by Chan Ming-hong. / Bibliography: leaves 67-68 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1986

Thesis (M.Phil.)--Chinese University of Hong Kong. / Bibliography: leaves 122-126.

圖像相似性度量準則是一個傳統的研究領域。大量經典的圖像處理技術被用來為各種類型的圖像設計相似性度量準則，這些圖像包括了線條圖，灰度圖，彩圖以及高動態範圍圖像。儘管已有的度量準則在指定的條件下可以實現優良的圖像相似度比較，這些度量準則極少系統地考慮或檢驗自身與人類視覺感知之間的一致性。而與人類知覺的一致性是由大量實際應用提出的共同需求。隨著三維立體設備的廣泛應用，圖像的相似性已經不只是傳統的可視差別，更包括了人眼利用三維立體設備同時觀看兩張不同的圖片時的視覺可接受度。 / 非嚴謹對準形狀相似性度量準則（AISS）可以比較兩幅具有固定尺寸的線條圖的形狀相似度。對於該度量準則，兩幅待比較圖像的形狀不要求完全對齊，同時，又會考慮到圖像的形變，例如位置，方向和縮放上的變化。 / 雙目觀看舒適度預測器（BVCP）是另一個度量準則。當人的雙眼同時觀看兩幅不同的圖像時，該準則可用以預測視覺的舒適度。根據著名的双眼單视理論，人的視覺可以將兩幅具有細節、對比度以及亮度差別的圖像合成一幅圖像，只要這些差別在限定的程度之內。在計算機圖形學領域，BVCP 首次嘗試去預測雙目的圖像差別會否引起觀看的不舒適。 / 在本論文中，實用的應用程序也被提出用以衡量AISS 和BVCP。AISS 被用在了一個名為“基於結構的ASCII 藝術的應用程序中，該應用程序可以利用ASCII 字符的形狀近似地表現參考圖像的線條結構信息。而BVCP 則被用在一個創新地應用框架中，該框架可以從單幅高動態範圍圖像中生成一組（兩幅）低動態範圍圖像。當這一組低動態範圍圖像組被人的雙眼同時觀看時，可以比傳統的單幅低動態範圍圖像保留更多的人類可感知視覺信息。可信的結果和使用者研究也用來證明SSIM 和BVCP 的有效性以及與人類知覺的一致性。 / Image similarity metric is a traditional research field. Classical image processing techniques are used to design similarity metrics for all kinds of images, such as line drawings, gray or color image and even high-dynamic range (HDR) images. While existing metrics perform well for the tasks of comparing images in specified situations, few of them have systematically considered or examined the consistency with human perception required by practical applications. With the blooming of stereo devices, the similarity to be measured is not only the traditional visual difference between two images, but also the visual acceptance of two images when they are viewed simultaneously with 3D devices. This thesis presents two image similarity metrics motivated by perceptual principles, also with applications to demonstrate their novelty and practical values. / Alignment-Insensitive Shape Similarity Metric (AISS) measures shape similarity of line drawings. This metric can tolerate misalignment between two shapes and, simultaneously, accounts for the differences in transformation such as, position, orientation and scaling. / Binocular Viewing Comfort Predictor (BVCP) is another metric proposed to measure visual discomfort when human's two eyes view two different images simultaneously. According to a human vision phenomenon - binocular single vision, human vision is able tofuse two images with differences in detail, contrast and luminance, up to a certain limit. BVCP makes a first attempt in computer graphics to predict such visual comfort limit. / Applications are also proposed to evaluate AISS and BVCP. AISS is utilized in an application of Structure-based ASCII Art, which approximates line structure of the reference image content with the shapes of ASCII characters. BVCP is utilized in a novel framework - Binocular Tone Mapping which generates a binocular low-dynamic range (LDR) image pair from one HDR image. Such binocular LDR pair can be viewed with stereo devices and can preserve more human-perceivable visual content than traditional one single LDR image. Convincing results and user studies are also shown to demonstrate that both AISS and BVCP are consistent with human perception and effective in practical usage. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhang, Linling. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 122-132). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Alignment-Insensitive Shape Similarity Metric --- p.8 / Chapter 2.1 --- Related Work --- p.10 / Chapter 2.2 --- Design of AISS --- p.13 / Chapter 2.2.1 --- Misalignment Tolerance --- p.14 / Chapter 2.2.2 --- Transformation Awareness --- p.16 / Chapter 2.2.3 --- Parameter Setting --- p.17 / Chapter 2.3 --- Results and Discussion --- p.18 / Chapter 2.4 --- Discussion --- p.20 / Chapter 3 --- Application for AISS: Structure-based ASCII Art --- p.21 / Chapter 3.1 --- Overview --- p.24 / Chapter 3.2 --- Optimization --- p.28 / Chapter 3.3 --- User Study and Discussion --- p.35 / Chapter 3.3.1 --- Metrics Comparison --- p.35 / Chapter 3.3.2 --- Comparison to Existing Work --- p.38 / Chapter 3.3.3 --- User Study --- p.40 / Chapter 3.4 --- Summary --- p.44 / Chapter 4 --- Binocular Viewing Comfort Predictor --- p.48 / Chapter 4.1 --- Background --- p.51 / Chapter 4.2 --- Design of BVCP --- p.54 / Chapter 4.2.1 --- Fusional Area --- p.55 / Chapter 4.2.2 --- Contour Fusion --- p.58 / Chapter 4.2.3 --- Contour and Regional Contrasts --- p.68 / Chapter 4.2.4 --- Failure of Rivalry --- p.70 / Chapter 4.2.5 --- The Overall Fusion Predictor --- p.74 / Chapter 4.3 --- User Study --- p.77 / Chapter 4.4 --- Discussion and Limitations --- p.84 / Chapter 5 --- Application for BVCP: Binocular Tone Mapping --- p.86 / Chapter 5.1 --- Framework --- p.90 / Chapter 5.1.1 --- Overview --- p.90 / Chapter 5.1.2 --- Optimization --- p.93 / Chapter 5.2 --- Results and Discussion --- p.96 / Chapter 5.2.1 --- Results --- p.96 / Chapter 5.2.2 --- User Study --- p.103 / Chapter 5.2.3 --- Incorporating Stereopsis --- p.106 / Chapter 5.2.4 --- Limitations --- p.109 / Chapter 5.3 --- Summary --- p.112 / Chapter 6 --- Conclusion --- p.113 / Chapter A --- User Study for ASCII art --- p.117 / Bibliography --- p.122

