Peter M. Hall

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Eigenspace models are a convenient way to represent sets of observations with widespread applications, including classification. In this paper we describe a new constructive method for incrementally adding observations to an eigenspace model. Our contribution is to explicitly account for a change in origin as well as a change in the number of eigenvectors(More)
We present new deterministic methods that given two eigenspace models, each representing a set of n-dimensional observations will: (1) merge the models to yield a representation of the union of the sets; (2) split one model from another to represent the difference between the sets; as this is done, we accurately keep track of the mean. These methods are(More)
The contribution of this paper is a novel framework for synthesizing nonphotorealistic animations from real video sequences. We demonstrate that, through automated mid-level analysis of the video sequence as a spatiotemporal volume - a block of frames with time as the third dimension - we are able to generate animations in a wide variety of artistic styles,(More)
This paper provides algorithms for adding and subtracting eigenspaces, thus allowing for incremental updating and downdating of data models. Importantly, and unlike previous work, we keep an accurate track of the mean of the data, which allows our methods to be used in classification applications. The result of adding eigenspaces, each made from a set of(More)
The contribution of this paper is a novel nonphotorealistic rendering (NPR) technique, influenced by the style of Cubist art. Specifically we are motivated by artists such as Picasso and Braque, who produced art work by composing elements of a scene taken from multiple points of view; paradoxically such compositions convey a sense of motion without assuming(More)
The contribution of this paper is a novel non-photorealistic rendering (NPR) algorithm for rendering real images in an impasto painterly style. We argue that figurative artworks are salience maps, and develop a novel painting algorithm that uses a genetic algorithm (GA) to search the space of possible paintings for a given image, so approaching an “optimal”(More)
We present a probabilistic approach for the automatic production of tree models with convincing 3D appearance and motion. The only input is a video of a moving tree that provides us an initial dynamic tree model, which is used to generate new individual trees of the same type. Our approach combines global and local constraints to construct a dynamic 3D tree(More)
We propose a novel hierarchical model of human dynamics for view independent tracking of the human body in monocular video sequences. The model is trained using real data from a collection of people. Kinematics are encoded using Hierarchical Principal Component Analysis, and dynamics are encoded using Hidden Markov Models. The top of the hierarchy contains(More)
We introduce a video-based approach for producing water surface models. Recent advances in this field output high-quality results but require dedicated capturing devices and only work in limited conditions. In contrast, our method achieves a good tradeoff between the visual quality and the production cost: It automatically produces a visually plausible(More)