Combining shape and sift features for 3-D object detection and pose estimation

Yoon Sik Tak, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We carried out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.

Original languageEnglish
Pages (from-to)429-435
Number of pages7
JournalTransactions of the Korean Institute of Electrical Engineers
Issue number2
Publication statusPublished - 2010 Feb


  • 3-D object retrieval
  • Distance curve
  • Pose estimation
  • SIFT
  • Shape-based retrieval

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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