published_or_final_version / Computer Science / Master / Master of Philosophy

The present work is based on the Visual Routine theory of Shimon Ullman. This theory holds that efficient visual perception is managed by first applying spatially parallel methods to an initial input image in order to construct the basic representation-maps of features within the image. Then, this phase is followed by the application of serial methods - visual routines - which are applied to the most salient items in these and other subsequently created maps. Recent work in the visual routine tradition is reviewed, as well as relevant psychological work on preattentive and attentive vision. An analysis is made of the problem of devising a visual routine language for computing geometric properties and relations. The most useful basic representations to compute directly from a world of 2-D geometric shapes are determined. An argument is made for the case that an experimental program is required to establish which basic operations and which methods for controlling them will lead to the efficient computation of geometric properties and relations. A description is given of an implemented computer system which can correctly compute, in images of simple 2-D geometric shapes, the properties vertical, horizontal, closed, and convex, and the relations inside, outside, touching, centred-in, connected, parallel, and being-part-of. The visual routines which compute these, the basic operations out of which the visual routines are composed, and the important logic which controls the goal-directed application of the routines to the image are all described in detail. The entire system is embedded in a Question-and-Answer system which is capable of answering questions of an image, such as "Find all the squares inside triangles" or "Find all the vertical bars outside of closed convex shapes." By asking many such questions about various test images, the effectiveness of the visual routines and their controlling logic is demonstrated. / Science, Faculty of / Computer Science, Department of / Graduate

基於審美的圖像質量自動評估近年來引起了計算機視覺領域的普遍關注。在這篇論文裡, 我們提出使用局部與整體特徵, 基於圖像內容進行圖片質量評估。首先, 圖像的主題區域被提取出來。這部分區域最吸引觀看者的注意力。基於主題區域, 我們提取局部特徵, 並結合整體特徵進行圖像質量評估。攝影專家拍攝圖片時, 對於不同內容的圖片, 會採取不同的技術手段和審美衡量標準。基於此項觀察, 我們提出根據圖片的內容, 在提取主題區域以及特徵的時候採用不同的手段。我們講數據根據圖像內容分為七類, 並分別設計主題區域提取方法和設計特徵。我們通過翔實的實驗數據，證明提出的框架之有效。 / 同時, 我們提出根據圖像內容特徵構建自適應分類器, 以在不事先知道圖像內容分類的情況下進行自動質量評估, 並取得滿意效果。 / Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using both regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted both from subject areas and background regions are combined with global features to assess photo quality. Since professional photographers adopt different photographic techniques and have different aesthetic criteria in mind when taking different types of photos (e.g. landscape versus portrait), we propose to segment subject areas and extract visual features in different ways according to the variety of photo content. We divide the photos into seven categories based on the irvisual content and develop a set of new subject are a extraction methods and new visual features specially designed for different categories. / This argument is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new features over different categories of photos. In addition, we propose an approach of online training an adaptive classifier to combine the proposed features according to the visual content of a test photo without knowing its category. Another contribution of this work is to construct a large and diversified benchmark database for the research of photo quality assessment. It includes 17, 613 photos with manually labeled ground truth. This new benchmark database will be released to the research community. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Luo, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 47-52). / Abstracts also in Chinese. / Chapter Abstract --- p.i / Chapter Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Photo Quality Assessment by Professionals --- p.2 / Chapter 1.2 --- Automatic Quality Assessment --- p.6 / Chapter 1.3 --- Our Approach --- p.8 / Chapter 2 --- RelatedWork --- p.12 / Chapter 3 --- Content-based Quality Assessment --- p.15 / Chapter 3.1 --- Global Features --- p.15 / Chapter 3.1.1 --- Hue Composition Feature --- p.15 / Chapter 3.1.2 --- Scene Composition Feature --- p.19 / Chapter 3.2 --- Subject Area Extraction Methods --- p.21 / Chapter 3.2.1 --- Clarity-Based Subject Area Extraction --- p.22 / Chapter 3.2.2 --- Layout-Based Subject Area Extraction --- p.25 / Chapter 3.2.3 --- Human-Based Subject Area Extraction --- p.25 / Chapter 3.3 --- Regional Features --- p.25 / Chapter 3.3.1 --- Dark Channel Feature --- p.27 / Chapter 3.3.2 --- Clarity Contrast Feature --- p.28 / Chapter 3.3.3 --- Lighting Contrast Feature --- p.30 / Chapter 3.3.4 --- Composition Geometry Feature --- p.30 / Chapter 3.3.5 --- Complexity Features --- p.31 / Chapter 3.3.6 --- Human Based Features --- p.31 / Chapter 3.4 --- Quality Assessment without the Information of Photo Categories --- p.33 / Chapter 4 --- Experimental Results --- p.37 / Chapter 4.1 --- Database description --- p.37 / Chapter 4.2 --- Experimental Settings --- p.40 / Chapter 4.3 --- Result Analysis --- p.41 / Chapter 4.4 --- Conclusions and Discussions --- p.44 / Bibliography --- p.47

