MX2017013128A - Sistemas y metodos para reducir densidad de los datos en grandes conjuntos de datos. - Google Patents

Sistemas y metodos para reducir densidad de los datos en grandes conjuntos de datos.

Info

Publication number
MX2017013128A
MX2017013128A MX2017013128A MX2017013128A MX2017013128A MX 2017013128 A MX2017013128 A MX 2017013128A MX 2017013128 A MX2017013128 A MX 2017013128A MX 2017013128 A MX2017013128 A MX 2017013128A MX 2017013128 A MX2017013128 A MX 2017013128A
Authority
MX
Mexico
Prior art keywords
systems
point
data point
methods
unknown
Prior art date
Application number
MX2017013128A
Other languages
English (en)
Inventor
Neumeier Zeev
Original Assignee
Inscape Data Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Inscape Data Inc filed Critical Inscape Data Inc
Publication of MX2017013128A publication Critical patent/MX2017013128A/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/7847—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using low-level visual features of the video content
    • G06F16/786—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using low-level visual features of the video content using motion, e.g. object motion or camera motion
    • 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
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G06V20/47—Detecting features for summarising video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Library & Information Science (AREA)
  • Evolutionary Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

Las técnicas y sistemas se proporcionan para identificar contenido desconocido. Por ejemplo, se puede determinar que una cantidad de vectores fuera de la pluralidad de vectores proyectados desde un punto de origen, está entre un punto de datos de referencia y un punto de datos desconocidos. La cantidad de vectores puede ser utilizada para estimar un ángulo entre un primer vector (desde el punto de origen a un punto de datos de referencia) y un segundo vector (desde el punto de origen a un punto de datos desconocidos). Luego se puede determinar la distancia entre el punto de datos de referencia y el punto de datos desconocidos. Al utilizar la distancia determinada, se pueden determinar puntos de datos candidatos de un conjunto de puntos de datos de referencia. Los puntos de datos candidatos pueden ser analizados para identificar el punto de datos desconocidos.
MX2017013128A 2015-04-17 2016-04-15 Sistemas y metodos para reducir densidad de los datos en grandes conjuntos de datos. MX2017013128A (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201562149193P 2015-04-17 2015-04-17
PCT/US2016/027691 WO2016168556A1 (en) 2015-04-17 2016-04-15 Systems and methods for reducing data density in large datasets

Publications (1)

Publication Number Publication Date
MX2017013128A true MX2017013128A (es) 2018-01-26

Family

ID=55971188

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2017013128A MX2017013128A (es) 2015-04-17 2016-04-15 Sistemas y metodos para reducir densidad de los datos en grandes conjuntos de datos.

Country Status (8)

Country Link
US (1) US10482349B2 (es)
EP (2) EP4375952A2 (es)
CN (1) CN107949849B (es)
AU (1) AU2016250276B2 (es)
CA (1) CA2982797C (es)
CL (1) CL2017002612A1 (es)
MX (1) MX2017013128A (es)
WO (1) WO2016168556A1 (es)

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