| 000 | 03870nam a22005655i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-52483-2 | ||
| 003 | DE-He213 | ||
| 005 | 20220801222352.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 170318s2017 sz | s |||| 0|eng d | ||
| 020 |
_a9783319524832 _9978-3-319-52483-2 |
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_a10.1007/978-3-319-52483-2 _2doi |
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_aUYQ _2bicssc |
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_a006.3 _223 |
| 100 | 1 |
_aPeters, James F. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _911752 |
|
| 245 | 1 | 0 |
_aFoundations of Computer Vision _h[electronic resource] : _bComputational Geometry, Visual Image Structures and Object Shape Detection / _cby James F. Peters. |
| 250 | _a1st ed. 2017. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
|
| 300 |
_aXVII, 431 p. 354 illus., 301 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aIntelligent Systems Reference Library, _x1868-4408 ; _v124 |
|
| 505 | 0 | _aBasics Leading to Machine Vision -- Working with Pixels -- Visualising Pixel Intensity Distributions -- Linear Filtering -- Edges, Lines, Corners, Gaussian kernel and Voronoï Meshes -- Delaunay Mesh Segmentation -- Video Processing. An Introduction to Real-Time and Offline Video Analysis -- Lowe Keypoints, Maximal Nucleus Clusters, Contours and Shapes -- Postscript. Where Do Shapes fit into the Computer Vision Landscape?. | |
| 520 | _aThis book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classification of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classification of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and first year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes. | ||
| 650 | 0 |
_aComputational intelligence. _97716 |
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| 650 | 0 |
_aComputer vision. _961118 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
|
| 650 | 0 |
_aGraph theory. _93662 |
|
| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aComputer Vision. _961119 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aGraph Theory. _93662 |
| 710 | 2 |
_aSpringerLink (Online service) _961120 |
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| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9783319524818 |
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_iPrinted edition: _z9783319524825 |
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_iPrinted edition: _z9783319849126 |
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_aIntelligent Systems Reference Library, _x1868-4408 ; _v124 _961121 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-52483-2 |
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| 942 | _cEBK | ||
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