Visual object recognition
Saved in:
| Author / Creator: | Grauman, Kristen Lorraine, 1979- |
|---|---|
| Imprint: | San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2011. |
| Description: | 1 electronic text (xvii, 163 p.) : ill., digital file. |
| Language: | English |
| Series: | Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; # 11 Synthesis digital library of engineering and computer science. Synthesis lectures on artificial intelligence and machine learning, # 11. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/10511006 |
MARC
| LEADER | 00000nam a2200000 a 4500 | ||
|---|---|---|---|
| 001 | 10511006 | ||
| 005 | 20110601105908.0 | ||
| 006 | m e d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 110423s2011 caua foab 000 0 eng d | ||
| 003 | ICU | ||
| 020 | |a 9781598299694 (electronic bk.) | ||
| 020 | |z 9781598299687 (pbk.) | ||
| 024 | 7 | |a 10.2200/S00332ED1V01Y201103AIM011 |2 doi | |
| 035 | |a MC201103AIM011 | ||
| 035 | |a (CaBNVSL)gtp00547895 | ||
| 040 | |a CaBNVSL |c CaBNVSL |d CaBNVSL | ||
| 050 | 4 | |a TA1634 |b .G728 2011 | |
| 082 | 0 | 4 | |a 006.37 |2 22 |
| 100 | 1 | |a Grauman, Kristen Lorraine, |d 1979- | |
| 245 | 1 | 0 | |a Visual object recognition |h [electronic resource] / |c Kristen Grauman, Bastian Leibe. |
| 260 | |a San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : |b Morgan & Claypool, |c c2011. | ||
| 300 | |a 1 electronic text (xvii, 163 p.) : |b ill., digital file. | ||
| 490 | 1 | |a Synthesis lectures on artificial intelligence and machine learning, |x 1939-4616 ; |v # 11 | |
| 500 | |a Part of: Synthesis digital library of engineering and computer science. | ||
| 500 | |a Series from website. | ||
| 504 | |a Includes bibliographical references (p. 133-162). | ||
| 505 | 0 | |a Preface -- Acknowledgments -- Figure credits -- | |
| 505 | 8 | |a 1. Introduction -- Overview -- Challenges -- The state of the art -- | |
| 505 | 8 | |a 2. Overview: recognition of specific objects -- Global image representations -- Local feature representations -- | |
| 505 | 8 | |a 3. Local features: detection and description -- Introduction -- Detection of interest points and regions -- Keypoint localization -- Scale invariant region detection -- Affine covariant region detection -- Orientation normalization -- Summary of local detectors -- Local descriptors -- The SIFT descriptor -- The SURF detector/descriptor -- Concluding remarks -- | |
| 505 | 8 | |a 4. Matching local features -- Efficient similarity search -- Tree-based algorithms -- Hashing-based algorithms and binary codes -- A rule of thumb for reducing ambiguous matches -- Indexing features with visual vocabularies -- Creating a visual vocabulary -- Vocabulary trees -- Choices in vocabulary formation -- Inverted file indexing -- Concluding remarks -- | |
| 505 | 8 | |a 5. Geometric verification of matched features -- Estimating geometric models -- Estimating similarity transformations -- Estimating affine transformations -- Homography estimation -- More general transformations -- Dealing with outliers -- RANSAC -- Generalized Hough transform -- Discussion -- | |
| 505 | 8 | |a 6. Example systems: specific-object recognition -- Image matching -- Object recognition -- Large-scale image retrieval -- Mobile visual search -- Image auto-annotation -- Concluding remarks -- | |
| 505 | 8 | |a 7. Overview: recognition of generic object categories -- | |
| 505 | 8 | |a 8. Representations for object categories -- Window-based object representations -- Pixel intensities and colors -- Window descriptors: global gradients and texture -- Patch descriptors: local gradients and texture -- A hybrid representation: bags of visual words -- Contour and shape features -- Feature selection -- Part-based object representations -- Overview of part-based models -- Fully-connected models: the constellation model -- Star graph models -- Mixed representations -- Concluding remarks -- | |
