PRINCIPAL GEODESIC ANALYSIS BOUNDARY DELINEATION WITH SUPERPIXEL-BASED CONSTRAINTS

In this paper an algorithm for accurate delineation of object boundaries is proposed. The method employs a superpixel algorithm to obtain an oversegmentation of the input image, used as a constraint in the task. A shape model is built by applying Principal Geodesic Analysis on angular representation of automatically placed uniformly distant landmark points. The shape model is used to detect the boundaries of an object on a given image by iterative elongation of a partial boundary along borders of superpixels. Contrary to many state-of-the-art methods, the proposed approach does not need an initial boundary. The algorithm was tested on two natural and two synthetic sets of images. Mean Dice coefficients between 0.91 and 0.97 were obtained. In almost all cases the object was found. In areas of relatively high gradient magnitude the borders are delineated very accurately, though further research is needed to improve the accuracy in areas of low gradient magnitude and automatically select the parameters of the proposed error function

Image-based tree variations

The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette-defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.Peer ReviewedPostprint (author's final draft

An iterative inference procedure applying conditional random fields for simultaneous classification of land cover and land use

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result

Automatic Extraction of Closed Contours Bounding Salient Objects: New Algorithms and Evaluation Methods

The problem under consideration in this dissertation is achieving salient object segmentation of natural images by means of probabilistic contour grouping. The goal is to extract the simple closed contour bounding the salient object in a given image. The method proposed here falls in the Contour Grouping category, searching for the optimal grouping of boundary entities to form an object contour. Our first contribution is to provide both a ground truth dataset and a performance measure for empirical evaluation of salient object segmentation methods. Our Salient Object Dataset (SOD) provides ground truth boundaries of salient objects perceived by humans in natural images. We also psychophysically evaluated 5 distinct performance measures that have been used in the literature and showed that a measure based upon minimal contour mappings is most sensitive to shape irregularities and most consistent with human judgements. In fact, the Contour Mapping measure is as predictive of human judgements as human subjects are of each other. Contour grouping methods often rely on Gestalt cues locally defined on pairs of oriented features. Accurate integration of these local cues with global cues is a challenge. A second major contribution of this dissertation is a novel, effective method for combining local and global cues. A third major contribution in this dissertation is a novel method based on Principal Component Analysis for promoting diversity among contour hypotheses, leading to substantial improvements in grouping performance. To further improve the performance, a multiscale implementation of this method has been studied. A fourth contribution in this dissertation is studying the effect of the multiscale prior on the performance and analysing the method for combining the results obtained in different resolutions. Our final contribution is comparing the performance of univariate distribution models for local cues used by our method with the use of a multivariate mixture model for their joint distribution. We obtain slight improvement by the mixture models. The proposed method has been evaluated and compared with four other state-of-the-art grouping methods, showing considerably better performance on the SOD ground truth dataset

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

EnViSoRS: Enhanced Vision System for Robotic Surgery. A User-Defined Safety Volume Tracking to Minimize the Risk of Intraoperative Bleeding

open6siIn abdominal surgery, intra-operative bleeding is one of the major complications that affect the outcome of minimally invasive surgical procedures. One of the causes is attributed to accidental damages to arteries or veins, and one of the possible risk factors falls on the surgeon's skills. This paper presents the development and application of an Enhanced Vision System for Robotic Surgery (EnViSoRS), based on a user-defined Safety Volume (SV) tracking to minimise the risk of intra-operative bleeding. It aims at enhancing the surgeon's capabilities by providing Augmented Reality (AR) assistance towards the protection of vessels from injury during the execution of surgical procedures with a robot. The core of the framework consists in: (i) a hybrid tracking algorithm (LT-SAT tracker) that robustly follows a user-defined Safety Area (SA) in long term; (ii) a dense soft tissue 3D reconstruction algorithm, necessary for the computation of the SV; (iii) AR features for visualisation of the SV to be protected and of a graphical gauge indicating the current distance between the instruments and the reconstructed surface. EnViSoRS was integrated with a commercial robotic surgery system (the dVRK system) for testing and validation. The experiments aimed at demonstrating the accuracy, robustness, performance and usability of EnViSoRS during the execution of a simulated surgical task on a liver phantom. Results show an overall accuracy in accordance with surgical requirements (< 5mm), and high robustness in the computation of the SV in terms of precision and recall of its identification. The optimisation strategy implemented to speed up the computational time is also described and evaluated, providing AR features update rate up to 4 fps without impacting the real-time visualisation of the stereo endoscopic video. Finally, qualitative results regarding the system usability indicate that the proposed system integrates well with the commercial surgical robot and has indeed potential to offer useful assistance during real surgeries.openPenza, Veronica; De Momi, Elena; Enayati, Nima; Chupin, Thibaud; Ortiz, Jesús; Mattos, Leonardo S.Penza, Veronica; DE MOMI, Elena; Enayati, Nima; Chupin, THIBAUD JEAN EUDES; Ortiz, Jesús; Mattos, Leonardo S

