Near-regular texture analysis and manipulation
A near-regular texture deviates geometrically and photometrically from a regular congruent tiling. Although near-regular textures are ubiquitous in the man-made and natural world, they present computational challenges for state of the art texture analysis and synthesis algorithms. Using regular tiling as our anchor point, and with user-assisted lattice extraction, we can explicitly model the deformation of a near-regular texture with respect to geometry, lighting and color. We treat a deformation field both as a function that acts on a texture and as a texture that is acted upon, and develop a multi-modal framework where each deformation field is subject to analysis, synthesis and manipulation. Using this formalization, we are able to construct simple parametric models to faithfully synthesize the appearance of a near-regular texture and purposefully control its regularity.

title: The Promise and Perils of Near-Regular Texture
abstract: Motivated by the low structural fidelity for near-regular textures in current texture synthesis algorithms, we propose and implement an alternative texture synthesis method for near-regular texture. We view such textures as statistical departures from regular patterns and argue that a thorough understanding of their structures in terms of their translation symmetries can enhance existing methods of texture synthesis. We demonstrate the perils of texture synthesis for near-regular texture and the promise of faithfully preserving the regularity as well as the randomness in a near-regular texture sample.

title: A Space-Time Delay Neural Network for Motion Recognition and its Application to Lipreading
abstract: Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.

title: A Lattice-Based MRF Model for Dynamic Near-Regular Texture Tracking
abstract: A near-regular texture (NRT) is a geometric and photometric deformation from its regular origin - a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Although NRTs are pervasive in man-made and natural environments, effective computational algorithms for NRTs are few. This paper addresses specific computational challenges in modeling and tracking dynamic NRTs, including ambiguous correspondences, occlusions, and drastic illumination and appearance variations. We propose a lattice-based Markov-random-field (MRF) model for dynamic NRTs in a 3D spatiotemporal space. Our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations. Based on the proposed MRF model, we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic NRT tracking without any assumption on the motion types or lighting conditions. We provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing

title: Some star extremal circulant graphs
abstract: The circular chromatic number @g"c(G) and the fractional chromatic number @g"f(G) are two generalizations of the ordinary chromatic number of a graph G. A graph is called star extremal if its circular chromatic number equals its fractional chromatic number. Gao and Zhu ( (1996) 147-156), Lih et al. ( (1999) 491-499) gave many classes of circulant graphs which are star extremal. In this paper, we study the star extremality of circulant graphs whose generating sets are of the form {1,2,...,m-1,k,k+1,...,k+m-2}, {k,k+1,...,k'}, and {k,k+1,...,k"1,k"2,k"2+1,...,@?p/[email protected]?}, where p is the vertex number of the graph. As a corollary, we give an improvement of a result of Gao and Zhu ( (1996) 147-156).

title: Reconfiguration Procedures for a Polymorphic and Partitionable Multiprocessor
abstract: This correspondence presents a collection of reconfiguration procedures for a multiprocessor which employs multistage interconnection networks. These procedures are used to dynamically partitipn the multiprocessor into many subsystems, and reconfigure them to form a variety commonly used topologies to match task graphs. By examining the switching capability of the interconnection network, design rules for avoiding connection conflicts are exploited. Then, on the basis of these rules, parallel procedures are designed. With the procedures, a subsystem can be reconfigured in the form of the desired topologies without interfering with other subsystems. In addition, the reconfiguration of a subsystem can be accomplished in constant time, independently of subsystem size.

title: Coloring of distance graphs with intervals as distance sets
abstract: Let D be a set of positive integers. The distance graph G(Z, D) with distance set D is the graph with vertex set Z in which two vertices x, y are adjacent if and only if |x - y| ∈ D. The fractional chromatic number, the chromatic number, and the circular chromatic number of G(Z, D) for various D have been extensively studied recently. In this paper, we investigate the fractional chromatic number, the chromatic number, and the circular chromatic number of the distance graphs with the distance sets of the form Dm, [k,k'] = {1, 2, ..., m} - {k, k + 1, ..., k'}, where m, k, and k' are natural numbers with m ≥ k' ≥ k. In particular, we completely determine the chromatic number of G(Z, Dm, [2,k']) for arbitrary m, and k'.

title: Modeling timing features in broadcast news video classification
abstract: Broadcast news programs are well-structured video, and timing can be a strong predictor for specific types of news reports. However, learning a classifier using timing features may not be an easy task when training data are noisy. We approach the problem from the generative model perspective, and approximate the class density in a non-parametric fashion. The results show that timing is a simple but extremely effective feature, and our method can achieve significantly better performance than a discriminative classifier.

title: Self-organizing fuzzy control of multi-variable systems using learning vector quantization network
abstract: Using learning vector quantization (LVQ) network to construct a self-organizing fuzzy controller (SOFC) for multi-variable nonlinear composite systems is developed in this paper. The LVQ network is used to provide information about the better locations of the IF-part membership functions through un-supervised learning. The generated fuzzy rule base is applied to the SOFC and updated by a self-learning procedure. Using Lyapunov stability methods, the proposed adaptive scheme is proven to provide the SOFC some degree of robust properties and guarantee uniform ultimate boundedness in the presence of disturbances, measurement noise and perturbed initialization error. The effectiveness of the proposed controller has been demonstrated numerically by applying to control a two-link manipulator.