http://dspace.cityu.edu.hk:80/handle/2031/783 2013-05-01T03:06:36Z http://dspace.cityu.edu.hk:80/handle/2031/6656 Title: Analysis of Feng Zikai's Chinese cartoons and their computer reproduction Authors: Zhang, Jing ( 張晶) Abstract: Feng Zikai, is an outstanding modern Chinese cartoonist, as well as an accomplished essayist, scholar, educator, translator, calligrapher, and musician. He is chiefly known for developing a unique style of Chinese cartoons incorporating a combination of traditional Chinese fine arts features and references to Western painting styles. The trajectory of Feng's career reflects a characteristic of modern Chinese intellectuals, that of, placing value on developing deep knowledge in multiple disciplines simultaneously. Mostly due to the difficulties with gaining access to primary resources, publications to date, especially journal articles, draw on a narrow range of works and somewhat haphazardly extend the discussion of Feng's creations and ideas. Based on a more extensive access to Feng's Chinese cartoons and essays, as well as Sino-Western publications about his life and work, this research provides a graphics-oriented analysis of Feng's art works, focusing on the foundations, grammatical theories, and themes of his Chinese cartoons. Moreover, programs and techniques for simulating the styles of Chinese brush work in contemporary computer graphics and animation are quite successful in a generic sense, but they are not very successful in reproducing a specific artist's painting style in an interactive digital painting process. This research therefore also aims at developing a preliminary computer framework capable of reproducing the specific artistic styles of selected Chinese paintings, focusing on a case study of a subset of the works of Feng Zikai, with specific reference to his signature painting of human facial features. Aside from the potential technical benefits of such a study, a comprehensive understanding of Feng's graphic style will also be relevant to discussions of the relationship between tradition and novelty in the world of Asian art and aesthetics. Notes: CityU Call Number: NC1699.F4 Z45 2011; xi, 431 leaves : ill. (some col.), facsims. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 384-397) 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/5695 Title: Acoulogy as a framework for environmental sound Authors: Maridet, Cedric Marie Yves Abstract: Electronic technologies enable the composer to select, repeat, fragment and otherwise manipulate and study sound. These possibilities have been a strong impetus in the development of innovative theories of sound alongside radical compositional practices using recorded environmental sounds. However, a holistic model that clarifies and maps the stakes of the various practices is lacking. Thus, I propose to elucidate the concept of listening intentions and to place it at the core of sonic art theory. In that sense, a model of acoulogy, as the study of listening, is a valuable framework to explain the heterogeneity of listening modes and the connections between composing and listening. This justifies a phenomenological approach, which focuses on a first-person description of the different ways to experience sounds. Several domains are thus identified concerning the potentials for abstraction contained in recorded sounds, like the relationship of the sound to its source, the paradigms of contextualized versus decontextualized sounds, found versus processed sound, and the relationship to reality. These concepts provide the basic vocabulary to describe and analyze different forms of sonic art. Notes: CityU Call Number: ML3805 .M37 2009; 1 v. (unpaged) 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2009.; Includes bibliographical references. 2009-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/5689 Title: Bounded-parameter partially observable Markov decision processes Authors: Ni, Yaodong (倪耀東) Abstract: In many online applications, it is critical to design an intelligent agent that is able to make sequential decisions in a complex environment with uncertainty. The partially observable Markov decision process (POMDP) is considered as a fundamental model for sequential decision making under uncertainty. Not only can the POMDP model describe the uncertainty in the action effects of the agent, but also describe the uncertainty in the agent’s observations. However, the lack of the knowledge for constructing the precise underlying model and the high computational complexity for obtaining optimality have made it difficult to apply POMDPs to real-world problems. In this dissertation, I focus on addressing the former, while the latter is also considered. In reality, it is usually an unattainable objective to precisely describe an environment by an exact POMDP model, due to various reasons such as inadequate data for model extraction, imprecision brought by consulting human experts and time-varying environment that cannot be described by a static POMDP model. However, it is possible to estimate the bounds on the parameters in the model based on the available information. In this dissertation, I study the POMDP problems with only information about the bounds of the model parameters. I propose the model of the bounded-parameter partially observable Markov decision process (BPOMDP), which is a generalization of the classic, exact POMDP model. In order to solve a BPOMDP, two key problems brought by parameter imprecision need to be addressed: (1) how to formulate the optimality criterion and (2) how to represent a policy. In this dissertation, I present two different solution performance criteria, namely themultimodel admissibility and the optimistic optimality. Themulti-model admissibility criterion refers to finding an admissible policy given that each policy is evaluated with multiple POMDP models. Under the optimistic optimality criterion, a policy is referred to as optimal if its highest possible total reward is themaximumamong all policies. In addition, I will discuss two representations of policies, each leading to an approach to solving BPOMDPs. The first policy representation, which is based on the concept of the policy tree, provides explicit policies for the finite-horizon