Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting
| Autor: | Mircea Ionita, Claudia Lindner, Paul A. Bromiley, Timothy F. Cootes |
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| Rok vydání: | 2015 |
| Předmět: |
Computer science
Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Pattern Recognition Automated Discriminative model Artificial Intelligence Image Processing Computer-Assisted Humans Computer vision Models Statistical Pixel business.industry Applied Mathematics Pattern recognition Active appearance model Random forest Computational Theory and Mathematics Feature (computer vision) Face Pattern recognition (psychology) Regression Analysis Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 37:1862-1874 |
| ISSN: | 2160-9292 0162-8828 |
| Popis: | A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection. |
| Databáze: | OpenAIRE |
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