Cheung, Kin Wang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 76-82). / Abstract also in Chinese. / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1. --- Background --- p.1 / Chapter 1.1.1. --- Introduction to Biometrics --- p.1 / Chapter 1.1.2. --- Face Recognition in General --- p.2 / Chapter 1.1.3. --- A Typical Face Recognition System Architecture --- p.4 / Chapter 1.1.4. --- Face Recognition in Surveillance Cameras --- p.6 / Chapter 1.1.5. --- Face recognition under Pose Variation --- p.9 / Chapter 1.2. --- Motivation and Objectives --- p.11 / Chapter 1.3. --- Related Works --- p.13 / Chapter 1.3.1. --- Overview of Pose-invariant Face Recognition --- p.13 / Chapter 1.3.2. --- Standard Face Recognition Setting --- p.14 / Chapter 1.3.3. --- Multi-Probe Setting --- p.19 / Chapter 1.3.4. --- Multi-Gallery Setting --- p.21 / Chapter 1.3.5. --- Non-frontal Face Databases --- p.23 / Chapter 1.3.6. --- Evaluation Metrics --- p.26 / Chapter 1.3.7. --- Summary of Non-frontal Face Recognition Settings --- p.27 / Chapter 1.4. --- Proposed Methods for Non-frontal Face Recognition --- p.28 / Chapter 1.5. --- Thesis Organization --- p.30 / Chapter 2. --- PARTIAL ELASTIC BUNCH GRAPH MATCHING --- p.31 / Chapter 2.1. --- Introduction --- p.31 / Chapter 2.2. --- EBGM for Non-frontal Face Recognition --- p.31 / Chapter 2.2.1. --- Overview of Baseline EBGM Algorithm --- p.31 / Chapter 2.2.2. --- Modified EBGM for Non-frontal Face Matching --- p.33 / Chapter 2.3. --- Experiments --- p.35 / Chapter 2.3.1. --- Experimental Setup --- p.35 / Chapter 2.3.2. --- Experimental Results --- p.37 / Chapter 2.4. --- Discussions --- p.40 / Chapter 3. --- FACE RECOGNITION BY FRONTAL VIEW SYNTHESIS WITH CALIBRATED STEREO CAMERAS --- p.43 / Chapter 3.1. --- Introduction --- p.43 / Chapter 3.2. --- Proposed Method --- p.44 / Chapter 3.2.1. --- Image Rectification --- p.45 / Chapter 3.2.2. --- Face Detection --- p.49 / Chapter 3.2.3. --- Head Pose Estimation --- p.51 / Chapter 3.2.4. --- Virtual View Generation --- p.52 / Chapter 3.2.5. --- Feature Localization --- p.54 / Chapter 3.2.6. --- Face Morphing --- p.56 / Chapter 3.3. --- Experiments --- p.58 / Chapter 3.3.1. --- Data Collection --- p.58 / Chapter 3.3.2. --- Synthesized Results --- p.59 / Chapter 3.3.3. --- Experiment Setup --- p.60 / Chapter 3.3.4. --- Experiment Results on FERET database --- p.61 / Chapter 3.3.5. --- Experiment Results on CAS-PEAL-R1 database --- p.62 / Chapter 3.4. --- Discussions --- p.64 / Chapter 3.5. --- Summary --- p.66 / Chapter 4. --- "EXPERIMENTS, RESULTS AND OBSERVATIONS" --- p.67 / Chapter 4.1. --- Experiment Setup --- p.67 / Chapter 4.2. --- Experiment Results --- p.69 / Chapter 4.3. --- Discussions --- p.70 / Chapter 5. --- CONCLUSIONS --- p.74 / Chapter 6. --- BIBLIOGRAPHY --- p.76

