has also shown promising results in super-resolution single molecule imaging.
引用
收藏
页码:255 / 275
页数:21
相关论文
共 50 条
  • [31] Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation
    Hart, Carl R.
    Reznicek, Nathan J.
    Wilson, D. Keith
    Pettit, Chris L.
    Nykaza, Edward T.
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2016, 139 (05): : 2640 - 2655
  • [32] EMC and BEM engineering education: Physics-based modeling, hands-on training, and challenges
    Sevgi, L
    IEEE ANTENNAS AND PROPAGATION MAGAZINE, 2003, 45 (02) : 114 - 119
  • [33] Engineering empowered by physics-based and data-driven hybrid models: A methodological overview
    Victor Champaney
    Francisco Chinesta
    Elias Cueto
    International Journal of Material Forming, 2022, 15
  • [34] EMC and BEM engineering education: Physics-based modeling, hands-on training, and challenges
    Sevgi, L
    IEEE ANTENNAS AND PROPAGATION MAGAZINE, 2004, 46 (02) : 140 - 145
  • [35] Engineering empowered by physics-based and data-driven hybrid models: A methodological overview
    Champaney, Victor
    Chinesta, Francisco
    Cueto, Elias
    INTERNATIONAL JOURNAL OF MATERIAL FORMING, 2022, 15 (03)
  • [36] Frequency domain scanning acoustic microscopy for power electronics: Physics-based feature identification and selectivity
    Uhrenfeldt, C.
    Munk-Nielsen, S.
    Beczkowski, S.
    MICROELECTRONICS RELIABILITY, 2018, 88-90 : 726 - 732
  • [37] Physics-based modeling of musical instruments
    Välimäki, V
    ACTA ACUSTICA UNITED WITH ACUSTICA, 2004, 90 (04) : 611 - 617
  • [38] Physics-based polarimetric BRDF models
    Duncan, DD
    Hahn, DV
    Thomas, ME
    OPTICAL DIAGNOSTIC METHODS FOR INORGANIC MATERIALS III, 2003, 5192 : 129 - 140
  • [39] Survey of physics-based character animation
    Zhao, Jianjun
    Wei, Yi
    Xia, Shihong
    Wang, Zhaoqi
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2015, 52 (12): : 2866 - 2878
  • [40] Polymer Physics-Based Classification of Neurons
    Kiri Choi
    Won Kyu Kim
    Changbong Hyeon
    Neuroinformatics, 2023, 21 : 177 - 193