Wai-Yin Ng

Image-based Rendering with Controllable Illumination

A new image-based rendering method, based on the light field and Lumigraph system, allows illumination to be changed interactively. It does not try to recover or use any geometrical information (e.g., depth or surface normals) to calculate the illumination, but the resulting images are physically correct. The scene is first sampled from different viewpoints and under different illuminations. Treating each pixel on the back plane of the light slab as a surface element,the sampled images are used to find an apparent BRDF of each surface element. The tabular BRDF data of each pixel is further transformed to the spherical harmonic domain for efficient storage. Whenever the user changes the illumination setting, a certain number of views are reconstructed. The correct user perspective view is then displayed using the texture mapping technique of the Lumigraph system. Hence, the intensity, the type and the number of the light sources can be manipulated interactively.

A Geometry Dependent Texture Generation Framework for Simulating Surface Imperfections

To model surface imperfections and weathering, we propose a two-step texture generation framework in between manual texture synthesis and automatic physical simulation. Although the pattern of blemishes looks random, the systematic and geometry dependent nature of the underlying distribution is still observable. A distribution of tendency (potential to contain blemishes) is modeled in the first step, which includes user control and geometric information. The second generates and distributes an irregular blemish pattern according to the modeled tendency distribution. As examples we model three common surface imperfections; dust accumulation, patina and peeling.

Illumination of image-based objects

A new data representation of image-based objects is presented. With this representation, the user can change the illumination as well as the viewpoint of an image-based scene. Physically correct imagery can be generated without knowing any geometrical information (e.g. depth or surface normal) of the scene. By treating each pixel on the image plane as a surface element, we can measure its apparent BRDF (bidirectional reflectance distribution function) by collecting information in the sampled images. These BRDFs allow us to calculate the correct pixel colour under a new illumination set-up by fitting the intensity, direction and number of the light sources. We demonstrate that the proposed representation allows re-rendering of the scene illuminated by different types of light sources. Moreover, two compression schemes, spherical harmonics and discrete cosine transform, are proposed to compress the huge amount of tabular BRDF data.

Club Formation by Rational Sharing: Content, Viability and Community Structure

Abstract A sharing community prospers when participation and contribution are both high. We suggest the two, while being related decisions every peer makes, should be given separate rational bases. Considered as such, a basic issue is the viability of club formation, which necessitates the modelling of two major sources of heterogeneity, namely, peers and shared content. This viability perspective clearly explains why rational peers contribute (or free-ride when they don’t) and how their collective action determines viability as well as the size of the club formed. It also exposes another fundamental source of limitation to club formation apart from free-riding, in the community structure in terms of the relation between peers’ interest (demand) and sharing (supply).

Illuminating image-based objects

We present a new scheme of data representation for image-based objects. It allows the illumination to be changed interactively without knowing any geometrical information (e.g. depth or surface normal) of the scene, but the resulting images are physically correct. The scene is first sampled from different view points and under different illuminations. By treating each pixel on the image plane as a surface element, the sampled images are used to measure the apparent BRDF of each surface element. Two compression schemes, spherical harmonics and discrete cosine transform, are proposed to compress the tabular BRDF data. Whenever the user changes the illumination a certain number of views are reconstructed. The correct user perspective view is then displayed using the standard texture mapping hardware. Hence, the intensity, the type and the number of the light sources can be manipulated interactively.

TESS: an interactive support system for school timetabling

A timetabling package called TESS, which stands for Timetabling Expert Support System, has been developed for use in Hong Kong schools. It is representative of the decision support system (DSS), being highly interactive and supportive of different practices of expert timetablers. Its distinctive feature is a suite of seemingly simple tools. Being simple, they behave in manners transparent to the user, who is then ready to command them at will to solve his/her particular timetabling problem.

Perspectives on search-based computer-aided control system design

Search-based computer-aided control system design (CACDS), a computational approach that emphasizes the efficient generation and evaluation of a large number of candidate designs under an effective control of the designer, is discussed. The aim is to delineate the nature and strengths of the approach, which is first characterized by a conceptual model. Interactive multiobjective programming, an area in operational research which is a rich source of ideas for developing fitting software tools for the search approach, is described. A research agenda covering five areas where further innovation and development are needed to construct an integrated design environment for effective applications of search-based CACSD is proposed.<<ETX>>

Arbitrated matching: Formulation and protocol

Abstract This paper presents an arbitration approach to bipartite matching in which a set of actors are matched up with a set of mates in a one-to-one manner. The arbitration is composed of two stages, viz. stage I in which ordinal preference is elicited on a need-to basis until all stable matchings are obtained. Stage II is sequential bargaining in which risk preference information is extracted by eliciting certainty equivalents of default randomizations declared by the arbitrator. Stage II comes out with a particular stable subset, randomization of which is preferable for all, to that of the entire stable set. Stage II is particularly effective when actors and mates are risk-averse, whence reducing uncertainty with a smaller randomization set proves to be advantageous to all. We also prove that it is superior to the traditional one-off approach in terms of the elicitation effort required as well as strategyproofness.

Iterative Estimation Algorithms Using Conjugate Function Lower Bound and Minorization-Maximization with Applications in Image Denoising

A fundamental problem in signal processing is to estimate signal from noisy observations. This is usually formulated as an optimization problem. Optimizations based on variational lower bound and minorization-maximization have been widely used in machine learning research, signal processing, and statistics. In this paper, we study iterative algorithms based on the conjugate function lower bound (CFLB) and minorization-maximization (MM) for a class of objective functions. We propose a generalized version of these two algorithms and show that they are equivalent when the objective function is convex and differentiable. We then develop a CFLB/MM algorithm for solving the MAP estimation problems under a linear Gaussian observation model. We modify this algorithm for wavelet-domain image denoising. Experimental results show that using a single wavelet representation the performance of the proposed algorithms makes better than that of the bishrinkage algorithm which is arguably one of the best in recent publications. Using complex wavelet representations, the performance of the proposed algorithm is very competitive with that of the state-of-the-art algorithms.

A Minorization-Maximization Algorithm for Maximum a Posteriori Signal Estimation

We develop an iterative algorithm based on minorization-maximization optimization to determine the maximum a posteriori estimate of the signal. We focus on linear Gaussian signal model with a family of heavy-tailed prior distributions which can be represented as scale mixture of Gaussian. We then modify the proposed algorithm for wavelet domain image denoising. Experimental results show that using complex wavelet representations, the performance of the proposed algorithm is very competitive with that of the state-of-the-art algorithms