No description available.

DNA sequence analysis involves precise discrimination of two of the sequence's most important components: exons and introns. Exons encode the proteins that are responsible for almost all the functions in a living organism. Introns interrupt the sequence coding for a protein and must be removed from primary RNA transcripts before translation to protein can occur. A pattern recognition technique called Finite Induction (FI) is utilized to study the language of exons and introns. FI is especially suited for analyzing and classifying large amounts of data representing sequences of interest. It requires no biological information and employs no statistical functions. Finite Induction is applied to the exon and intron components of DNA by building a collection of rules based upon what it finds in the sequences it examines. It then attempts to match the known rule patterns with new rules formed as a result of analyzing a new sequence. A high number of matches predict a probable close relationship between the two sequences; a low number of matches signifies a large amount of difference between the two. This research demonstrates FI to be a viable tool for measurement when known patterns are available for the formation of rule sets.

No description available.

by Cheung Wai-Hung Wellis. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 52-54). / ACKNOWLEDGEMENTS / ABSTRACT / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- CLASSIFICATION OF CHARACTER RECOGNITION --- p.1 / Chapter 1.2 --- HISTORICAL DEVELOPMENT --- p.3 / Chapter 1.3 --- RECOGNITION METHODOLOGY --- p.4 / Chapter 2 --- ORGANIZATION OF THIS REPORT --- p.7 / Chapter 3 --- DATA SAMPLING --- p.8 / Chapter 3.1 --- GENERAL CONSIDERATION --- p.8 / Chapter 3.2 --- IMPLEMENTATION --- p.9 / Chapter 4 --- PREPROCESSING --- p.10 / Chapter 4.1 --- GENERAL CONSIDERATION --- p.10 / Chapter 4.2 --- IMPLEMENTATION --- p.12 / Chapter 4.2.1 --- Stroke connection --- p.12 / Chapter 4.2.2 --- Rotation --- p.12 / Chapter 4.2.3 --- Scaling --- p.14 / Chapter 4.2.4 --- De-skewing --- p.15 / Chapter 5 --- STROKE SEGMENTATION --- p.17 / Chapter 5.1 --- CONSIDERATION --- p.17 / Chapter 5.2 --- IMPLEMENTATION --- p.20 / Chapter 6 --- LEARNING --- p.26 / Chapter 7 --- PROTOTYPE MANAGEMENT --- p.27 / Chapter 8 --- RECOGNITION --- p.29 / Chapter 8.1 --- CONSIDERATION --- p.29 / Chapter 8.1.1 --- Delayed Stroke Tagging --- p.29 / Chapter 8.1.2 --- Bi-gram --- p.29 / Chapter 8.1.3 --- Character Scoring --- p.30 / Chapter 8.1.4 --- Ligature Handling --- p.32 / Chapter 8.1.5 --- Word Scoring --- p.32 / Chapter 8.2 --- IMPLEMENTATION --- p.33 / Chapter 8.2.1 --- Simple Matching --- p.33 / Chapter 8.2.2 --- Best First Search Matching --- p.33 / Chapter 8.2.3 --- Multiple Track Method --- p.35 / Chapter 8.3 --- SYSTEM PERFORMANCE TUNING --- p.37 / Chapter 9 --- POST-PROCESSING --- p.38 / Chapter 9.1 --- PROBABILITY MODEL --- p.38 / Chapter 9.2 --- WORD DICTIONARY APPROACH --- p.39 / Chapter 10 --- SYSTEM IMPLEMENTATION AND PERFORMANCE --- p.41 / Chapter 11 --- DISCUSSION --- p.43 / Chapter 12 --- EPILOG --- p.47 / Chapter APPENDIX I - --- PROBLEMS ENCOUNTERED AND SUGGESTED ENHANCEMENTS ON THE SYSTEM --- p.48 / Chapter APPENDIX II - --- GLOSSARIES --- p.51 / REFERENCES --- p.52