Optimization of Rooftop Delineation from Aerial Imagery with Deep Learning

High-definition (HD) maps of building rooftops or footprints are important for urban application and disaster management. Rapid creation of such HD maps through rooftop delineation at the city scale using high-resolution satellite and aerial images with deep leaning methods has become feasible and draw much attention. In the context of rooftop delineation, the end-to-end Deep Convolutional Neural Networks (DCNNs) have demonstrated remarkable performance in accurately delineating rooftops from aerial imagery. However, several challenges still exist in this task, which are addressed in this thesis. These challenges include: (1) the generalization issues of models when test data differ from training data, (2) the scale-variance issues in rooftop delineation, and (3) the high cost of annotating accurate rooftop boundaries. To address the challenges mentioned above, this thesis proposes three novel deep learning-based methods. Firstly, a super-resolution network named Momentum and Spatial-Channel Attention Residual Feature Aggregation Network (MSCA-RFANet) is proposed to tackle the generalization issue. The proposed super-resolution network shows better performance compared to its baseline and other state-of-the-art methods. In addition, data composition with MSCA-RFANet shows high performance on dealing with the generalization issues. Secondly, an end-to-end rooftop delineation network named Higher Resolution Network with Dynamic Scale Training (HigherNet-DST) is developed to mitigate the scale-variance issue. The experimental results on publicly available building datasets demonstrate that HigherNet-DST achieves competitive performance in rooftop delineation, particularly excelling in accurately delineating small buildings. Lastly, a weakly supervised deep learning network named Box2Boundary is developed to reduce the annotation cost. The experimental results show that Box2Boundary with post processing is effective in dealing with the cost annotation issues with decent performance. Consequently, the research with these three sub-topics and the three resulting papers are thought to hold potential implications for various practical applications

