Probabilistic and Biologically Inspired Feature Representations (Record no. 84606)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03598nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01822-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163435.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2018 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031018220 |
| -- | 978-3-031-01822-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006 |
| 100 1# - AUTHOR NAME | |
| Author | Felsberg, Michael. |
| 245 10 - TITLE STATEMENT | |
| Title | Probabilistic and Biologically Inspired Feature Representations |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 89 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Computer Vision, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface -- Acknowledgments -- Introduction -- Basics of Feature Design -- Channel Coding of Features -- Channel-Coded Feature Maps -- CCFM Decoding and Visualization -- Probabilistic Interpretation of Channel Representations -- Conclusions -- Bibliography -- Author's Biography -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife-they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Digital techniques. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01822-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2018. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| -- | rdacarrier |
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| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Image processing |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern recognition systems. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Vision. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automated Pattern Recognition. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2153-1064 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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