by Tsang Kwok Hang Elton. / Thesis submitted in: December 1998. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 78-83). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Virtual-hand Recognition --- p.5 / Chapter 2.1 --- Hand model --- p.6 / Chapter 2.1.1 --- Hand structure --- p.6 / Chapter 2.1.2 --- Motions of the hand joints --- p.8 / Chapter 2.2 --- Hand-tracking technologies --- p.9 / Chapter 2.2.1 --- Glove-based tracking --- p.10 / Chapter 2.2.2 --- Image-based tracking --- p.12 / Chapter 2.3 --- Problems in virtual-hand recognition --- p.13 / Chapter 2.3.1 --- Hand complexity --- p.13 / Chapter 2.3.2 --- Human variations --- p.13 / Chapter 2.3.3 --- Immature hand-tracking technologies --- p.14 / Chapter 2.3.4 --- Time-varying signal --- p.14 / Chapter 2.3.5 --- Efficiency --- p.14 / Chapter 3 --- Previous Work --- p.16 / Chapter 3.1 --- Posture and gesture recognition algorithms --- p.16 / Chapter 3.1.1 --- Template Matching --- p.17 / Chapter 3.1.2 --- Neural networks --- p.18 / Chapter 3.1.3 --- Statistical classification --- p.20 / Chapter 3.1.4 --- Discontinuity matching --- p.21 / Chapter 3.1.5 --- Model-based analysis --- p.23 / Chapter 3.1.6 --- Hidden Markov Models --- p.23 / Chapter 3.2 --- Hand-input systems --- p.24 / Chapter 3.2.1 --- Gesture languages --- p.25 / Chapter 3.2.2 --- 3D modeling --- p.25 / Chapter 3.2.3 --- Medical visualization --- p.26 / Chapter 4 --- Posture Recognition --- p.28 / Chapter 4.1 --- Fuzzy concepts --- p.28 / Chapter 4.1.1 --- Degree of membership --- p.29 / Chapter 4.1.2 --- Certainty factor --- p.30 / Chapter 4.1.3 --- Evidence combination --- p.30 / Chapter 4.2 --- Fuzzy posture recognition system --- p.31 / Chapter 4.2.1 --- Objectives --- p.32 / Chapter 4.2.2 --- System overview --- p.32 / Chapter 4.2.3 --- Input parameters --- p.33 / Chapter 4.2.4 --- Posture database --- p.36 / Chapter 4.2.5 --- Classifier --- p.37 / Chapter 4.2.6 --- Identifier --- p.40 / Chapter 5 --- Performance Evaluation --- p.42 / Chapter 5.1 --- Experiments --- p.42 / Chapter 5.1.1 --- Accuracy analysis --- p.43 / Chapter 5.1.2 --- Efficiency analysis --- p.46 / Chapter 5.2 --- Discussion --- p.48 / Chapter 5.2.1 --- Strengths and weaknesses --- p.48 / Chapter 5.2.2 --- Summary --- p.50 / Chapter 6 --- Posture Database Editor --- p.51 / Chapter 6.1 --- System architecture --- p.51 / Chapter 6.1.1 --- Hardware configuration --- p.51 / Chapter 6.1.2 --- Software tools --- p.53 / Chapter 6.2 --- User interface --- p.54 / Chapter 6.2.1 --- Menu bar --- p.55 / Chapter 6.2.2 --- Working frame and data frame --- p.56 / Chapter 6.2.3 --- Control panel --- p.56 / Chapter 7 --- An Application: 3D Virtual World Modeler --- p.59 / Chapter 7.1 --- System Design --- p.60 / Chapter 7.2 --- Common operations --- p.62 / Chapter 7.3 --- Virtual VRML Worlds --- p.65 / Chapter 8 --- Conclusion --- p.70 / Chapter 8.1 --- Summaries on previous work --- p.70 / Chapter 8.2 --- Contributions --- p.73 / Chapter 9 --- Future Work --- p.75 / Bibliography --- p.78

Structural Health Monitoring (SHM) is concerned with the analysis of aerospace, mechanical and civil systems with the objective of identifying damage at its onset. In civil engineering applications, damage may be defined as any change in the structural properties that hinders the current or future performance of that system. This is the premise on which vibration-based techniques are based. Vibration-based methods exploit the response measured directly on the system to solve the SHM assignment. However, also fluctuations in the external conditions may induce changes in the structural properties. For these reasons, the SHM problem is ideally suited to be solved within the context of statistical pattern recognition, which is the discipline concerned with the automatic classification of objects into categories. Within the statistical pattern recognition based SHM framework, the structural response is portrayed by means of a compact representation of its main traits, called damage sensitive features (dsf). In this dissertation, two typologies of dsf are studied: the first type is extracted from the response of the system by means of digital signal processes alone, while the other is obtained by making use of a physical model of the system. In both approaches, the effects of external conditions are accounted for by modeling the damage sensitive features as random variables. While the first method uses outlier analysis tools and delivers a method optimally apt to perform the task of damage detection within the short-term horizon, the second approach, being model-based, allows for a deeper characterization of damage, and it is then more suited for long-term monitoring purposes. In the dissertation, an approach is also proposed that allows the use of the statistical pattern recognition framework when there is limited availability of data to model the damage sensitive features. All proposed methodologies are validated both numerically and experimentally.

