Learning Local Objective Functions for Robust Face Model Fitting
Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms h...
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| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 30; no. 8; pp. 1357 - 1370 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Los Alamitos, CA
IEEE
01.08.2008
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539 |
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| Abstract | Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc, based on implicit and domain-dependent knowledge. In this article, we address the root of the problem by learning more robust objective functions. First, we formulate a set of desirable properties for objective functions and give a concrete example function that has these properties. Then, we propose a novel approach that learns an objective function from training data generated by manual image annotations and this ideal objective function. In this approach, critical decisions such as feature selection are automated, and the remaining manual steps hardly require domain-dependent knowledge. Furthermore, an extensive empirical evaluation demonstrates that the obtained objective functions yield more robustness. Learned objective functions enable fitting algorithms to determine the best model fit more accurately than with designed objective functions. |
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| AbstractList | Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc, based on implicit and domain-dependent knowledge. In this article, we address the root of the problem by learning more robust objective functions. First, we formulate a set of desirable properties for objective functions and give a concrete example function that has these properties. Then, we propose a novel approach that learns an objective function from training data generated by manual image annotations and this ideal objective function. In this approach, critical decisions such as feature selection are automated, and the remaining manual steps hardly require domain-dependent knowledge. Furthermore, an extensive empirical evaluation demonstrates that the obtained objective functions yield more robustness. Learned objective functions enable fitting algorithms to determine the best model fit more accurately than with designed objective functions. Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond [abstract truncated by publisher]. Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc, based on implicit and domain-dependent knowledge. In this article, we address the root of the problem by learning more robust objective functions. First, we formulate a set of desirable properties for objective functions and give a concrete example function that has these properties. Then, we propose a novel approach that learns an objective function from training data generated by manual image annotations and this ideal objective function. In this approach, critical decisions such as feature selection are automated, and the remaining manual steps hardly require domain-dependent knowledge. Furthermore, an extensive empirical evaluation demonstrates that the obtained objective functions yield more robustness. Learned objective functions enable fitting algorithms to determine the best model fit more accurately than with designed objective functions.Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc, based on implicit and domain-dependent knowledge. In this article, we address the root of the problem by learning more robust objective functions. First, we formulate a set of desirable properties for objective functions and give a concrete example function that has these properties. Then, we propose a novel approach that learns an objective function from training data generated by manual image annotations and this ideal objective function. In this approach, critical decisions such as feature selection are automated, and the remaining manual steps hardly require domain-dependent knowledge. Furthermore, an extensive empirical evaluation demonstrates that the obtained objective functions yield more robustness. Learned objective functions enable fitting algorithms to determine the best model fit more accurately than with designed objective functions. [...] we formulate a set of desirable properties for objective functions and give a concrete example function that has these properties. |
| Author | Radig, B. Pietzsch, S. Wimmer, M. Stulp, F. |
| Author_xml | – sequence: 1 givenname: M. surname: Wimmer fullname: Wimmer, M. organization: Inf. 9-Bildverstehen und Wissensbasierte Syst., Tech. Univ. Munchen, Garching – sequence: 2 givenname: F. surname: Stulp fullname: Stulp, F. – sequence: 3 givenname: S. surname: Pietzsch fullname: Pietzsch, S. – sequence: 4 givenname: B. surname: Radig fullname: Radig, B. |
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| Keywords | Model-based coding Computer vision Modeling and recovery of physical attributes Shape Image Processing and Computer Vision Vision and Scene Understanding Computational models of vision Real-time systems Object recognition Texture Pattern matching Face and gesture recognition Image interpretation Objective analysis Gesture recognition Global optimum Real time system Annotation Search algorithm face and gesture recognition Model matching Scene analysis real-time systems vision and scene understanding Facies Objective function Pattern analysis Artificial intelligence |
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| SubjectTerms | Acceleration Algorithm design and analysis Algorithms Applied sciences Artificial Intelligence Computational models of vision Computer science; control theory; systems Computer Simulation Computer vision Concrete Exact sciences and technology Face - anatomy & histology Face and gesture recognition Face detection Face recognition Fittings Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image Processing and Computer Vision Imaging, Three-Dimensional - methods Layout Learning Mathematical models Model-based coding Modeling and recovery of physical attributes Models, Anatomic Object recognition Optimization Pattern analysis Pattern matching Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Real time systems Reproducibility of Results Robustness Sensitivity and Specificity Shape Studies Subtraction Technique Texture Training data Vision and Scene Understanding |
| Title | Learning Local Objective Functions for Robust Face Model Fitting |
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