Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given example. Using our algorithm, textures can be generated within seconds, and the synthesized results are always tileable. Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing data. A bounding and maximization process is given to specifically optimize conditional likelihood instead of the usual joint likelihood. Weapply the method to conditioned mixture models and use bounding techniques to derive the model's update rules. Monotonic convergence, computational efficiency and regression results superior to EM are demonstrated.

We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically uncovers correlations between past gestures from one human participant (an action) and a subsequent gesture(areaction) from another participant. A probabilistic model is trainedfrom data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The estimation uses general bounding and maximization to monotonically find the maximum conditional likelihood solution. The learning system drives a graphical interactive character which probabilistically predicts a likely response to a user's behaviour and performs it interactively. Thus, after analyzing human interaction in a pair of participants, the system is able to replace one of them and interact with a single remaining user. 1

We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observations of statistical regularities in such images, and parameterized by (1) the local autocorrelation of the coefficients in each subband; (2) both the local auto-correlation and cross-correlation of coefficient magnitudes at other orientations and spatial scales; and (3) the first few moments of the image pixel histogram. We develop an efficient algorithm for synthesizing random images subject to these constraints using alternated projections, and demonstrate its effectiveness on a wide range of synthetic and natural textures. In particular, we show that many important structural elements in textures (e.g., edges, repeated patterns or alternated patches of simpler texture), can be captured through joint second order statistics of the coefficient magnitudes. We also show the flexibility of the representation, by applying to a variety...

Abstract not found

We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this methodto analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically uncovers a mapping between gestures from one human participant (an action) and a subsequent gesture(areaction) from another participant. Aprobabilistic model is trained from data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The system drives a graphical interactive character which probabilistically predicts the most likely response to the user's behaviour and performs it interactively. Thus, after analyzing human interaction in a pair of participants, the system is able to replaceoneof them and interact with a single remaining user.

We present a statistical characterization of texture images in the context of an overcomplete complex wavelet transform. The characterization is based on empirical observations of statistical regularities in such images, and parameterized by (1) the local auto-correlation of the coefficients in each subband; (2) both the local auto-correlation and cross-correlation of coefficient magnitudes at other orientations and spatial scales; and (3) the first few moments of the image pixel histogram. We develop an efficient algorithm for synthesizing random images subject to these constraints using alternated projections, and demonstrate its effectiveness on a wide range of synthetic and natural textures. We also show the flexibility of the representation, by applying to a variety of tasks which can be viewed as constrained image synthesis problems. Vision is arguably our most important sensory system, judging from both the ubiquity of visual forms of communication, and the large proportion of ...

I propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. I apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically uncovers a mapping between past gestures from one human participant (an action) and a subsequent gesture (a reaction) from another participant. A probabilistic model is trained from data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The estimation uses general bounding and maximizationtofindthemaximum conditional likelihood solution. The learning system drives a graphical interactivecharacter which probabilistically predicts the most likely response to a user's behaviour and performs it interactively.Thus, after analyzing human interaction in a pair of participants, the system is able to replace one of them and interact with a single remaining user. Thesis Supervisor: Alex Pentland Title: Academic Head and Toshiba Professor of Media Arts and Sciences, MIT Media Lab This work was supported in part by British Telecom and Texas Instruments. Action-Reaction Learning: Analysis and Synthesis of Human Behaviour by Tony Jebara The following people served as readers for this thesis: Reader: Bruce M. Blumberg Asahi Broadcasting Corporation Career Development Assistant Professor of Media Arts and Sciences MIT Media Laboratory Reader: Aaron Bobick Assistant Professor of Computational Vision MIT Media Laboratory 4 Acknowledgments I extend warm thanks to my advisor, Professor Alex Pentland for having given me to opport...

. This paper describes a two-stage method of document image compression wherein a grayscale document image is rst processed to improve its compressibility, then losslessly compressed. The initial processing involves hierarchical, coarse-to-ne morphological operations designed to combat the noiselike variability of the low-order bits while attempting to preserve or even improve intelligibility. The result of this stage is losslessly compressed by an arithmetic coder that uses a mixture model to derive context-conditional graylevel probabilities. The lossless stage is compared experimentally with several reference methods, and is found to be competitive at all rates. The overall system is found to be comparable with JPEG in terms of mean-square error performance, but appears to outperform JPEG in terms of subjectively judged document image intelligibility. Key words: document image compression, image morphology, arithmetic coding, multiresolution, Gaussian mixtures [Appears in Mathem...