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Energy Minimization Methods in Computer Vision and Pattern Recognition [electronic resource] : 9th International Conference, EMMCVPR 2013, Lund, Sweden, August 19-21, 2013. Proceedings /

Contributor(s): Material type: TextTextSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 8081Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Edition: 1st ed. 2013Description: XII, 363 p. 130 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642403958
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.4 23
LOC classification:
  • Q337.5
  • TK7882.P3
Online resources:
Contents:
Medical Imaging -- Image Editing -- 3D Reconstruction -- Shape Matching -- Scene Understanding -- Segmentation; Superpixels -- Statistical Methods and Learning.
In: Springer Nature eBookSummary: This volume constitutes the refereed proceedings of the 9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013, held in Lund, Sweden, in August 2013. The 26 revised full papers were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections on Medical Imaging; Image Editing; 3D Reconstruction; Shape Matching; Scene Understanding; Segmentation; Superpixels; Statistical Methods and Learning.
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Medical Imaging -- Image Editing -- 3D Reconstruction -- Shape Matching -- Scene Understanding -- Segmentation; Superpixels -- Statistical Methods and Learning.

This volume constitutes the refereed proceedings of the 9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013, held in Lund, Sweden, in August 2013. The 26 revised full papers were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections on Medical Imaging; Image Editing; 3D Reconstruction; Shape Matching; Scene Understanding; Segmentation; Superpixels; Statistical Methods and Learning.

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