MX362765B - Personalizacion del descriptor por funcionalidad en linea. - Google Patents

Personalizacion del descriptor por funcionalidad en linea.

Info

Publication number
MX362765B
MX362765B MX2016009706A MX2016009706A MX362765B MX 362765 B MX362765 B MX 362765B MX 2016009706 A MX2016009706 A MX 2016009706A MX 2016009706 A MX2016009706 A MX 2016009706A MX 362765 B MX362765 B MX 362765B
Authority
MX
Mexico
Prior art keywords
descriptor
customization
feature descriptor
define
error
Prior art date
Application number
MX2016009706A
Other languages
English (en)
Other versions
MX2016009706A (es
Inventor
Richardson Andrew
Olson Edwin
Original Assignee
Ford Global Tech Llc
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 Ford Global Tech Llc filed Critical Ford Global Tech Llc
Publication of MX2016009706A publication Critical patent/MX2016009706A/es
Publication of MX362765B publication Critical patent/MX362765B/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/469—Contour-based spatial representations, e.g. vector-coding
    • 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
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • 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
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/776—Validation; Performance evaluation

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)

Abstract

Un sistema de procesamiento de imagen incluye un dispositivo de procesamiento que cuenta con un dispositivo de memoria para almacenar instrucciones ejecutables por computadora. El dispositivo de procesamiento está programado para definir un primer descriptor en una primera imagen, generar una máscara desde el primer descriptor y definir un segundo descriptor en una segunda imagen. El dispositivo de procesamiento está además programado para comparar el primer descriptor con el segundo descriptor para definir un primer vector de error, determinar un segundo vector de error mediante la aplicación de una máscara y computar el error total del segundo vector de error para determinar un error entre el primer descriptor y el segundo descriptor.
MX2016009706A 2015-07-31 2016-07-26 Personalizacion del descriptor por funcionalidad en linea. MX362765B (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US14/814,630 US9811760B2 (en) 2015-07-31 2015-07-31 Online per-feature descriptor customization

Publications (2)

Publication Number Publication Date
MX2016009706A MX2016009706A (es) 2017-04-25
MX362765B true MX362765B (es) 2019-02-07

Family

ID=57795742

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2016009706A MX362765B (es) 2015-07-31 2016-07-26 Personalizacion del descriptor por funcionalidad en linea.

Country Status (5)

Country Link
US (1) US9811760B2 (es)
CN (1) CN106447592B (es)
DE (1) DE102016113904A1 (es)
MX (1) MX362765B (es)
RU (1) RU2708955C2 (es)

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JP7069456B2 (ja) 2017-03-17 2022-05-18 ザ・リージェンツ・オブ・ザ・ユニバーシティ・オブ・ミシガン マルチポリシー意思決定を導くための有益な結果を構築するための方法および装置
US11352023B2 (en) 2020-07-01 2022-06-07 May Mobility, Inc. Method and system for dynamically curating autonomous vehicle policies
WO2022132774A1 (en) 2020-12-14 2022-06-23 May Mobility, Inc. Autonomous vehicle safety platform system and method
WO2022133242A1 (en) 2020-12-17 2022-06-23 May Mobility, Inc. Method and system for dynamically updating an environmental representation of an autonomous agent
EP4314708A1 (en) 2021-04-02 2024-02-07 May Mobility, Inc. Method and system for operating an autonomous agent with incomplete environmental information
WO2022256249A1 (en) 2021-06-02 2022-12-08 May Mobility, Inc. Method and system for remote assistance of an autonomous agent
CN113408608A (zh) * 2021-06-17 2021-09-17 四川大学 一种基于计算机视觉的同类铜币的相似度分析方法
US11814072B2 (en) 2022-02-14 2023-11-14 May Mobility, Inc. Method and system for conditional operation of an autonomous agent

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US4969200A (en) * 1988-03-25 1990-11-06 Texas Instruments Incorporated Target autoalignment for pattern inspector or writer
KR100222989B1 (ko) * 1997-07-04 1999-10-01 윤종용 셔틀 방식의 독취 장치에서 독취된 이미지를 이진화하기 위한 이미지 처리 장치 및 방법
KR100413679B1 (ko) * 2000-10-21 2003-12-31 삼성전자주식회사 형상 기술자 추출방법
US7020305B2 (en) * 2000-12-06 2006-03-28 Microsoft Corporation System and method providing improved head motion estimations for animation
US7382897B2 (en) * 2004-04-27 2008-06-03 Microsoft Corporation Multi-image feature matching using multi-scale oriented patches
EP1850270B1 (en) 2006-04-28 2010-06-09 Toyota Motor Europe NV Robust interest point detector and descriptor
GB0700468D0 (en) * 2007-01-10 2007-02-21 Mitsubishi Electric Inf Tech Improved image identification
US8774498B2 (en) 2009-01-28 2014-07-08 Xerox Corporation Modeling images as sets of weighted features
US9348141B2 (en) * 2010-10-27 2016-05-24 Microsoft Technology Licensing, Llc Low-latency fusing of virtual and real content
WO2012136642A1 (en) 2011-04-08 2012-10-11 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Feature descriptor for image sections
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US20140310314A1 (en) * 2013-04-16 2014-10-16 Samsung Electronics Co., Ltd. Matching performance and compression efficiency with descriptor code segment collision probability optimization
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Also Published As

Publication number Publication date
CN106447592A (zh) 2017-02-22
CN106447592B (zh) 2021-10-15
RU2016129943A3 (es) 2019-09-27
RU2708955C2 (ru) 2019-12-12
DE102016113904A1 (de) 2017-02-02
US20170032217A1 (en) 2017-02-02
MX2016009706A (es) 2017-04-25
RU2016129943A (ru) 2018-01-29
US9811760B2 (en) 2017-11-07

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