Motion Effects Synthesis for 4D Films.

4D film is an immersive entertainment system that presents various physical effects with a film in order to enhance viewers' experiences. Despite the recent emergence of 4D theaters, production of 4D effects relies on manual authoring. In this paper, we present algorithms that synthesize three classes of motion effects from the audiovisual content of a film.

Joint Image Clustering and Labeling by Matrix Factorization.

We propose a novel algorithm to cluster and annotate a set of input images jointly, where the images are clustered into several discriminative groups and each group is identified with representative labels automatically. For these purposes, each input image is first represented by a distribution of candidate labels based on its similarity to images in a labeled reference image database. A set of these label-based representations are then refined collectively through a non-negative matrix factorization with sparsity and orthogonality constraints; the refined representations are employed to cluster and annotate the input images jointly.

On-Line Video Event Detection by Constraint Flow.

We present a novel approach in describing and detecting the composite video events based on scenarios, which constrain the configurations of target events by temporal-logical structures of primitive events. We propose a new scenario description method to represent composite events more fluently and efficiently, and discuss an on-line event detection algorithm based on a combinatorial optimization. For this purpose, constraint flow-a dynamic configuration of scenario constraints-is first generated automatically by our scenario parsing algorithm.

FaceReview: supporting interactive exploration of linked heterogeneous datasets for unilateral cleft lip and palate.

Children with unilateral cleft lip and palate (UCLP) suffer from negative public perceptions. A better treatment strategy should be established to help them live an ordinary life with improved perceptions. To do that, it is important to understand the relationship between physical facial features and perceptual judgment.

Density-based multifeature background subtraction with support vector machine.

Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification.

Visual tracking by continuous density propagation in sequential bayesian filtering framework.

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation.

Sequential kernel density approximation and its application to real-time visual tracking.

Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time.