Automated Pattern Detection and Generalization of Building Groups

This dissertation focuses on the topic of building group generalization by considering the detection of building patterns. Generalization is an important research field in cartography, which is part of map production and the basis for the derivation of multiple representation. As one of the most important features on map, buildings occupy large amount of map space and normally have complex shape and spatial distribution, which leads to that the generalization of buildings has long been an important and challenging task. For social, architectural and geographical reasons, the buildings were built with some special rules which forms different building patterns. Building patterns are crucial structures which should be carefully considered during graphical representation and generalization. Although people can effortlessly perceive these patterns, however, building patterns are not explicitly described in building datasets. Therefore, to better support the subsequent generalization process, it is important to automatically recognize building patterns. The objective of this dissertation is to develop effective methods to detect building patterns from building groups. Based on the identified patterns, some generalization methods are proposed to fulfill the task of building generalization. The main contribution of the dissertation is described as the following five aspects: (1) The terminology and concept of building pattern has been clearly explained; a detailed and relative complete typology of building patterns has been proposed by summarizing the previous researches as well as extending by the author; (2) A stroke-mesh based method has been developed to group buildings and detect different patterns from the building groups; (3) Through the analogy between line simplification and linear building group typification, a stroke simplification based typification method has been developed aiming at solving the generalization of building groups with linear patterns; (4) A mesh-based typification method has been developed for the generalization of the building groups with grid patterns; (5) A method of extracting hierarchical skeleton structures from discrete buildings have been proposed. The extracted hierarchical skeleton structures are regarded as the representations of the global shape of the entire region, which is used to control the generalization process. With the above methods, the building patterns are detected from the building groups and the generalization of building groups are executed based on the patterns. In addition, the thesis has also discussed the drawbacks of the methods and gave the potential solutions.:Abstract I Kurzfassung III Contents V List of Figures IX List of Tables XIII List of Abbreviations XIV Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1.1 Cartographic generalization 1 1.1.2 Urban building and building patterns 1 1.1.3 Building generalization 3 1.1.4 Hierarchical property in geographical objects 3 1.2 Research objectives 4 1.3 Study area 5 1.4 Thesis structure 6 Chapter 2 State of the Art 8 2.1 Operators for building generalization 8 2.1.1 Selection 9 2.1.2 Aggregation 9 2.1.3 Simplification 10 2.1.4 Displacement 10 2.2 Researches of building grouping and pattern detection 11 2.2.1 Building grouping 11 2.2.2 Pattern detection 12 2.2.3 Problem analysis . 14 2.3 Researches of building typification 14 2.3.1 Global typification 15 2.3.2 Local typification 15 2.3.3 Comparison analysis 16 2.3.4 Problem analysis 17 2.4 Summary 17 Chapter 3 Using stroke and mesh to recognize building group patterns 18 3.1 Abstract 19 3.2 Introduction 19 3.3 Literature review 20 3.4 Building pattern typology and study area 22 3.4.1 Building pattern typology 22 3.4.2 Study area 24 3.5 Methodology 25 3.5.1 Generating and refining proximity graph 25 3.5.2 Generating stroke and mesh 29 3.5.3 Building pattern recognition 31 3.6 Experiments 33 3.6.1 Data derivation and test framework 33 3.6.2 Pattern recognition results 35 3.6.3 Evaluation 39 3.7 Discussion 40 3.7.1 Adaptation of parameters 40 3.7.2 Ambiguity of building patterns 44 3.7.3 Advantage and Limitation 45 3.8 Conclusion 46 Chapter 4 A typification method for linear building groups based on stroke simplification 47 4.1 Abstract 48 4.2 Introduction 48 4.3 Detection of linear building groups 50 4.3.1 Stroke-based detection method 50 4.3.2 Distinguishing collinear and curvilinear patterns 53 4.4 Typification method 55 4.4.1 Analogy of building typification and line simplification 55 4.4.2 Stroke generation 56 4.4.3 Stroke simplification 57 4.5 Representation of newly typified buildings 60 4.6 Experiment 63 4.6.1 Linear building group detection 63 4.6.2 Typification results 65 4.7 Discussion 66 4.7.1 Comparison of reallocating remained nodes 66 4.7.2 Comparison with classic line simplification method 67 4.7.3 Advantage 69 4.7.4 Further improvement 71 4.8 Conclusion 71 Chapter 5 A mesh-based typification method for building groups with grid patterns 73 5.1 Abstract 74 5.2 Introduction 74 5.3 Related work 75 5.4 Methodology of mesh-based typification 78 5.4.1 Grid pattern classification 78 5.4.2 Mesh generation 79 5.4.3 Triangular mesh elimination 80 5.4.4 Number and positioning of typified buildings 82 5.4.5 Representation of typified buildings 83 5.4.6 Resizing Newly Typified Buildings 85 5.5 Experiments 86 5.5.1 Data derivation 86 5.5.2 Typification results and evaluation 87 5.5.3 Comparison with official map 91 5.6 Discussion 92 5.6.1 Advantages 92 5.6.2 Further improvements 93 5.7 Conclusion 94 Chapter 6 Hierarchical extraction of skeleton structures from discrete buildings 95 6.1 Abstract 96 6.2 Introduction 96 6.3 Related work 97 6.4 Study area 99 6.5 Hierarchical extraction of skeleton structures 100 6.5.1 Proximity Graph Network (PGN) of buildings 100 6.5.2 Centrality analysis of proximity graph network 103 6.5.3 Hierarchical skeleton structures of buildings 108 6.6 Generalization application 111 6.7 Experiment and discussion 114 6.7.1 Data statement 114 6.7.2 Experimental results 115 6.7.3 Discussion 118 6.8 Conclusions 120 Chapter 7 Discussion 121 7.1 Revisiting the research problems 121 7.2 Evaluation of the presented methodology 123 7.2.1 Strengths 123 7.2.2 Limitations 125 Chapter 8 Conclusions 127 8.1 Main contributions 127 8.2 Outlook 128 8.3 Final thoughts 131 Bibliography 132 Acknowledgements 142 Publications 14