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Imitation cartoon drawing is an important skill for cartoonists, requiring quantity of efforts on practising and guidance. In this paper, we propose EvaToon, an imitated drawing evaluate system, which automatically assigns judging scores and marks improper drawing regions. With our system, cartoonists can practise and get guidance by themselves. We have cooperated with several experts on developing...
A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering...
Recognition of objects from arbitrary viewpoints embedded document images is a new challenge in content-oriented document image analysis. In this paper, we propose a novel framework for detecting generic objects from arbitrary viewpoints described by varied object appearances. We first model the annotated objects from different viewpoints, and then build an explicit correspondence across multi-view...
A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented. The proposed framework essentially turns the complex anomaly detection process into two parts: motion pattern representation and spatio-temporal context modeling. We propose a new 4D spatio-temporal hypervolume representation by integrating the depth constraints to enrich...
We present an ensemble recognition method for graphic symbols that could be interfered by intersecting objects from the context. The symbol is first represented as a set of shape points, each of which is described by a shape context pyramid capturing the local shape characteristics of multi-scale regions surrounding the shape point. A Hough forest ensemble classifier is then employed to learn the...
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