Generative statistical 3D reconstruction of unfoliaged trees from terrestrial images
العنوان: | Generative statistical 3D reconstruction of unfoliaged trees from terrestrial images |
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المؤلفون: | Helmut Mayer, Hai Huang |
المصدر: | Annals of GIS. 15:97-105 |
بيانات النشر: | Informa UK Limited, 2009. |
سنة النشر: | 2009 |
مصطلحات موضوعية: | business.industry, Gaussian, 3D reconstruction, Pattern recognition, Computer Science Applications, Branching (linguistics), symbols.namesake, Generative model, Maximum a posteriori estimation, symbols, General Earth and Planetary Sciences, Clutter, Artificial intelligence, Likelihood function, business, Generative grammar, Mathematics |
الوصف: | This article presents a generative statistical approach for the automatic three-dimensional (3D) extraction and reconstruction of unfoliaged deciduous trees from terrestrial wide-baseline image sequences. Unfoliaged trees are difficult to reconstruct from images because of partially weak contrast, background clutter, occlusions, and particularly the possibly varying order of branches in images from different viewpoints. This work combines generative modeling by L-systems and a statistical approach for maximum a posteriori estimation for the reconstruction of the 3D branching structure of trees. Background estimation is conducted by means of gray scale morphology to provide a good basis for generative modeling. A Gaussian likelihood function based on intensity differences is used to evaluate the hypotheses. The target tree is classified into three typical branching types after the extraction of the first level of branches and specific production rules of an L-system are used. Generic prior distributions fo... |
تدمد: | 1947-5691 1947-5683 |
الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::51b21862d72ffdd4dea47cc9e334dc6dTest https://doi.org/10.1080/19475680903464621Test |
حقوق: | OPEN |
رقم الانضمام: | edsair.doi...........51b21862d72ffdd4dea47cc9e334dc6d |
قاعدة البيانات: | OpenAIRE |
تدمد: | 19475691 19475683 |
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