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008 220601s2010 sz | s |||| 0|eng d
020 _a9783031018084
_9978-3-031-01808-4
024 7 _a10.1007/978-3-031-01808-4
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aKang, Sing Bang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987651
245 1 0 _aImage-Based Modeling of Plants and Trees
_h[electronic resource] /
_cby Sing Bang Kang, Long Quan.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aX, 74 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Computer Vision,
_x2153-1064
505 0 _aIntroduction -- Review of Plant and Tree Modeling Techniques -- Image-Based Technique for Modeling Plants -- Image-Based Technique for Modeling Trees -- Single Image Tree Modeling -- Summary and Concluding Remarks -- Acknowledgments.
520 _aPlants and trees are among the most complex natural objects. Much work has been done attempting to model them, with varying degrees of success. In this book, we review the various approaches in computer graphics, which we categorize as rule-based, image-based, and sketch-based methods. We describe our approaches for modeling plants and trees using images. Image-based approaches have the distinct advantage that the resulting model inherits the realistic shape and complexity of a real plant or tree. We use different techniques for modeling plants (with relatively large leaves) and trees (with relatively small leaves).With plants, we model each leaf from images, while for trees, the leaves are only approximated due to their small size and large number. Both techniques start with the same initial step of structure from motion on multiple images of the plant or tree that is to be modeled. For our plant modeling system, because we need to model the individual leaves, these leaves need to besegmented out from the images. We designed our plant modeling system to be interactive, automating the process of shape recovery while relying on the user to provide simple hints on segmentation. Segmentation is performed in both image and 3D spaces, allowing the user to easily visualize its effect immediately. Using the segmented image and 3D data, the geometry of each leaf is then automatically recovered from the multiple views by fitting a deformable leaf model. Our system also allows the user to easily reconstruct branches in a similar manner. To model trees, because of the large leaf count, small image footprint, and widespread occlusions, we do not model the leaves exactly as we do for plants. Instead, we populate the tree with leaf replicas from segmented source images to reconstruct the overall tree shape. In addition, we use the shape patterns of visible branches to predict those of obscured branches. As a result, we are able to design our tree modeling system so as to minimize user intervention. We also handle the special case of modeling a tree from only a single image. Here, the user is required to draw strokes on the image to indicate the tree crown (so that the leaf region is approximately known) and to refine the recovery of branches. As before, we concatenate the shape patterns from a library to generate the 3D shape. To substantiate the effectiveness of our systems, we show realistic reconstructions of a variety of plants and trees from images. Finally, we offer our thoughts on improving our systems and on the remaining challenges associated with plant and tree modeling. Table of Contents: Introduction / Review of Plant and Tree Modeling Techniques / Image-Based Technique for Modeling Plants / Image-Based Technique for Modeling Trees / Single Image Tree Modeling / Summary and Concluding Remarks / Acknowledgments.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_987652
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_987654
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aQuan, Long.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987655
710 2 _aSpringerLink (Online service)
_987657
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031006807
776 0 8 _iPrinted edition:
_z9783031029363
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_987658
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01808-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c86130
_d86130