problems and approximate solutions for infinite-horizon problems. I present a modified value iteration as the basic strategy for generating a multi-model admissible solution, and further propose a UL-based value iteration (ULVI) algorithm that is convenient to execute and helps reduce computational costs. Different settings of subroutines in the ULVI algorithm can lead to different performances. Theoretically, it is proven that the reward loss of the solution generated by the ULVI algorithm originates only from the imprecision in parameters. I have experiments to show that imprecision in the parameters affects both the computational costs and the reward loss and, at the expense of solution performance, the ULVI algorithm can be taken as an approximate method for exact POMDPs for saving computation. The policy representation called the finite-state controller (FSC) is particularly suitable for infinite-horizon problems, which occupies only finite memory and allows explicit policy-executing. Based on the theoretical results, I propose a value iteration method for evaluating FSCs. By representing a policy as an FSC, I develop the policy iteration algorithm for BPOMDPs. It is shown that the policy iteration algorithm converges to an ǫ-optimal policy under the optimistic optimality criterion in finite iterations. The BPOMDP model is a generalization of the traditional POMDP model in the sense that any exact POMDP model can be represented as a BPOMDP model, thus the approaches proposed in this dissertation are applicable for standard POMDP problems. As a more robust model, the BPOMDP model is expected to find a larger variety of applications than the POMDP model. In this view, the work presented in this dissertation is of general significance for sequential decision making under uncertainty. Notes: CityU Call Number: QA274.7 .N5 2009; xv, 118 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2009.; Includes bibliographical references (leaves 94-104) 2009-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/5267 Title: Bayesian graph-theoretic approaches to image structure modeling and matching Authors: Feng, Wei (馮偉) Abstract: Image structure modeling and matching remain two active topics in computer vision for decades, and compose the foundation for many content-based applications in media computing. Currently, most research on these two problems is usually conducted independently with different methods. In this dissertation, we generalize image structure modeling and matching with a unified paradigm, namely the Bayesian graph-theoretic approach. We present a series of novel structural modeling and matching algorithms arising from the Bayesian graph formulation. This dissertation mainly addresses the following three problems: 1) How to extract a reliable structural labeling using Bayesian decision theory, Markov random fields (MRFs) and graph formulation? 2) How to construct a compact and effective structural model to encode both global topology and salient local features? 3) How to efficiently match two structural models under moderate occlusion and clutter? Our work on the first problem focuses on integrating graph formulation and graphtheoretic methods with MRFs and Bayesian model. The MRFs and Bayesian decision theory provide a powerful tool for statistical image analysis and structural pattern modeling. However, self-validation always remains an open problem for traditional methods. Besides, the computational complexity of maximum a posteriori probability (MAP) estimation is another critical weakness, leading to a large sacrifice of accuracy for speed. Using Bayesian graph formulation and graph-theoretic methods, we proposed a general paradigm, namely graduated graph mincuts (GGC) that is self-validated and significantly reduce the computational complexity compared to flat MRF/MAP methods. Within the GGC framework, we propose three concrete algorithms: tree-structured graph cuts (TSGC), net-structured graph cuts (NSGC) and hierarchical graph cuts (HGC). We also generalize the discriminative spectral clustering to Bayesian spectral clustering by combining with generative models. We address the second problem based on the proposed structural labeling methods. A reliable structural model called Bayesian structural content abstraction (BaSCA) is proposed, which is an attributed graph representation of image structural content. The BaSCA model is robust to non-content changing operations (NCOs) and sensitive to content-changing operations (COs). In addition, to support dynamic NCO/CO partition, we further optimize the BaSCA structure model in the user-defined NCO space with an analogy mean shift algorithm, namely identical structure extension. Besides the robustness, another nice property of the BaSCA model is that it can be extended naturally by integrating salient local features. The BaSCA model is applied to region-level image authentication. Experiments show that the BaSCA signature significantly improves the false positive rate and has comparable false negative rates with previous methods. We study the third problem, structural matching, with spectral graph theory. We show how to handle the occlusion/clutter problem in the normalized eigenspace using a spectral approach, which is considered difficult for classical spectral methods. We also discuss the spectral multiplicity problem in graph matching, and propose a multiplicity-tolerant algorithm for structural matching. Finally, the proposed algorithms are applied to solving some real-world problems in computer vision and media computing, such as media content authentication, automatic/ interactive segmentation, shape matching and retrieval etc. Some related problems, e.g., multiple grouping cues extraction and shape structure abstraction, are also discussed. The algorithms developed in this dissertation are not limited to two-dimensional Cartesian spaces or any metric structure. Therefore, the Bayesian graph-theoretic approach may readily find a large variety of applications in computer vision, pattern recognition and media computing. Notes: xvi, 195 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2008.; Includes bibliographical references (leaves 179-195); CityU Call Number: TA1637 .F46 2008 2008-01-01T00:00:00Z