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Automated scene interpretation has benefited from advances in machine learning, and restricted tasks, such as face detection, have been solved with sufficient accuracy for restricted settings. However, the performance of machines in providing rich semantic descriptions of natural scenes from digital images remains highly limited and hugely inferior to that(More)
We describe a general and exact method to speed up the training of linear object detection systems operating in a sliding, multi-scale window fashion, such as deformable part-based models. Our approach consists of reformulating the computation of the gradient as a convolution, and making use of properties of the Fourier transform to obtain a speedup factor(More)
This paper presents a novel method of enhancing image quality of face pictures using 3D and spectral information. Most conventional techniques directly work on the image data, shifting the skin color to a predefined skin tone, and thus do not take into account the effects of shape and lighting. The proposed method first recovers the 3D shape of a face in an(More)
Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern for the computational cost. Some applications, in particular in computer vision, may involve millions of training examples and very large feature spaces. In such contexts, the training time of off-the-shelf Boosting algorithms may become prohibitive. Several methods(More)