by Chan Pak Kei, Bernard. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 125-127). / Chapter 1 --- Introduction --- p.10 / Chapter 1.1 --- Overview --- p.10 / Chapter 1.2 --- Classification Approaches --- p.11 / Chapter 1.3 --- The Use of Neural Network --- p.12 / Chapter 1.4 --- Motivations --- p.14 / Chapter 1.5 --- Organization of Thesis --- p.16 / Chapter 2 --- Related Work --- p.19 / Chapter 2.1 --- Overview --- p.19 / Chapter 2.2 --- Neural Network --- p.20 / Chapter 2.2.1 --- Backpropagation Feedforward Neural Network --- p.20 / Chapter 2.2.2 --- Training of a Backpropagation Feedforward Neural Network --- p.22 / Chapter 2.2.3 --- Single Hidden-layer Model --- p.27 / Chapter 2.2.4 --- Data Preprocessing --- p.27 / Chapter 2.3 --- Fuzzy Sets --- p.29 / Chapter 2.3.1 --- Fuzzy Linear Regression Analysis --- p.29 / Chapter 2.4 --- Network Architecture Altering Algorithms --- p.31 / Chapter 2.4.1 --- Pruning Algorithms --- p.32 / Chapter 2.4.2 --- Constructive/Growing Algorithms --- p.35 / Chapter 2.5 --- Summary --- p.38 / Chapter 3 --- Hybrid Classification Systems --- p.39 / Chapter 3.1 --- Overview --- p.39 / Chapter 3.2 --- Literature Review --- p.41 / Chapter 3.2.1 --- Fuzzy Linear Regression(FLR) with Fuzzy Interval Analysis --- p.41 / Chapter 3.3 --- Data Sample and Methodology --- p.44 / Chapter 3.4 --- Hybrid Model --- p.46 / Chapter 3.4.1 --- Construction of Model --- p.46 / Chapter 3.5 --- Experimental Results --- p.50 / Chapter 3.5.1 --- Experimental Results on Breast Cancer Database --- p.50 / Chapter 3.5.2 --- Experimental Results on Synthetic Data --- p.53 / Chapter 3.6 --- Conclusion --- p.55 / Chapter 4 --- Searching for Suitable Network Size Automatically --- p.59 / Chapter 4.1 --- Overview --- p.59 / Chapter 4.2 --- Literature Review --- p.61 / Chapter 4.2.1 --- Pruning Algorithm --- p.61 / Chapter 4.2.2 --- Constructive Algorithms (Growing) --- p.66 / Chapter 4.2.3 --- Integration of methods --- p.67 / Chapter 4.3 --- Methodology and Approaches --- p.68 / Chapter 4.3.1 --- Growing --- p.68 / Chapter 4.3.2 --- Combinations of Growing and Pruning --- p.69 / Chapter 4.4 --- Experimental Results --- p.75 / Chapter 4.4.1 --- Breast-Cancer Cytology Database --- p.76 / Chapter 4.4.2 --- Tic-Tac-Toe Database --- p.82 / Chapter 4.5 --- Conclusion --- p.89 / Chapter 5 --- Conclusion --- p.91 / Chapter 5.1 --- Recall of Thesis Objectives --- p.91 / Chapter 5.2 --- Summary of Achievements --- p.92 / Chapter 5.2.1 --- Data Preprocessing --- p.92 / Chapter 5.2.2 --- Network Size --- p.93 / Chapter 5.3 --- Future Works --- p.94 / Chapter A --- Experimental Results of Ch3 --- p.95 / Chapter B --- Experimental Results of Ch4 --- p.112 / Bibliography --- p.125

