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  • 1
    Online Resource
    Online Resource
    New York, NY [u.a.] : Springer
    UID:
    b3kat_BV040124543
    Format: 1 Online-Ressource
    ISBN: 9781441993267
    Additional Edition: Erscheint auch als Druckausgabe ISBN 978-1-4419-9325-0
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    San Rafael, Calif. : Morgan & Claypool
    UID:
    gbv_1653282843
    Format: X, 128 S. , Ill., graph. Darst.
    Edition: Online-Ausg. Online-Ressource Synthesis digital library of engineering and computer science
    Edition: Computer & information science. collection three
    ISBN: 9781608451333 , 160845133X
    Series Statement: Synthesis lectures on computer vision 2
    Note: Includes bibliographical references (pages 113-126)
    Additional Edition: ISBN 9781608451340
    Additional Edition: ISBN 1608451348
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Book
    Book
    New York [u.a.] :Springer,
    UID:
    almahu_BV040099969
    Format: VIII, 329 S. : , Ill., graph. Darst.
    Edition: 2012
    ISBN: 978-1-4419-9325-0
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen
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  • 4
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Imprint: Springer
    UID:
    gbv_1823900755
    Format: 1 Online-Ressource(XCVIII, 6 p.)
    Edition: 1st ed. 2006.
    ISBN: 9783031022418
    Series Statement: Synthesis Lectures on Image, Video, and Multimedia Processing
    Content: The Light Field -- Light Field Spectral Analysis -- Light Field Uniform Sampling -- The Freeform Sampling Framework -- Light Field Active Sampling -- The Self-Reconfigurable Camera Array -- Conclusions and Future Work.
    Content: Light field is one of the most representative image-based rendering techniques that generate novel virtual views from images instead of 3D models. The light field capture and rendering process can be considered as a procedure of sampling the light rays in the space and interpolating those in novel views. As a result, light field can be studied as a high-dimensional signal sampling problem, which has attracted a lot of research interest and become a convergence point between computer graphics and signal processing, and even computer vision. This lecture focuses on answering two questions regarding light field sampling, namely how many images are needed for a light field, and if such number is limited, where we should capture them. The book can be divided into three parts. First, we give a complete analysis on uniform sampling of IBR data. By introducing the surface plenoptic function, we are able to analyze the Fourier spectrum of non-Lambertian and occluded scenes. Given the spectrum, we also apply the generalized sampling theorem on the IBR data, which results in better rendering quality than rectangular sampling for complex scenes. Such uniform sampling analysis provides general guidelines on how the images in IBR should be taken. For instance, it shows that non-Lambertian and occluded scenes often require a higher sampling rate. Next, we describe a very general sampling framework named freeform sampling. Freeform sampling handles three kinds of problems: sample reduction, minimum sampling rate to meet an error requirement, and minimization of reconstruction error given a fixed number of samples. When the to-be-reconstructed function values are unknown, freeform sampling becomes active sampling. Algorithms of active sampling are developed for light field and show better results than the traditional uniform sampling approach. Third, we present a self-reconfigurable camera array that we developed, which features a very efficient algorithm for real-time rendering and the ability of automatically reconfiguring the cameras to improve the rendering quality. Both are based on active sampling. Our camera array is able to render dynamic scenes interactively at high quality. To the best of our knowledge, it is the first camera array that can reconfigure the camera positions automatically.
    Additional Edition: ISBN 9783031011139
    Additional Edition: ISBN 9783031033698
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031011139
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031033698
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    [San Rafael] : Morgan & Claypool Publishers
    UID:
    gbv_723616752
    Format: 1 Online-Ressource (102 Seiten)
    Edition: Electronic reproduction; Available via World Wide Web
    ISBN: 1598290770 , 9781598290776
    Series Statement: Synthesis Lectures on Image, Video, and Multimedia Processing #6
    Content: Light field is one of the most representative image-based rendering techniques that generate novel virtual views from images instead of 3D models. The light field capture and rendering process can be considered as a procedure of sampling the light rays in the space and interpolating those in novel views. As a result, light field can be studied as a high-dimensional signal sampling problem, which has attracted a lot of research interest and become a convergence point between computer graphics and signal processing, and even computer vision. This lecture focuses on answering two questions regarding light field sampling, namely how many images are needed for a light field, and if such number is limited, where we should capture them. The book can be divided into three parts. First, we give a complete analysis on uniform sampling of IBR data. By introducing the surface plenoptic function, we are able to analyze the Fourier spectrum of non-Lambertian and occluded scenes. Given the spectrum, we also apply the generalized sampling theorem on the IBR data, which results in better rendering quality than rectangular sampling for complex scenes. Such uniform sampling analysis provides general guidelines on how the images in IBR should be taken. For instance, it shows that non-Lambertian and occluded scenes often require a higher sampling rate. Next, we describe a very general sampling framework named freeform sampling. Freeform sampling handles three kinds of problems: sample reduction, minimum sampling rate to meet an error requirement, and minimization of reconstruction error given a fixed number of samples. When the to-be-reconstructed function values are unknown, freeform sampling becomes active sampling. Algorithms of active sampling are developed for light field and show better results than the traditional uniform sampling approach. Third, we present a self-reconfigurable camera array that we developed, which features a very efficient algorithm for real-time rendering and the ability of automatically reconfiguring the cameras to improve the rendering quality. Both are based on active sampling. Our camera array is able to render dynamic scenes interactively at high quality. To the best of our knowledge, it is the first camera array that can reconfigure the camera positions automatically
    Content: The light field -- Light field spectral analysis -- Light field uniform sampling -- The free form sampling framework -- Light field active sampling -- The self-reconfigurable camera array -- Conclusions and futurework
    Note: Description based upon print version of record , The light fieldLight field spectral analysis -- Light field uniform sampling -- The free form sampling framework -- Light field active sampling -- The self-reconfigurable camera array -- Conclusions and futurework. , Electronic reproduction; Available via World Wide Web , System requirements: PDF reader. , Mode of access: World Wide Web.
