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Title: Identification of uncommon objects in containers

Abstract

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Inventors:
; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1389841
Patent Number(s):
9,760,801
Application Number:
14/709,741
Assignee:
Lawrence Livermore National Security, LLC LLNL
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Patent
Resource Relation:
Patent File Date: 2015 May 12
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS; 97 MATHEMATICS AND COMPUTING

Citation Formats

Bremer, Peer-Timo, Kim, Hyojin, and Thiagarajan, Jayaraman J. Identification of uncommon objects in containers. United States: N. p., 2017. Web.
Bremer, Peer-Timo, Kim, Hyojin, & Thiagarajan, Jayaraman J. Identification of uncommon objects in containers. United States.
Bremer, Peer-Timo, Kim, Hyojin, and Thiagarajan, Jayaraman J. Tue . "Identification of uncommon objects in containers". United States. doi:. https://www.osti.gov/servlets/purl/1389841.
@article{osti_1389841,
title = {Identification of uncommon objects in containers},
author = {Bremer, Peer-Timo and Kim, Hyojin and Thiagarajan, Jayaraman J.},
abstractNote = {A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 12 00:00:00 EDT 2017},
month = {Tue Sep 12 00:00:00 EDT 2017}
}

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Works referenced in this record:

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
journal, November 2012

  • Achanta, R.; Shaji, A.; Smith, K.
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 11, p. 2274-2282
  • DOI: 10.1109/TPAMI.2012.120

Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
conference, January 2001

  • Boykov, Y.Y.; Jolly, M.-P.
  • Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001
  • DOI: 10.1109/ICCV.2001.937505

Local Discriminant Embedding and Its Variants
conference, January 2005

  • Chen, Hwann-Tzong; Chang, Huang-Wei; Liu, Tyng-Luh
  • 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • DOI: 10.1109/CVPR.2005.216

Reference-Based Scheme Combined With K-SVD for Scene Image Categorization
journal, January 2013

  • Li, Qun; Zhang, Honggang; Guo, Jun
  • IEEE Signal Processing Letters, Vol. 20, Issue 1, p. 67-70
  • DOI: 10.1109/LSP.2012.2228852