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020 _a9783031018237
_9978-3-031-01823-7
024 7 _a10.1007/978-3-031-01823-7
_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 _aDana, Kristin J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980191
245 1 0 _aComputational Texture and Patterns
_h[electronic resource] :
_bFrom Textons to Deep Learning /
_cby Kristin J. Dana.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 99 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 _aPreface -- Acknowledgments -- Visual Patterns and Texture -- Textons in Human and Computer Vision -- Texture Recognition -- Texture Segmentation -- Texture Synthesis -- Texture Style Transfer -- Return of the Pyramids -- Open Issues in Understanding Visual Patterns -- Applications for Texture and Patterns -- Tools for Mining Patterns: Cloud Services and Software Libraries -- Bibliography -- Author's Biography.
520 _aVisual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance-to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adaptingto new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_980192
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_980193
650 2 4 _aAutomated Pattern Recognition.
_931568
710 2 _aSpringerLink (Online service)
_980194
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000805
776 0 8 _iPrinted edition:
_z9783031006951
776 0 8 _iPrinted edition:
_z9783031029516
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_980195
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01823-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c84915
_d84915