Methods for Structural Pattern Recognition: Complexity and Applications

Katedra kybernetik

Entwurf und Implementation einer auf Graph-Grammatiken beruhenden Sprache zur Funktions-Struktur-Modellierung von Pflanzen

Increasing biological knowledge requires more and more elaborate methods to translate the knowledge into executable model descriptions, and increasing computational power allows to actually execute these descriptions. Such a simulation helps to validate, extend and question the knowledge. For plant modelling, the well-established formal description language of Lindenmayer systems reaches its limits as a method to concisely represent current knowledge and to conveniently assist in current research. On one hand, it is well-suited to represent structural and geometric aspects of plant models - of which units is a plant composed, how are these connected, what is their location in 3D space -, but on the other hand, its usage to describe functional aspects - what internal processes take place in the plant structure, how does this interact with the structure - is not as convenient as desirable. This can be traced back to the underlying representation of structure as a linear chain of units, while the intrinsic nature of the structure is a tree or even a graph. Therefore, we propose to use graphs and graph grammars as a basis for plant modelling which combines structural and functional aspects. In the first part of this thesis, we develop the necessary theoretical framework. Starting with a presentation of the state of the art concerning Lindenmayer systems and graph grammars, we develop the formalism of relational growth grammars as a variant of graph grammars. We show that this formalism has a natural embedding of Lindenmayer systems which keeps all relevant properties, but represents branched structures directly as axial trees and not as linear chains with indirect encoding of branches. In the second part, we develop the main practical result, the XL programming language as an extension of the Java programming language by very general rule-based features. Short examples illustrate the application of the new language features. We describe the built-in pattern matching algorithm of the implemented run-time system for the XL programming language, and we sketch a possible implementation of an XL compiler. The third part is an application of relational growth grammars and the XL programming language. We show how the general XL interfaces can be customized for relational growth grammars. On top of this customization, several examples from a variety of disciplines demonstrate the usefulness of the developed formalism and language to describe plant growth, especially functional-structural plant models, but also artificial life, architecture or interactive games. Some examples operate on custom graphs like XML DOM trees or scene graphs of commercial 3D modellers, while the majority uses the 3D modelling platform GroIMP, a software developed in conjunction with this thesis. The appendix gives an overview of the GroIMP software. The practical usage of its plug-in for relational growth grammars is also illustrated.Das zunehmende Wissen über biologische Prozesse verlangt nach geeigneten Methoden, es in ausführbare Modelle zu übersetzen, und die zunehmende Rechenleistung der Computer ermöglicht es, diese Modelle auch tatsächlich auszuführen. Solche Simulationen dienen zur Validierung, Erweiterung und Hinterfragung des Wissens. Speziell für die Pflanzenmodellierung wurden Lindenmayer-Systeme mit Erfolg eingesetzt, jedoch stoßen diese bei aktuellen Modellierungsproblemen und Forschungsvorhaben an ihre Grenzen. Zwar sind sie gut geeignet, Pflanzenstruktur und Geometrie abzubilden - aus welchen Einheiten setzt sich eine Pflanze zusammen, wie sind diese verbunden, wie ist ihre räumliche Lage -, aber die lineare Datenstruktur erschwert die Integration von Funktionsmodellen, welche Prozesse innerhalb der verzweigten Struktur und des beanspruchten Raumes beschreiben. Daher wird in dieser Arbeit vorgeschlagen, anstelle der linearen Stuktur Graphen und Graph-Grammatiken als Grundlage für die kombinierte Funktions-Struktur-Modellierung von Pflanzen zu verwenden. Im ersten Teil der Dissertation wird der theoretische Unterbau entwickelt. Nach einer Vorstellung des aktuellen Wissensstandes auf dem Gebiet der Lindenmayer-Systeme und Graph-Grammatiken werden relationale Wachstumsgrammatiken eingeführt, die auf bekannten Mechanismen für parallele Graph-Grammatiken aufbauen und Lindenmayer-Systeme als Spezialfall enthalten, dabei jedoch verzweigte Strukturen direkt als axiale Bäume darstellen. Zur praktischen Anwendung wird im zweiten Teil die Programmiersprache XL entwickelt, die Java um allgemein gehaltene Sprachkonstrukte für Graph-Grammatiken erweitert. Kurze Beispiele zeigen die Anwendung der neuen Sprachmerkmale. Der Algorithmus zur Mustersuche wird erläutert, und die Implementation des XL-Compilers wird vorgestellt. Im dritten Teil werden mögliche Anwendungen relationaler Wachstumsgrammatiken aufgezeigt. Dazu werden zunächst die allgemeinen XL-Schnittstellen für relationale Wachstumsgrammatiken konkretisiert, um dieses System dann für Modelle aus verschiedenen Bereichen zu nutzen, darunter Funktions-Struktur-Modelle von Pflanzen, Künstliches Leben, Architektur und interaktive Spiele. Einige Beispiele nutzen spezifische Graphen wie XML-DOM-Bäume oder Szenengraphen kommerzieller 3D-Modellierprogramme, aber der überwiegende Teil baut auf der 3D-Plattform GroIMP auf, die zusammen mit dieser Dissertation entwickelt wurde. Im Anhang wird die Software GroIMP kurz vorgestellt und ihre praktische Anwendung für relationale Wachstumsgrammatiken erläutert

