Symmetry for face analysis.

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

ON SYMMETRY: A FRAMEWORK FOR AUTOMATED SYMMETRY DETECTION

Symmetry has weaved itself into almost all fabrics of science, as well as in arts, and has left an indelible imprint on our everyday lives. And, in the same manner, it has pervaded a wide range of areas of computer science, especially computer vision area, and a copious amount of literature has been produced to seek an algorithmic way to identify symmetry in digital data. Notwithstanding decades of endeavor and attempt to have an efficient system that can locate and recover symmetry embedded in real-world images, it is still challenging to fully automate such tasks while maintaining a high level of efficiency. The subject of this thesis is symmetry of imaged objects. Symmetry is one of the non-accidental features of shapes and has long been (maybe mistakenly) speculated as a pre-attentive feature, which improves recognition of quickly presented objects and reconstruction of shapes from incomplete set of measurements. While symmetry is known to provide rich and useful geometric cues to computer vision, it has been barely used as a principal feature for applications because figuring out how to represent and recognize symmetries embedded in objects is a singularly difficult task, both for computer vision and for perceptual psychology. The three main problems addressed in the dissertation are: (i) finding approximate symmetry by identifying the most prominent axis of symmetry out of an entire region, (ii) locating bilaterally symmetrical areas from a scene, and (iii) automating the process of symmetry recovery by solving the problems mentioned above. Perfect symmetries are rare in the extreme in natural images and symmetry perception in humans allows for qualification so that symmetry can be graduated based on the degree of structural deformation or replacement error. There have been many approaches to detect approximate symmetry by searching an optimal solution in a form of an exhaustive exploration of the parameter space or surmising the center of mass. The algorithm set out in this thesis circumvents the computationally intensive operations by using geometric constraints of symmetric images, and assumes no prerequisite knowledge of the barycenter. The results from an extensive set of evaluation experiments on metrics for symmetry distance and a comparison of the performance between the method presented in this thesis and the state of the art approach are demonstrated as well. Many biological vision systems employ a special computational strategy to locate regions of interest based on local image cues while viewing a compound visual scene. The method taken in this thesis is a bottom-up approach that causes the observer favors stimuli based on their saliency, and creates a feature map contingent on symmetry. With the help of summed area tables, the time complexity of the proposed algorithm is linear in the size of the image. The distinguished regions are then delivered to the algorithm described above to uncover approximate symmetry

A New Measure of Cluster Validity Using Line Symmetry

[[abstract]]Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of "line symmetry" is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. Several data sets are used to illustrate the performance of the proposed measure.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]TW

Estimating Symmetry/Asymmetry in the Human Torso: A Novel Computational Method

Asymmetry in human body has largely been based on bilateral traits and/or subjective estimates, with potential usage in fields such as medicine, rehabilitation and apparel product design. In case of apparel, asymmetry in human body has been measured primarily by estimating differential linear measurement of bilateral traits. However, the characteristics of asymmetry can be better understood and be useful for clinicians and designers if it is quantified by considering the whole 3D surface. To address the prevailing issues in measuring asymmetry objectively, this research attempts to develop a novel method to quantify asymmetry that is robust, effective and non-invasive in operation. The method discussed here uses 3D scans of human torso to estimate asymmetry as a numerical index. Furthermore, using skeletal landmarks, twist and tilt measurements of the torsos are computed numerically. Together, these three measures can characterize the asymmetric/symmetric nature of a human torso. The approach taken in this research uses cross sections of torso to estimate local plane of symmetry that equi-divides a given cross section on the basis of its area, and connecting those planes to form a global surface that divides the torso volumetrically. The computational approach in estimating the area of cross section is based on the Green's theorem. The developed method was validated by both testing it on a known geometric model and by comparing the estimated index with subjective ratings by experts. This method has potential applications in various fields requiring characterizing asymmetry i.e., in case of scoliosis patients as diagnostic tool or an evaluation metric for rehabilitation efficiency, for body builders, and fashion models as an evaluation tool.Design, Housing and Merchandisin

Image Processing and Pattern Recognition Applied to Soil Structure

This thesis represents a collaborative research between the Department of Electronics & Electrical Engineering and the Department of Civil Engineering, University of Glasgow. The project was initially aimed at development of some theories and techniques of image processing and pattern recognition for the study of soil microstructures. More specifically, the aim was to study the shapes, orientations, and arrangements of soil particles and voids (i.e. pores): these three are very important properties, which are used both for description, recognition and classification of soils, and also for studying the relationships between the soil structures and physical, chemical, geological, geographical, and environmental changes. The work presented here was based principally on a need for analysing the structure of soil as recorded in two-dimensional images which might be conventional photographs, optical micrographs, or electron-micrographs. In this thesis, first a brief review of image processing and pattern recognition and their previous application in the study of soil microstructures is given. Then a convex hull based shape description and classification for soil particles is presented. A new algorithm, SPCH, is proposed for finding the convex hull of either a binary object or a cluster of points in a plane. This algorithm is efficient and reliable. Features of pattern vectors for shape description and classification are obtained from the convex hull and the object. These features are invariant with respect to coordinate rotation, translation, and scaling. The objects can then be classified by any standard feature-space method: here minimum-distance classification was used. Next the orientation analysis of soil particles is described. A new method, Directed Vein, is proposed for the analysis. Another three methods: Convex Hull, Principal Components, and Moments, are also presented. Comparison of the four methods shows that the Directed Vein method appears the fastest; but it also has the special property of estimating an 'internal preferred orientation' whereas the other methods estimate an 'elongation direction'. Fourth, the roundness/sharpness analysis of soil particles is presented. Three new algorithms, referred to as the Centre, Gradient Centre, and Radius methods, all based on the Circular Hough Transform, are proposed. Two traditional Circular Hough Transform algorithms are presented as well. The three new methods were successfully applied to the measurement of the roundness (sharpness of comers) of two-dimensional particles. The five methods were compared from the points of view of memory requirement, speed, and accuracy; and the Radius method appears to be the best for the special topic of sharpness/roundness analysis. Finally the analysis and classification of aggregates of objects is introduced. A new method. Extended Linear Hough Transform, is proposed. In this method, the orientations and locations of the objects are mapped into extended Hough space. The arrangements of the objects within an aggregate are then determined by analysing the data distributions in this space. The aggregates can then be classified using a tree classifier. Taken together, the methods developed or tested here provide a useful toolkit for analysing the shapes, orientation, and aggregation of particles such as those seen in two-dimensional images of soil structure at various scales

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The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

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