by Tung Lun Hsing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 80-84). / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Survey --- p.8 / Chapter 2.1 --- The Freeman Chain Code Approach --- p.8 / Chapter 2.2 --- The Moment Approach --- p.10 / Chapter 2.3 --- The Rectangular Cover Approach --- p.12 / Chapter 2.4 --- The Potential-Based Approach --- p.15 / Chapter 2.5 --- The Normalized Coordinate System Approach --- p.17 / Chapter 2.6 --- The Hausdorff Distance Method --- p.20 / Chapter 2.7 --- The PCA Approach --- p.22 / Chapter 3 --- Binary Shape Descriptor --- p.26 / Chapter 3.1 --- Basic idea --- p.26 / Chapter 3.2 --- Standardized Binary String Descriptor --- p.27 / Chapter 3.3 --- Number of equivalent classes for n-gons --- p.28 / Chapter 4 --- The Two-Stage Framework --- p.30 / Chapter 5 --- Multi-Resolution Area Matching --- p.33 / Chapter 5.1 --- The idea --- p.33 / Chapter 5.2 --- Computing MRAI --- p.34 / Chapter 5.3 --- Measuring similarity using MRAI --- p.36 / Chapter 5.4 --- Query processing using MRAM --- p.38 / Chapter 5.5 --- Characteristics and Discussion --- p.40 / Chapter 6 --- Circular Error Bound and Minimum Circular Error Bound --- p.41 / Chapter 6.1 --- Polygon Matching using Circular Error Bound --- p.41 / Chapter 6.1.1 --- Translation --- p.43 / Chapter 6.1.2 --- Translation and uniform scaling in x-axis and y-axis directions --- p.45 / Chapter 6.1.3 --- Translation and independent scaling in x-axis and y-axis directions --- p.47 / Chapter 6.2 --- Minimum Circular Error Bound --- p.48 / Chapter 6.3 --- Characteristics --- p.49 / Chapter 7 --- Experimental Results --- p.50 / Chapter 7.1 --- Setup --- p.50 / Chapter 7.1.1 --- Polygon generation --- p.51 / Chapter 7.1.2 --- Database construction --- p.52 / Chapter 7.1.3 --- Query processing --- p.54 / Chapter 7.2 --- Running time comparison --- p.55 / Chapter 7.2.1 --- Experiment I --- p.55 / Chapter 7.2.2 --- Experiment II --- p.58 / Chapter 7.2.3 --- Experiment III --- p.60 / Chapter 7.3 --- Visual ranking comparison --- p.61 / Chapter 7.3.1 --- Experiment I --- p.61 / Chapter 7.3.2 --- Experiment II --- p.62 / Chapter 7.3.3 --- Experiment III --- p.63 / Chapter 7.3.4 --- Conclusion on visual ranking experiments --- p.66 / Chapter 8 --- Discussion --- p.68 / Chapter 8.1 --- N-ary Shape Descriptor --- p.68 / Chapter 8.2 --- Distribution of polygon equivalent classes --- p.69 / Chapter 8.3 --- Comparing polygons with different number of vertices --- p.72 / Chapter 8.4 --- Relaxation of assumptions --- p.73 / Chapter 8.4.1 --- Non-degenerate --- p.74 / Chapter 8.4.2 --- Simple --- p.74 / Chapter 8.4.3 --- Closed --- p.76 / Chapter 9 --- Conclusion --- p.78 / Bibliography --- p.80