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No description available.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

The primary objective of this thesis is to develop a methodology for clustering of objects based on their functionality typified by the notion of concept. We begin by giving a formal definition of concept. By assigning a functional interpretation to the underlying concept, we demonstrate the applicability of the functionally interpreted concept for clustering objects. This functional interpretation leads us to identifying two classes of concepts, namely, the Necessary class and the Quality-Improvement class. Next, we categorize the functional cohesiveness among objects into three different classes. Further, we axiomatize the restrictions imposed, on the execution of functions of objects, by the non-availability of sufficient resources. To facilitate describing functional clusters in a succinct manner, we define connectives that capture the imposed restrictions. Also we justify the adequacy of these connectives for describing functional clusters. We then propose a suitable data structure to represent the functionally interpreted concept, and develop an algorithm to perform this axiomatic functional partitioning of objects. We illustrate the functional partitioning of objects through a real-world example. We formally establish the invariance of the resulting cluster descriptions, with respect to the order in which the given set of objects is examined. This invariance would facilitate parallel implementations of the proposed methodology. We then analyze different functional cluster configurations from a structural viewpoint. In doing so, we identify the presence of a specific property among certain cluster configurations. We also state a sufficient condition for the presence of this property in any cluster. A separate class of concepts, namely the Concept Transformer class, displaying certain properties, is identified and studied in detail. We also demonstrate its applicability to functional clustering. Finally, we examine a knowledge-based pattern synthesis problem from a functional angle as a significant application of the functional interpretation of concept and associated data structures. Here, we show that a concept, from the functional view-point, can be viewed as the synthesis of various other concepts; the synthesis is an outcome of a knowledge-based goal-directed pattern-matching activity. The proposed methodology has the potential to cluster objects that imply functions by virtue of their physical properties.

Thesis (M.S.) -- New Jersey Institute of Technology, Dept. of Computer and Information Science, 2000. / Includes bibliographical references. Also available via the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.