WO2019140434A3 - Overlapping pattern differentiation at low signal-to-noise ratio - Google Patents

Overlapping pattern differentiation at low signal-to-noise ratio Download PDF

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Publication number
WO2019140434A3
WO2019140434A3 PCT/US2019/013614 US2019013614W WO2019140434A3 WO 2019140434 A3 WO2019140434 A3 WO 2019140434A3 US 2019013614 W US2019013614 W US 2019013614W WO 2019140434 A3 WO2019140434 A3 WO 2019140434A3
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WO
WIPO (PCT)
Prior art keywords
stage
pattern
analysis
noise ratio
low signal
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Application number
PCT/US2019/013614
Other languages
French (fr)
Other versions
WO2019140434A2 (en
Inventor
Nancy E. KLECKNER
Frederick S. CHANG
Original Assignee
President And Fellows Of Harvard College
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Application filed by President And Fellows Of Harvard College filed Critical President And Fellows Of Harvard College
Publication of WO2019140434A2 publication Critical patent/WO2019140434A2/en
Publication of WO2019140434A3 publication Critical patent/WO2019140434A3/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a form of a two-stage Likelihood pipeline analysis for differentiating multiple closely-spaced spots that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions. The second stage may include a full Likelihood analysis. Once a pattern of interest instance is characterized, it may be subtracted from the underlying data, and the two-stage analysis may be performed on the reduced data to detect a further pattern of interest proximate the characterized pattern.
PCT/US2019/013614 2018-01-15 2019-01-15 Overlapping pattern differentiation at low signal-to-noise ratio WO2019140434A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201862617567P 2018-01-15 2018-01-15
US62/617,567 2018-01-15

Publications (2)

Publication Number Publication Date
WO2019140434A2 WO2019140434A2 (en) 2019-07-18
WO2019140434A3 true WO2019140434A3 (en) 2020-04-30

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2019/013614 WO2019140434A2 (en) 2018-01-15 2019-01-15 Overlapping pattern differentiation at low signal-to-noise ratio

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WO (1) WO2019140434A2 (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110533046B (en) * 2019-08-30 2022-03-29 北京地平线机器人技术研发有限公司 Image instance segmentation method and device, computer readable storage medium and electronic equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130294652A1 (en) * 2012-05-04 2013-11-07 Xerox Corporation License plate character segmentation using likelihood maximization
US8729502B1 (en) * 2010-10-28 2014-05-20 The Research Foundation For The State University Of New York Simultaneous, single-detector fluorescence detection of multiple analytes with frequency-specific lock-in detection
WO2017040669A1 (en) * 2015-08-31 2017-03-09 President And Fellows Of Harvard College Pattern detection at low signal-to-noise ratio

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8729502B1 (en) * 2010-10-28 2014-05-20 The Research Foundation For The State University Of New York Simultaneous, single-detector fluorescence detection of multiple analytes with frequency-specific lock-in detection
US20130294652A1 (en) * 2012-05-04 2013-11-07 Xerox Corporation License plate character segmentation using likelihood maximization
WO2017040669A1 (en) * 2015-08-31 2017-03-09 President And Fellows Of Harvard College Pattern detection at low signal-to-noise ratio

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
CHEN ET AL.: "Segmenting focused objects based on the Amplitude Decomposition Mode", PATTERN RECOGNITION LETTERS, vol. 33, no. 12, 1 September 2012 (2012-09-01), XP028503878, Retrieved from the Internet <URL:https://www.sciencedirecl..com/science/article/pii/S0167865512001389> [retrieved on 20190326] *
CHOWDHURY ET AL.: "Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE", BMC BIOINFORMATICS, vol. 14, no. 10, 2013, XP021158358, Retrieved from the Internet <URL:https://bmcbioinformatics.biomedcentral.eom/articles/10.1186/1471-2105-14-S10-S8> [retrieved on 20190326] *

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