Zhang, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 95-109). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Area of Machine Learning --- p.1 / Chapter 1.1.1 --- Types of Algorithms --- p.2 / Chapter 1.1.2 --- Modeling Assumptions --- p.4 / Chapter 1.2 --- Dimensionality Reduction --- p.4 / Chapter 1.3 --- Structure of the Thesis --- p.8 / Chapter 2 --- Dimensionality Reduction --- p.10 / Chapter 2.1 --- Feature Extraction --- p.11 / Chapter 2.1.1 --- Linear Feature Extraction --- p.11 / Chapter 2.1.2 --- Nonlinear Feature Extraction --- p.16 / Chapter 2.1.3 --- Sparse Feature Extraction --- p.19 / Chapter 2.1.4 --- Nonnegative Feature Extraction --- p.19 / Chapter 2.1.5 --- Incremental Feature Extraction --- p.20 / Chapter 2.2 --- Feature Selection --- p.20 / Chapter 2.2.1 --- Viewpoint of Feature Extraction --- p.21 / Chapter 2.2.2 --- Feature-Level Score --- p.22 / Chapter 2.2.3 --- Subset-Level Score --- p.22 / Chapter 3 --- Various Views of Feature Extraction --- p.24 / Chapter 3.1 --- Probabilistic Models --- p.25 / Chapter 3.2 --- Matrix Factorization --- p.26 / Chapter 3.3 --- Graph Embedding --- p.28 / Chapter 3.4 --- Manifold Learning --- p.28 / Chapter 3.5 --- Distance Metric Learning --- p.32 / Chapter 4 --- Tensor linear Laplacian discrimination --- p.34 / Chapter 4.1 --- Motivation --- p.35 / Chapter 4.2 --- Tensor Linear Laplacian Discrimination --- p.37 / Chapter 4.2.1 --- Preliminaries of Tensor Operations --- p.38 / Chapter 4.2.2 --- Discriminant Scatters --- p.38 / Chapter 4.2.3 --- Solving for Projection Matrices --- p.40 / Chapter 4.3 --- Definition of Weights --- p.44 / Chapter 4.3.1 --- Contextual Distance --- p.44 / Chapter 4.3.2 --- Tensor Coding Length --- p.45 / Chapter 4.4 --- Experimental Results --- p.47 / Chapter 4.4.1 --- Face Recognition --- p.48 / Chapter 4.4.2 --- Texture Classification --- p.50 / Chapter 4.4.3 --- Handwritten Digit Recognition --- p.52 / Chapter 4.5 --- Conclusions --- p.54 / Chapter 5 --- Semi-Supervised Semi-Riemannian Metric Map --- p.56 / Chapter 5.1 --- Introduction --- p.57 / Chapter 5.2 --- Semi-Riemannian Spaces --- p.60 / Chapter 5.3 --- Semi-Supervised Semi-Riemannian Metric Map --- p.61 / Chapter 5.3.1 --- The Discrepancy Criterion --- p.61 / Chapter 5.3.2 --- Semi-Riemannian Geometry Based Feature Extraction Framework --- p.63 / Chapter 5.3.3 --- Semi-Supervised Learning of Semi-Riemannian Metrics --- p.65 / Chapter 5.4 --- Discussion --- p.72 / Chapter 5.4.1 --- A General Framework for Semi-Supervised Dimensionality Reduction --- p.72 / Chapter 5.4.2 --- Comparison to SRDA --- p.74 / Chapter 5.4.3 --- Advantages over Semi-supervised Discriminant Analysis --- p.74 / Chapter 5.5 --- Experiments --- p.75 / Chapter 5.5.1 --- Experimental Setup --- p.76 / Chapter 5.5.2 --- Face Recognition --- p.76 / Chapter 5.5.3 --- Handwritten Digit Classification --- p.82 / Chapter 5.6 --- Conclusion --- p.84 / Chapter 6 --- Summary --- p.86 / Chapter A --- The Relationship between LDA and LLD --- p.89 / Chapter B --- Coding Length --- p.91 / Chapter C --- Connection between SRDA and ANMM --- p.92 / Chapter D --- From S3RMM to Graph-Based Approaches --- p.93 / Bibliography --- p.95

No description available.

A variety of measures are enlisted in an explanation of some longstanding perceptual phenomena associated with an assortment of visual patterns. In following the proximity principle of Gestalt psychology, these are commonly based upon a statistical treatment applied to one or another of a hierarchy of distance measures. Following from this, some problems of visual perception are tackled in terms of an active perceiving mechanism, which generates transformations in the realization of object invariance in space and over time. This generative transformational approach is also employed in an account of perception of various patterns and visual illusions. Although a range of proximity measures is involved throughout, the nearest neighbour metric is staple. For perception of unstructured visual arrays, the contribution of distance mechanisms, particularly nearest neighbours, is shown to be important. For structured arrays, the contribution of distance mechanisms along with transformations is important. Information about relative positions of image elements permits the selection of transformations that reveal structure. With respect to such information, however, the proximity principle is taken to its limits. / Thesis (Ph.D.)--School of Psychology, 2006.