39 research outputs found

    Symmetry Signatures for Image-Based Applications in Robotics

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    Symmetry for face analysis.

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    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

    Image processing for plastic surgery planning

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    This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

    PRS-Net: planar reflective symmetry detection net for 3D models

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    In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces

    Tackling the X-ray cargo inspection challenge using machine learning

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    The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection

    Visual attention and active vision:from natural to artificial systems

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