![]() to infer the correspondence between name and face. The system will then attempt to propagate names from otogra level to face level, i.e. In this paper, we propose and investigate a new user scenario for face annotation, in which users are allowed to multi-select a group of otogras and assign names to these otogras. Finally, we provide the first large standard data set for face detection, so that future researches on the topic can be compared on the same training and testing set. Experimental results show that all the new techniques effectively improve the detection performance. Moreover, we propose a strategy for using our dynamic cascade algorithm with multiple sets of features to further improve the detection performance without significant increase in the detector's computational cost. It produces more stable boost classifiers with fewer features. The second is the introduction of a new kind of weak classifier, called "Bayesian stump", for training boost classifiers. The first is a new cascade algorithm called "dynamic cascade ", which can train cascade classifiers on massive data sets and only requires a small number of training parameters. In this paper, we propose a novel method, called "dynamic cascade", for training an efficient face detector on massive data sets. The results indicate that our approach performs excellently in stylization and personalization for images and videos. We also extend our algorithm from single image stylization to video personalization, by maintaining the temporal coherence and identifying faces in video sequences. Compared to other algorithms, our method not only synthesizes the style, but also preserves the image content well. To combine the draft component and the obtained style information, the final artistic result can be achieved via a reconstruction step. Style transfer is formulated as a global optimization problem by using Markov random fields, and a coarse-to-fine belief propagation algorithm is used to solve the optimization problem. Then the style is transferred from the template image to the source image in the paint and edge components. First, inspired by the steps of drawing a picture, an image is decomposed into three components: draft, paint and edge, which describe the content, main style, and strengthened strokes along the boundaries. ![]() In this paper, we propose a style transfer algorithm via a novel component analysis approach, based on various image processing techniques. ![]() However, most existing methods do not consider the content and style separately. Example-based stylization provides an easy way of making artistic effects for images and videos.
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