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Multi-modal statistics of local image structures and its applications for depth prediction

Başlık çevirisi mevcut değil.

  1. Tez No: 400162
  2. Yazar: SİNAN KALKAN
  3. Danışmanlar: PROF. FLORANTİN WÖRGÖTTER, PROF. NORBERT KRÜGER
  4. Tez Türü: Doktora
  5. Konular: Matematik, Mathematics
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2007
  8. Dil: İngilizce
  9. Üniversite: Georg-August-Unıversıtat Gottıngen
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

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Özet (Çeviri)

Processing in most artificial vision systems and in the human vision system starts with early vision whichinvolves the extraction of local visual modalities (like optical flow, disparity and contrast transition etc.)and local image structures (edge-like, junction-like and texture-like structures). Since information inearly vision is processed only locally, it is inherently ambiguous. For example, estimation of optical flowfaces the aperture problem, and thus, only the flow along the intensity gradient is computable for edgelikestructures. Moreover, the extracted flow information at weakly-textured image areas are unreliable.Analogously, stereopsis needs to deal with the correspondence problem: as correspondences at weaklytextured image areas cannot be found, the disparity information at such places is not accurate. Oneway to deal with the missing and ambiguous information is to make use of the redundancy of visualinformation by exploiting the statistical regularities of natural scenes. Such regularities are carried in thevisual system using feedback mechanisms between different layers, or by lateral connections within alayer.This thesis is interested in the ambiguities and the biased and missing information in the processing ofoptic flow, stereo and junctions using statistical means. It uses statistical properties of images to analyzethe extent of the ambiguous processing in optical flow estimation and whether the missing informationin stereo can be recovered using interpolation of depth information at edge-like structures. Moreover, itproposes a feedback mechanism for dealing with the bias in junction detection, and another model forrecovering the missing depth information in stereo computation using only the depth information at theedges.

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