There is much debate in the design community concerning how to make an easy-to-understand graph. While expert designers recommend including as little non-data ink as possible, there is little empirical evidence to support their arguments. Non-data ink refers to any ink on a graph that is not required to display the graph's data. As a result of the lack of strong evidence concerning how to design graphs, there is widespread confusion when it comes to best practices. This paper describes a preliminary study of graph perception and learning using an eye-tracking system at UNC's School of Information and Library Science.

No description available.

M.Com. (Accounting) / Please refer to full text to view abstract

No description available.

D.Ing. / This thesis deals with an investigation into facial recognition and some variables that influence the performance of such a system. Firstly there is an investigation into the influence of image variability on the overall recognition performance of a system and secondly the performance and subsequent suitability of a neural network based system is tested. Both tests are carried out on two distinctly different databases, one more variable than the other. The results indicate that the greater the amount of variability the more negatively affected is the performance rating of a specific facial recognition system. The results further indicate the success with the implementation of a neural network system over a more conventional statistical system.

Yuan Tianqiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 51-55). / Abstracts in English and Chinese. / abstract --- p.i / acknowledgments --- p.iv / table of contents --- p.v / list of figures --- p.vii / list of tables --- p.ix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Reflectional Symmetry Detection --- p.1 / Chapter 1.2 --- Research Progress on Face Analysis --- p.2 / Chapter 1.2.1 --- Face Detection --- p.3 / Chapter 1.2.2 --- Face Alignment --- p.4 / Chapter 1.2.3 --- Face Recognition --- p.6 / Chapter 1.3 --- Organization of this thesis --- p.8 / Chapter Chapter 2 --- Local reflectional symmetry detection --- p.9 / Chapter 2.1 --- Proposed Method --- p.9 / Chapter 2.1.1 --- Symmetry measurement operator --- p.9 / Chapter 2.1.2 --- Potential regions selection --- p.10 / Chapter 2.1.3 --- Detection of symmetry axes --- p.11 / Chapter 2.2 --- Experiments --- p.13 / Chapter 2.2.1 --- Parameter setting and analysis --- p.13 / Chapter 2.2.2 --- Experimental Results --- p.14 / Chapter Chapter 3 --- Global perspective reflectional symmetry detection --- p.16 / Chapter 3.1 --- Introduction of camera models --- p.16 / Chapter 3.2 --- Property of Symmetric Point-Pair --- p.18 / Chapter 3.3 --- analysis and Experiment --- p.20 / Chapter 3.3.1 --- Confirmative Experiments --- p.20 / Chapter 3.3.2 --- Face shape generation with PSI --- p.22 / Chapter 3.3.3 --- Error Analysis --- p.24 / Chapter 3.3.4 --- Experiments of Pose Estimation --- p.25 / Chapter 3.4 --- Summary --- p.28 / Chapter Chapter 4 --- Pre-processing of face analysis --- p.30 / Chapter 4.1 --- Introduction of Hough Transform --- p.30 / Chapter 4.2 --- Eye Detection --- p.31 / Chapter 4.2.1 --- Coarse Detection --- p.32 / Chapter 4.2.2 --- Refine the eyes positions --- p.34 / Chapter 4.2.3 --- Experiments and Analysis --- p.35 / Chapter 4.3 --- Face Components Detection with GHT --- p.37 / Chapter 4.3.1 --- Parameter Analyses --- p.38 / Chapter 4 3.2 --- R-table Construction --- p.38 / Chapter 4.3.3 --- Detection Procedure and Voting Strategy --- p.39 / Chapter 4.3.4 --- Experiments and Analysis --- p.41 / Chapter Chapter 5 --- Pose estimation with face symmetry --- p.45 / Chapter 5.1 --- Key points selection --- p.45 / Chapter 5.2 --- Face Pose Estimation --- p.46 / Chapter 5.2.1 --- Locating eye corners --- p.46 / Chapter 5.2.2 --- Analysis and Summary --- p.47 / Chapter Chapter 6 --- Conclusions and future work --- p.49 / bibliography --- p.51