WO2013034878A3 - Image processing - Google Patents

Image processing Download PDF

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Publication number
WO2013034878A3
WO2013034878A3 PCT/GB2012/000705 GB2012000705W WO2013034878A3 WO 2013034878 A3 WO2013034878 A3 WO 2013034878A3 GB 2012000705 W GB2012000705 W GB 2012000705W WO 2013034878 A3 WO2013034878 A3 WO 2013034878A3
Authority
WO
WIPO (PCT)
Prior art keywords
signal
data signal
thereafter
visual saliency
resolution
Prior art date
Application number
PCT/GB2012/000705
Other languages
French (fr)
Other versions
WO2013034878A2 (en
Inventor
Toby BRECKON
Ioannis KATRAMADOS
Original Assignee
Cranfield University
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 Cranfield University filed Critical Cranfield University
Publication of WO2013034878A2 publication Critical patent/WO2013034878A2/en
Publication of WO2013034878A3 publication Critical patent/WO2013034878A3/en

Links

Classifications

    • 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
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • 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]

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

Apparatus for generating a visual saliency data signal S comprises an input for an image data signal U1 of resolution w x h and an output for a visual saliency data signal S. The apparatus is configured to successively downsample the image data signal U1 using a Gaussian filter n-1 times to create a first Gaussian pyramid having an nth data level signal Un of resolution (w/2n-1) x (h/2n-1); thereafter successively upsample the data level signal Un using a Gaussian filter n-1 times to create a second Gaussian pyramid having a base data level signal D1, thereafter calculate a minimum ratio signal matrix M, where and thereafter generate a visual saliency data signal S, wherein Sij = 1 - Mij.
PCT/GB2012/000705 2011-09-09 2012-09-10 Image processing WO2013034878A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GBGB1115600.7A GB201115600D0 (en) 2011-09-09 2011-09-09 Image processing
GB1115600.7 2011-09-09

Publications (2)

Publication Number Publication Date
WO2013034878A2 WO2013034878A2 (en) 2013-03-14
WO2013034878A3 true WO2013034878A3 (en) 2013-04-25

Family

ID=44908309

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/GB2012/000705 WO2013034878A2 (en) 2011-09-09 2012-09-10 Image processing

Country Status (2)

Country Link
GB (1) GB201115600D0 (en)
WO (1) WO2013034878A2 (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105551060A (en) * 2015-12-10 2016-05-04 电子科技大学 Infrared weak small object detection method based on space-time significance and quaternary cosine transformation
EP3489901A1 (en) * 2017-11-24 2019-05-29 V-Nova International Limited Signal encoding

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"Field Programmable Logic and Application", vol. 1614, 1 January 1999, SPRINGER BERLIN HEIDELBERG, Berlin, Heidelberg, ISBN: 978-3-54-045234-8, ISSN: 0302-9743, article STÉPHANE BRES ET AL: "Detection of Interest Points for Image Indexation", pages: 427 - 435, XP055053120, DOI: 10.1007/3-540-48762-X_53 *
IOANNIS KATRAMADOS ET AL: "Real-time visual saliency by Division of Gaussians", IMAGE PROCESSING (ICIP), 2011 18TH IEEE INTERNATIONAL CONFERENCE ON, IEEE, 11 September 2011 (2011-09-11), pages 1701 - 1704, XP032079937, ISBN: 978-1-4577-1304-0, DOI: 10.1109/ICIP.2011.6115785 *
LINDEBERG TONY: "Scale-Space Theory in Computer Vision", 1994, KLUWER ACADEMIC PUBLISHERS, Dortrecht, NL, ISBN: 0-7923-9418-6, pages: 33 - 43, XP002692128 *

Also Published As

Publication number Publication date
GB201115600D0 (en) 2011-10-26
WO2013034878A2 (en) 2013-03-14

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