    Additional Edition: ISBN 1598290762
    Additional Edition: ISBN 9781598290769
    Additional Edition: Erscheint auch als Druck-Ausgabe Light Field Sampling
    Language: English
    Keywords: Electronic books
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    [San Rafael] : Morgan & Claypool Publishers
    UID:
    gbv_723615810
    Format: 1 Online-Ressource (140 Seiten)
    Edition: Also available in print
    ISBN: 9781608451340
    Series Statement: Synthesis Lectures on Computer Vision #2
    Content: Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms.We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning
    Content: 1. A brief survey of the face detection literature -- Introduction -- The Viola-Jones face detector -- The integral image -- AdaBoost learning -- The attentional cascade structure -- Recent advances in face detection -- Feature extraction -- Variations of the boosting learning algorithm -- Other learning schemes -- Book overview --
    Content: 2. Cascade-based real-time face detection -- Soft-cascade training -- Fat stumps -- Multiple instance pruning -- Pruning using the final classification -- Multiple instance pruning -- Experimental results --
    Content: 3. Multiple instance learning for face detection -- MILboost -- Noisy-or MILboost -- ISR MILboost -- Application of MILboost to low resolution face detection -- Multiple category boosting -- Probabilistic McBoost -- Winner-take-all McBoost -- Experimental results -- A practical multi-view face detector --
    Content: 4. Detector adaptation -- Problem formulation -- Parametric learning -- Detector adaptation -- Taylor-expansion-based adaptation -- Adaptation of logistic regression classifiers -- Logistic regression -- Adaptation of logistic regression classifier -- Direct labels -- Similarity labels -- Adaptation of boosting classifiers -- Discussions and related work -- Experimental results -- Results on direct labels -- Results on similarity labels --
    Content: 5. Other applications -- Face verification with boosted multi-task learning -- Introduction -- AdaBoosting LBP -- Boosted multi-task learning -- Experimental results -- Boosting-based multimodal speaker detection -- Introduction -- Related works -- Sound source localization -- Boosting-based multimodal speaker detection -- Merge of detected windows -- Alternative speaker detection algorithms -- Experimental results --
    Content: 6. Conclusions and future work -- Bibliography -- Authors' biographies
    Note: Description based upon print version of record , Preface; A Brief Survey of the Face Detection Literature; Introduction; The Viola-Jones Face Detector; The Integral Image; AdaBoost Learning; The Attentional Cascade Structure; Recent Advances in Face Detection; Feature Extraction; Variations of the Boosting Learning Algorithm; Other Learning Schemes; Book Overview; Cascade-based Real-Time Face Detection; Soft-Cascade Training; Fat Stumps; Multiple Instance Pruning; Pruning Using the Final Classification; Multiple Instance Pruning; Experimental Results; Multiple Instance Learning for Face Detection; MILBoost; Noisy-OR MILBoost; ISR MILBoost , Application of MILBoost to Low Resolution Face DetectionMultiple Category Boosting; Probabilistic McBoost; Winner-Take-All McBoost; Experimental Results; A Practical Multi-view Face Detector; Detector Adaptation; Problem Formulation; Parametric Learning; Detector Adaptation; Taylor-Expansion-Based Adaptation; Adaptation of Logistic Regression Classifiers; Logistic Regression; Adaptation of Logistic Regression Classifier; Direct Labels; Similarity Labels; Adaptation of Boosting Classifiers; Discussions and Related Work; Experimental Results; Results on Direct Labels , Results on Similarity LabelsOther Applications; Face Verification with Boosted Multi-Task Learning; Introduction; AdaBoosting LBP; Boosted Multi-Task Learning; Experimental Results; Boosting-based Multimodal Speaker Detection; Introduction; Related Works; Sound Source Localization; Boosting-Based Multimodal Speaker Detection; Merge of Detected Windows; Alternative Speaker Detection Algorithms; Experimental Results; Conclusions and Future Work; Bibliography; Authors' Biographies; , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader.
    Additional Edition: ISBN 9781608451333
    Additional Edition: Erscheint auch als Druck-Ausgabe Boosting-Based Face Detection and Adaptation
    Language: English
    Keywords: Electronic books
    Library Location Call Number Volume/Issue/Year Availability
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