Indexing and Retrieval of 3D Articulated Geometry Models

In this PhD research study, we focus on building a content-based search engine for 3D articulated geometry models. 3D models are essential components in nowadays graphic applications, and are widely used in the game, animation and movies production industry. With the increasing number of these models, a search engine not only provides an entrance to explore such a huge dataset, it also facilitates sharing and reusing among different users. In general, it reduces production costs and time to develop these 3D models. Though a lot of retrieval systems have been proposed in recent years, search engines for 3D articulated geometry models are still in their infancies. Among all the works that we have surveyed, reliability and efficiency are the two main issues that hinder the popularity of such systems. In this research, we have focused our attention mainly to address these two issues. We have discovered that most existing works design features and matching algorithms in order to reflect the intrinsic properties of these 3D models. For instance, to handle 3D articulated geometry models, it is common to extract skeletons and use graph matching algorithms to compute the similarity. However, since this kind of feature representation is complex, it leads to high complexity of the matching algorithms. As an example, sub-graph isomorphism can be NP-hard for model graph matching. Our solution is based on the understanding that skeletal matching seeks correspondences between the two comparing models. If we can define descriptive features, the correspondence problem can be solved by bag-based matching where fast algorithms are available. In the first part of the research, we propose a feature extraction algorithm to extract such descriptive features. We then convert the skeletal matching problems into bag-based matching. We further define metric similarity measure so as to support fast search. We demonstrate the advantages of this idea in our experiments. The improvement on precision is 12\% better at high recall. The indexing search of 3D model is 24 times faster than the state of the art if only the first relevant result is returned. However, improving the quality of descriptive features pays the price of high dimensionality. Curse of dimensionality is a notorious problem on large multimedia databases. The computation time scales exponentially as the dimension increases, and indexing techniques may not be useful in such situation. In the second part of the research, we focus ourselves on developing an embedding retrieval framework to solve the high dimensionality problem. We first argue that our proposed matching method projects 3D models on manifolds. We then use manifold learning technique to reduce dimensionality and maximize intra-class distances. We further propose a numerical method to sub-sample and fast search databases. To preserve retrieval accuracy using fewer landmark objects, we propose an alignment method which is also beneficial to existing works for fast search. The advantages of the retrieval framework are demonstrated in our experiments that it alleviates the problem of curse of dimensionality. It also improves the efficiency (3.4 times faster) and accuracy (30\% more accurate) of our matching algorithm proposed above. In the third part of the research, we also study a closely related area, 3D motions. 3D motions are captured by sticking sensor on human beings. These captured data are real human motions that are used to animate 3D articulated geometry models. Creating realistic 3D motions is an expensive and tedious task. Although 3D motions are very different from 3D articulated geometry models, we observe that existing works also suffer from the problem of temporal structure matching. This also leads to low efficiency in the matching algorithms. We apply the same idea of bag-based matching into the work of 3D motions. From our experiments, the proposed method has a 13\% improvement on precision at high recall and is 12 times faster than existing works. As a summary, we have developed algorithms for 3D articulated geometry models and 3D motions, covering feature extraction, feature matching, indexing and fast search methods. Through various experiments, our idea of converting restricted matching to bag-based matching improves matching efficiency and reliability. These have been shown in both 3D articulated geometry models and 3D motions. We have also connected 3D matching to the area of manifold learning. The embedding retrieval framework not only improves efficiency and accuracy, but has also opened a new area of research

Analysis of switching dc-dc converters using a grid-point approach

Author name used in this publication: P. K. S. TamAuthor name used in this publication: F. H. F. LeungVersion of RecordPublishe

Neural Plastic Effects of Cognitive Training on Aging Brain.

published_or_final_versio

Tree management and the greening of the environment in Hong Kong : a study of collaborative governance

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

Predictors of functional deterioration in Chinese patients with Psoriatic arthritis: A longitudinal study.

10.1186/1471-2474-15-284BMC Musculoskeletal Disorders15128

A Multidimensional PERMA-H Positive Education Model, General Satisfaction of School Life, and Character Strengths Use in Hong Kong Senior Primary School Students: Confirmatory Factor Analysis and Path Analysis Using the APASO-II

The multidimensional PERMA-H positive education model provided evaluation and education framework for the theoretical and practice development of positive psychology in schools. Character strengths use mediates the association of strength knowledge and well-being. Using the Assessment Program for Affective and Social Outcomes (2nd Version) (APASO-II), the Subjective Happiness Scale, and the Physical Health Subscale of the PERMA-profiler, a multidimensional measure of PERMA-H was validated using confirmatory factor analysis in the context of a positive education program evaluation in senior primary school students. The association of PERMA-H measurements with school well-being as measured by general satisfaction of school life, and levels of depression and anxiety, and the mediation mechanism of character strengths use in such association were studied using path analysis. A cross-sectional sample of 726 senior primary school students (i.e., grade 4–6) aged 8–13 from the two primary schools completed a baseline evaluation questionnaire of a positive education program. Satisfactory internal reliability of the scales was obtained with Cronbach's alpha coefficients < 0.70. The scales were generally positively and moderately inter-correlated, except for level of anxiety and depression symptoms which was negative. Good psychometric properties of APASO-II were evidenced from the factor structure of sub-scale scores conforming to six factors of the PERMA-H model by confirmatory factor analysis. Path analyses showed that the APASO-II factors together with measures of subject happiness and positive health as the multidimensional PERMA-H model of positive education differentially predicted general satisfaction of school life, level of anxiety and depression, and character strengths use. Character strengths use mediated the relationship of Positive Engagement with general satisfaction of school life. Positive education utilizes knowledge and research findings from positive psychology in schools to produce intended positive outcomes like enhanced well-being and reduced level of depression in students. This study provided a solid foundation for related scientific research and the understanding of the multidimensional framework of positive psychology concepts. Systematic promotion and longitudinal evaluation of positive education at the institutional level in Hong Kong can be achieved with the use of APASO-II and the positive education scales of subjective happiness and physical health

Measurement of cosmic-ray muons and muon-induced neutrons in the Aberdeen Tunnel Underground Laboratory

postprin

Measurement of Cosmic-ray Muons and Muon-induced Neutrons in the Aberdeen Tunnel Underground Laboratory

We have measured the muon flux and production rate of muon-induced neutrons at a depth of 611 m water equivalent. Our apparatus comprises three layers of crossed plastic scintillator hodoscopes for tracking the incident cosmic-ray muons and 760 L of gadolinium-doped liquid scintillator for producing and detecting neutrons. The vertical muon intensity was measured to be $I_{\mu} = (5.7 \pm 0.6) \times 10^{-6}$ cm$^{-2}$s$^{-1}$sr$^{-1}$. The yield of muon-induced neutrons in the liquid scintillator was determined to be $Y_{n} = (1.19 \pm 0.08 (stat) \pm 0.21 (syst)) \times 10^{-4}$ neutrons/($\mu\cdot$g$\cdot$cm$^{-2}$). A fit to the recently measured neutron yields at different depths gave a mean muon energy dependence of $\left\langle E_{\mu} \right\rangle^{0.76 \pm 0.03}$ for liquid-scintillator targets.Comment: 14 pages, 17 figures, 3 table