Deformable meshes for shape recovery: models and applications

With the advance of scanning and imaging technology, more and more 3D objects become available. Among them, deformable objects have gained increasing interests. They include medical instances such as organs, a sequence of objects in motion, and objects of similar shapes where a meaningful correspondence can be established between each other. Thus, it requires tools to store, compare, and retrieve them. Many of these operations depend on successful shape recovery. Shape recovery is the task to retrieve an object from the environment where its geometry is hidden or implicitly known. As a simple and versatile tool, mesh is widely used in computer graphics for modelling and visualization. In particular, deformable meshes are meshes which can take the deformation of deformable objects. They extend the modelling ability of meshes. This dissertation focuses on using deformable meshes to approach the 3D shape recovery problem. Several models are presented to solve the challenges for shape recovery under different circumstances. When the object is hidden in an image, a PDE deformable model is designed to extract its surface shape. The algorithm uses a mesh representation so that it can model any non-smooth surface with an arbitrary precision compared to a parametric model. It is more computational efficient than a level-set approach. When the explicit geometry of the object is known but is hidden in a bank of shapes, we simplify the deformation of the model to a graph matching procedure through a hierarchical surface abstraction approach. The framework is used for shape matching and retrieval. This idea is further extended to retain the explicit geometry during the abstraction. A novel motion abstraction framework for deformable meshes is devised based on clustering of local transformations and is successfully applied to 3D motion compression

Shape Retrieval Methods for Architectural 3D Models

This thesis introduces new methods for content-based retrieval of architecture-related 3D models. We thereby consider two different overall types of architectural 3D models. The first type consists of context objects that are used for detailed design and decoration of 3D building model drafts. This includes e.g. furnishing for interior design or barriers and fences for forming the exterior environment. The second type consists of actual building models. To enable efficient content-based retrieval for both model types that is tailored to the user requirements of the architectural domain, type-specific algorithms must be developed. On the one hand, context objects like furnishing that provide similar functions (e.g. seating furniture) often share a similar shape. Nevertheless they might be considered to belong to different object classes from an architectural point of view (e.g. armchair, elbow chair, swivel chair). The differentiation is due to small geometric details and is sometimes only obvious to an expert from the domain. Building models on the other hand are often distinguished according to the underlying floor- and room plans. Topological floor plan properties for example serve as a starting point for telling apart residential and commercial buildings. The first contribution of this thesis is a new meta descriptor for 3D retrieval that combines different types of local shape descriptors using a supervised learning approach. The approach enables the differentiation of object classes according to small geometric details and at the same time integrates expert knowledge from the field of architecture. We evaluate our approach using a database containing arbitrary 3D models as well as on one that only consists of models from the architectural domain. We then further extend our approach by adding a sophisticated shape descriptor localization strategy. Additionally, we exploit knowledge about the spatial relationship of object components to further enhance the retrieval performance. In the second part of the thesis we introduce attributed room connectivity graphs (RCGs) as a means to characterize a 3D building model according to the structure of its underlying floor plans. We first describe how RCGs are inferred from a given building model and discuss how substructures of this graph can be queried efficiently. We then introduce a new descriptor denoted as Bag-of-Attributed-Subgraphs that transforms attributed graphs into a vector-based representation using subgraph embeddings. We finally evaluate the retrieval performance of this new method on a database consisting of building models with different floor plan types. All methods presented in this thesis are aimed at an as automated as possible workflow for indexing and retrieval such that only minimum human interaction is required. Accordingly, only polygon soups are required as inputs which do not need to be manually repaired or structured. Human effort is only needed for offline groundtruth generation to enable supervised learning and for providing information about the orientation of building models and the unit of measurement used for modeling

A Methodology for Extracting Human Bodies from Still Images

Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia