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The research discussed in this thesis is concerned with surface understanding from the viewpoint of recognition-oriented, scale-related processing based on surface curvatures and diffusion smoothing. Four problems below high level visual processing are investigated: 1) 3-dimensional data smoothing using a diffusion process; 2) Behaviour of shape features across multiple scales, 3) Surface segmentation over multiple scales; and 4) Symbolic description of surface features at multiple scales. In this thesis, the noisy data smoothing problem is treated mathematically as a boundary value problem of the diffusion equation instead of the well-known Gaussian convolution, In such a way, it provides a theoretical basis to uniformly interpret the interrelationships amongst diffusion smoothing, Gaussian smoothing, repeated averaging and spline smoothing. It also leads to solving the problem with a numerical scheme of unconditional stability, which efficiently reduces the computational complexity and preserves the signs of curvatures along the surface boundaries. Surface shapes are classified into eight types using the combinations of the signs of the Gaussian curvature K and mean curvature H, both of which change at different scale levels. Behaviour of surface shape features over multiple scale levels is discussed in terms of the stability of large shape features, the creation, remaining and fading of small shape features, the interaction between large and small features and the structure of behaviour of the nested shape features in the KH sign image. It provides a guidance for tracking the movement of shape features from fine to large scales and for setting up a surface shape description accordingly. A smoothed surface is partitioned into a set of regions based on curvature sign homogeneity. Surface segmentation is posed as a problem of approximating a surface up to the degree of Gaussian and mean curvature signs using the depth data alone How to obtain feasible solutions of this under-determined problem is discussed, which includes the surface curvature sign preservation, the reason that a sculptured surface can be segmented with the KH sign image alone and the selection of basis functions of surface fitting for obtaining the KH sign image or for region growing. A symbolic description of the segmented surface is set up at each scale level. It is composed of a dual graph and a geometrical property list for the segmented surface. The graph describes the adjacency and connectivity among different patches as the topological-invariant properties that allow some object's flexibility, whilst the geometrical property list is added to the graph as constraints that reduce uncertainty. With this organisation, a tower-like surface representation is obtained by tracking the movement of significant features of the segmented surface through different scale levels, from which a stable description can be extracted for inexact matching during object recognition.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

by Or Siu Hang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1991. / Bibliography: leaves [102]-[105]. / Acknowledgements --- p.V / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- The problem --- p.1 / Chapter 1.1.1 --- The correspondence problem --- p.5 / Chapter 1.1.2 --- The problem of surface reconstruction --- p.6 / Chapter 1.2 --- Our goal --- p.8 / Chapter 1.3 --- Previous works --- p.8 / Chapter 1.3.1 --- Constraints on matching --- p.10 / Chapter 1.3.2 --- Interpolation of disparity surfaces --- p.12 / Chapter Chapter 2 --- Preprocessing of images / Chapter 2.1 --- Which operator to use --- p.14 / Chapter 2.2 --- Directional zero-crossing --- p.14 / Chapter 2.3 --- Laplacian of Gaussian --- p.16 / Chapter 2.3.1 --- Theoretical background of the Laplacian of Gaussian --- p.18 / Chapter 2.3.2 --- Implementation of the operator --- p.21 / Chapter Chapter 3 --- Disparity Layers Generation / Chapter 3.1 --- Geometrical constraint --- p.23 / Chapter 3.2 --- Basic idea of disparity layer --- p.26 / Chapter 3.3 --- Consideration in matching --- p.28 / Chapter 3.4 --- effect of vertical misalignment of sensor --- p.37 / Chapter 3.5 --- Final approach --- p.39 / Chapter Chapter 4 --- Disparity combination / Chapter 4.1 --- Ambiguous match from different layers --- p.52 / Chapter 4.2 --- Our approach --- p.54 / Chapter Chapter 5 --- Generation of dense disparity map / Chapter 5.1 --- Introduction --- p.58 / Chapter 5.2 --- Cooperative computation --- p.58 / Chapter 5.2.1 --- Formulation of oscillation algorithm --- p.59 / Chapter 5.3 --- Interpolation by Gradient descent method --- p.69 / Chapter 5.3.1 --- Formulation of constraints --- p.70 / Chapter 5.3.2 --- Gradient projection interpolation algorithm --- p.72 / Chapter 5.3.3 --- Implementation of the algorithm --- p.78 / Chapter Chapter 6 --- Conclusion --- p.89 / Reference / Appendix (Dynamical behavior of the cooperative algorithm)

The overall argument this thesis makes is that topological object structures captured within hierarchical image descriptions are invariant to depictive styles and offer a level of abstraction found in many modern abstract artworks. To show how object structures can be extracted from images, two hierarchical image descriptions are proposed. The first of these is inspired by perceptual organisation; whereas, the second is based on agglomerative clustering of image primitives. This thesis argues the benefits and drawbacks of each image description and empirically show why the second is more suitable in capturing object strucutures. The value of graph theory is demonstrated in extracting object structures, especially from the second type of image description. User interaction during the structure extraction process is also made possible via an image hierarchy editor. Two applications of object structures are studied in depth. On the computer vision side, the problem of object classification is investigated. In particular, this thesis shows that it is possible to classify objects regardless of their depictive styles. This classification problem is approached using a graph theoretic paradigm; by encoding object structures as feature vectors of fixed lengths, object classification can then be treated as a clustering problem in structural feature space and that actual clustering can be done using conventional machine learning techniques. The benefits of object structures in computer graphics are demonstrated from a Non-Photorealistic Rendering (NPR) point of view. In particular, it is shown that topological object structures deliver an appropriate degree of abstraction that often appears in well-known abstract artworks. Moreover, the value of shape simplification is demonstrated in the process of making abstract art. By integrating object structures and simple geometric shapes, it is shown that artworks produced in child-like paintings and from artists such as Wassily Kandinsky, Joan Miro and Henri Matisse can be synthesised and by doing so, the current gamut of NPR styles is extended. The whole process of making abstract art is built into a single piece of software with intuitive GUI.