| 505 | 8 | |a 9. Generic object detection: finding and scoring candidates -- Detection via classification -- Speeding up window-based detection -- Limitations of window-based detection -- Detection with part-based models -- Combination classifiers -- Voting and the generalized Hough transform -- RANSAC -- Generalized distance transform -- | |
| 505 | 8 | |a 10. Learning generic object category models -- Data annotation -- Learning window-based models -- Specialized similarity measures and kernels -- Learning part-based models -- Learning in the constellation model -- Learning in the implicit shape model -- Learning in the pictorial structure model -- | |
| 505 | 8 | |a 11. Example systems: generic object recognition -- The Viola-Jones face detector -- Training process -- Recognition process -- Discussion -- The HOG person detector -- Bag-of-words image classification -- Training process -- Recognition process -- Discussion -- The implicit shape model -- Training process -- Recognition process -- Vote backprojection and top-down segmentation -- Hypothesis verification -- Discussion -- Deformable part-based models -- Training process -- Recognition process -- Discussion -- | |
| 505 | 8 | |a 12. Other considerations and current challenges -- Benchmarks and datasets -- Context-based recognition -- Multi-viewpoint and multi-aspect recognition -- Role of video -- Integrated segmentation and recognition -- Supervision considerations in object category learning -- Using weakly labeled image data -- Maximizing the use of manual annotations -- Unsupervised object discovery -- Language, text, and images -- | |
| 505 | 8 | |a 13. Conclusions -- Bibliography -- Authors' biographies. | |
| 506 | |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | |a Compendex | |
| 510 | 0 | |a INSPEC | |
| 510 | 0 | |a Google scholar | |
| 510 | 0 | |a Google book search | |
| 520 | 3 | |a The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 538 | |a System requirements: Adobe Acrobat Reader. | ||
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 653 | |a Global representations versus local descriptors | ||
| 653 | |a Detection and description of local invariant features | ||
| 653 | |a Efficient algorithms for matching local features | ||
| 653 | |a Tree-based and hashing-based search algorithms | ||
| 653 | |a Visual vocabularies and bags-of-words | ||
| 653 | |a Methods to verify geometric consistency according to parameterized geometric transformations | ||
| 653 | |a Dealing with outliers in correspondences | ||
| 653 | |a RANSAC and the Generalized Hough transform | ||
| 653 | |a Window-based descriptors | ||
| 653 | |a Histograms of oriented gradients and rectangular features | ||
| 653 | |a Part-based models | ||
| 653 | |a Star graph models and fully connected constellations | ||
| 653 | |a Pyramid match kernels | ||
| 653 | |a Detection via sliding windows | ||
| 653 | |a Hough voting | ||
| 653 | |a Generalized distance transform | ||
| 653 | |a Implicit Shape Model | ||
| 653 | |a Deformable Part-based Model | ||
| 700 | 1 | |a Leibe, Bastian. | |
| 830 | 0 | |a Synthesis digital library of engineering and computer science. | |
| 830 | 0 | |a Synthesis lectures on artificial intelligence and machine learning, |x 1939-4616 ; |v # 11. | |
| 856 | 4 | 0 | |u http://dx.doi.org/10.2200/S00332ED1V01Y201103AIM011 |y Morgan & Claypool |
| 929 | |a eresource | ||
| 999 | f | f | |i faf63080-4a63-51b1-97be-6273102aa5c9 |s fae6c391-116e-52f2-91fc-a0089a822e1c |
| 928 | |t Library of Congress classification |a TA1634.G728 2011 |l Online |c UC-FullText |u http://dx.doi.org/10.2200/S00332ED1V01Y201103AIM011 |z Morgan & Claypool |g ebooks |i 8690001 | ||