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CL2017002612A1 - Sistemas y métodos para reducir densidad de los datos en grandes conjuntos de datos - Google Patents

Sistemas y métodos para reducir densidad de los datos en grandes conjuntos de datos

Info

Publication number
CL2017002612A1
CL2017002612A1 CL2017002612A CL2017002612A CL2017002612A1 CL 2017002612 A1 CL2017002612 A1 CL 2017002612A1 CL 2017002612 A CL2017002612 A CL 2017002612A CL 2017002612 A CL2017002612 A CL 2017002612A CL 2017002612 A1 CL2017002612 A1 CL 2017002612A1
Authority
CL
Chile
Prior art keywords
point
video data
vector
video
systems
Prior art date
Application number
CL2017002612A
Other languages
English (en)
Inventor
Zeev Neumeier
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 CL2017002612A1 publication Critical patent/CL2017002612A1/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Library & Information Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computing Systems (AREA)
  • Evolutionary Biology (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Character Discrimination (AREA)

Abstract

<p>SISTEMA QUE COMPRENDE UNO O MÁS PROCESADORES Y UNO O MÁS MEDIOS DE ALMACENAMIENTO NO TRANSITORIO LEGIBLES POR COMPUTADOR QUE REALIZAN LAS OPERACIONES DE OBTENER UNA PLURALIDAD DE PUNTOS DE DATOS DE VIDEO DE REFERENCIA, DETERMINAR EL LARGO DE UN PRIMER VECTOR DESDE UN PUNTO DE ORIGEN A UN PUNTO DE DATOS DE VIDEO DE REFERENCIA, OBTENER UN PUNTO DE DATOS DE VIDEO DESCONOCIDOS, DETERMINAR EL LARGO DE UN SEGUNDO VECTOR DESDE EL PUNTO DE ORIGEN AL PUNTO DE DATOS DE VIDEO DESCONOCIDOS, PROYECTAR UNA PLURALIDAD DE VECTORES DESDE EL PUNTO DE ORIGEN, DETERMINAR UNA CANTIDAD DE LA PLURALIDAD DE VECTORES ENTRE EL PUNTO DE DATOS DE VIDEO DE REFERENCIA Y EL PUNTO DE DATOS DE VIDEO DESCONOCIDOS, ESTIMAR UN ÁNGULO ENTRE EL PRIMER VECTOR Y EL SEGUNDO VECTOR, DETERMINAR LA DISTANCIA ENTRE EL PUNTO DE DATOS DE VIDEO DE REFERENCIA Y EL PUNTO DE DATOS DE VIDEO DESCONOCIDOS, IDENTIFICAR UNO O MÁS PUNTOS DE DATOS DE VIDEO CANDIDATOS DE LA PLURALIDAD DE PUNTOS DE DATOS DE VIDEO DE REFERENCIA E IDENTIFICAR EL CONTENIDO DE VIDEO.</p>
CL2017002612A 2015-04-17 2017-10-16 Sistemas y métodos para reducir densidad de los datos en grandes conjuntos de datos CL2017002612A1 (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US201562149193P 2015-04-17 2015-04-17

Publications (1)

Publication Number Publication Date
CL2017002612A1 true CL2017002612A1 (es) 2018-04-02

Family

ID=55971188

Family Applications (1)

Application Number Title Priority Date Filing Date
CL2017002612A CL2017002612A1 (es) 2015-04-17 2017-10-16 Sistemas y métodos para reducir densidad de los datos en grandes conjuntos de datos

Country Status (8)

Country Link
US (1) US10482349B2 (es)
EP (2) EP4375952A3 (es)
CN (1) CN107949849B (es)
AU (1) AU2016250276B2 (es)
CA (1) CA2982797C (es)
CL (1) CL2017002612A1 (es)
MX (1) MX389253B (es)
WO (1) WO2016168556A1 (es)

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