A Close-Form Iterative Algorithm for Depth Inferring from a Single Image View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Yang Cao , Yan Xia , Zengfu Wang

ABSTRACT

Inferring depth from a single image is a difficult task in computer vision, which needs to utilize adequate monocular cues contained in the image. Inspired by Saxena et al’s work, this paper presents a close-form iterative algorithm to process multi-scale image segmentation and depth inferring alternately, which can significantly improve segmentation and depth estimate results. First, an EM-based algorithm is applied to obtain an initial multi-scale image segmentation result. Then, the multi-scale Markov random field (MRF) model, trained by supervised learning, is used to infer both depths and the relations between depths at different image regions. Next, a graph-based region merging algorithm is applied to merge the segmentations at the larger scales by incorporating the inferred depths. At the last, the refined multi-scale image segmentations are used as input of MRF model and the depth are re-inferred. The above processes are iteratively continued until the expected results are achieved. Since there are no changes on the segmentations at the finest scale in the iterative process, it still can capture the detailed 3D structure. Meanwhile, the refined segmentations at the other scales will help obtain more global structure information in the image. The contrastive experimental results verify the validity of our method that it can infer quantitatively better depth estimations for 62.7% of 134 images downloaded from the Saxena’s database. Our method can also improve the image segmentation results in the sense of scene interpretation. Moreover, the paper extends the method to estimate the depth of the scene with fore-objects. More... »

PAGES

729-742

References to SciGraph publications

  • 2004-09. Efficient Graph-Based Image Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2002-07. Geotensity: Combining Motion and Lighting for 3D Surface Reconstruction in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1996-02. Direct computation of shape cues using scale-adapted spatial derivative operators in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1997-06. Computing Local Surface Orientation and Shape from Texture for Curved Surfaces in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2002-04. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008-10. Putting Objects in Perspective in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008-01. 3-D Depth Reconstruction from a Single Still Image in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Computer Vision – ECCV 2010

    ISBN

    978-3-642-15554-3
    978-3-642-15555-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15555-0_53

    DOI

    http://dx.doi.org/10.1007/978-3-642-15555-0_53

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1043642084


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