Driving Assistance Systems such as lane departure and front collision warning has caught great attention for its promising usage on road driving. This, this research focus on implementing lane departure and front collision warning at same time. In order to make the system really useful for real situation, it is critical that the whole process could be near real-time. Thus we chose Hough Transform as the main algorithm for detecting lane on the road. Hough Transform is used for that it is a very fast and robust algorithm, which makes it possible to execute as many frames as possible per frames. Hough Transform is used to get boundary information, so that we could decide if the car is doing lane departure based on the car's position in lane. Later, we move on to use front car's symmetry character to do front car detection, and combine it with Camshift tracking algorithm to fill the gap for failure of detection. Later we introduce camera calibration, stereo calibration, and how to calculate real distance from depth map.

EBI and iEBI mechanism have all the desirable properties of a good interpolation: all given input-output examples are satisfied exactly, and the interpolation is smooth with minimum oscillations between the examples. / Example-Based Interpolation (EBI) is a powerful method to interpolate function from a set of input-output examples. The first part of the dissertation exams the EBI in detail and proposes a new enhanced EBI, indexed function Example-Based Interpolation (iEBI). The second part demonstrates the application of both EBI and iEBI to solve three well-defined problems of computer vision. / First, the dissertation has analyzed EBI solution in detail. It argues and demonstrates that there are three desired properties for any EBI solution. To satisfy all three desirable properties, the EBI solution must have adequate degrees of freedom. This dissertation shows in details that, for the EBI solution to have enough degrees of freedom, it needs only be in a simple format: the sum of a basis function plus a linear function. This dissertation also presents that a particular EBI solution, in a certain least-squares-error sense, could satisfy exactly all the three desirable properties. / Moreover, this dissertation also points out EBI's restriction and describes a new interpolation mechanism that could overcome EBI's restriction by constructing general indexed function from examples. The new mechanism, referred to as the general indexed function Example-Based Interpolation (iEBI) mechanism, first applies EBI to establish the initial correspondences over all input examples, and then interpolates the general indexed function from those initial correspondences. / Novel View Synthesis (NVS) is an important problem in image rendering. It tries to synthesize an image of a scene at any specified (novel) viewpoint using only a few images of that scene at some sample viewpoints. To avoid explicit 3-D reconstruction of the scene, this dissertation formulates the problem of NVS as an indexed function interpolation problem by treating viewpoint and image as the input and output of a function. The interpolation formulation has at least two advantages. First, it allows certain imaging details like camera intrinsic parameters to be unknown. Second, the viewpoint specification need not be physical. For example, the specification could consist of any set of values that adequately describe the viewpoint space and need not be measured in metric units. This dissertation solves the NVS problem using the iEBI formulation and presents how the iEBI mechanism could be used to synthesize images at novel viewpoints and acquire quality novel views even from only a few example views. / Stereo matching, or the determination of corresponding image points projected by the same 3-D feature, is one of the fundamental and long-studied problems in computer vision. Yet, few have tried to solve it using interpolation. This dissertation presents an interpolation approach, Interpolation-based Iterative Stereo Matching (IISM), that could construct dense correspondences in stereo image from sparse initial correspondences. IISM improves the existing EBI to ensure that the established correspondences satisfy exactly the epipolar constraint of the image pair, and to a certain extent, preserve discontinuities in the stereo disparity space of the imaged scene. IISM utilizes the refinement technique of coarse-to-fine to iteratively apply the improved EBI algorithm, and eventually, produces the dense disparity map for stereo image pair. / The second part of the dissertation focuses on applying the EBI and iEBI methods to solve three correspondence-based problems in computer vision: (1) stereo matching, (2) novel view synthesis, and (3) viewpoint determination. / This dissertation also illustrates, for all the three problems, experimental results on a number of real and benchmarking image datasets, and shows that interpolation-based methods could be effective in arriving at good solution even with sparse input examples. / Viewpoint determination of image is the problem of, given an image, determining the viewpoint from which the image was taken. This dissertation demonstrates to solve this problem without referencing to or estimating any explicit 3-D structure of the imaged scene. Used for reference are a small number of sample snapshots of the scene, each of which has the associated viewpoint. By treating image and its associated viewpoint as the input and output of a function, and the given snapshot-viewpoint pairs as examples of that function, the problem has a natural formulation of interpolation. Same as that in NVS, the interpolation formulation allows the given images to be uncalibrated and the viewpoint specification to be not necessarily measured. This dissertation presents an interpolation-based solution using iEBI mechanism that guarantees all given sample data are satisfied exactly with the least complexity in the interpolated function. / Liang Bodong. / "February 2006." / Adviser: Ronald Chi-kit Chung. / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6516. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 127-145). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.