A Semantics-Based Approach to Optimizing Unstructured Mesh Abstractions

Computational scientists are frequently confronted with a choice: implement algorithms using high-level abstractions, such as matrices and mesh entities, for greater programming productivity or code them using low-level language constructs for greater execution efficiency. We have observed that the cost of implementing a representative unstructured mesh code with high-level abstractions is poor computational intensity---the ratio of floating point operations to memory accesses. Related scientific applications frequently produce little ``science per cycle'' because their abstractions both introduce additional overhead and hinder compiler analysis and subsequent optimization. Our work exploits the semantics of abstractions, as employed in unstructured mesh codes, to overcome these limitations and to guide a series of manual, domain-specific optimizations that significantly improve computational intensity. We propose a framework for the automation of such high-level optimizations within the ROSE source-to-source compiler infrastructure. The specification of optimizations is left to domain experts and library writers who best understand the semantics of their applications and libraries and who are thus best poised to describe their optimization. Our source-to-source approach translates different constructs (e.g., C code written in a procedural style or C++ code written in an object-oriented style) to a procedural form in order to simplify the specification of optimizations. This is accomplished through raising operators, which are specified by a domain expert and are used to project a concrete application from an implementation space to an abstraction space, where optimizations are applied. The transformed code in the abstraction space is then reified as a concrete implementation via lowering operators, which are automatically inferred by inverting the raising operators. Applying optimizations within the abstraction space, rather than the implementation space, leads to greater optimization portability. We use this framework to automate two high-level optimizations. The first uses an inspector/executor approach to avoid costly and redundant traversals of a static mesh by memoizing the relatively few references required to perform the mathematical computations. During the executor phase, the stored entities are accessed directly without resort to the indirection inherent in the original traversal. The second optimization lowers an object-oriented mesh framework, which uses C++ objects to access the mesh and iterate over mesh entities, to a low-level implementation, which uses integer-based access and iteration.Support was provided by a DOE High-Performance Computer Science Fellowship administered by The Krell Institute, Ames, IA

A domain independent adaptive imaging system for visual inspection

Computer vision is a rapidly growing area. The range of applications is increasing very quickly, robotics, inspection, medicine, physics and document processing are all computer vision applications still in their infancy. All these applications are written with a specific task in mind and do not perform well unless there under a controlled environment. They do not deploy any knowledge to produce a meaningful description of the scene, or indeed aid in the analysis of the image. The construction of a symbolic description of a scene from a digitised image is a difficult problem. A symbolic interpretation of an image can be viewed as a mapping from the image pixels to an identification of the semantically relevant objects. Before symbolic reasoning can take place image processing and segmentation routines must produce the relevant information. This part of the imaging system inherently introduces many errors. The aim of this project is to reduce the error rate produced by such algorithms and make them adaptable to change in the manufacturing process. Thus a prior knowledge is needed about the image and the objects they contain as well as knowledge about how the image was acquired from the scene (image geometry, quality, object decomposition, lighting conditions etc,). Knowledge on algorithms must also be acquired. Such knowledge is collected by studying the algorithms and deciding in which areas of image analysis they work well in. In most existing image analysis systems, knowledge of this kind is implicitly embedded into the algorithms employed in the system. Such an approach assumes that all these parameters are invariant. However, in complex applications this may not be the case, so that adjustment must be made from time to time to ensure a satisfactory performance of the system. A system that allows for such adjustments to be made, must comprise the explicit representation of the knowledge utilised in the image analysis procedure. In addition to the use of a priori knowledge, rules are employed to improve the performance of the image processing and segmentation algorithms. These rules considerably enhance the correctness of the segmentation process. The most frequently given goal, if not the only one in industrial image analysis is to detect and locate objects of a given type in the image. That is, an image may contain objects of different types, and the goal is to identify parts of the image. The system developed here is driven by these goals, and thus by teaching the system a new object or fault in an object the system may adapt the algorithms to detect these new objects as well compromise for changes in the environment such as a change in lighting conditions. We have called this system the Visual Planner, this is due to the fact that we use techniques based on planning to achieve a given goal. As the Visual Planner learns the specific domain it is working in, appropriate algorithms are selected to segment the object. This makes the system domain independent, because different algorithms may be selected for different applications and objects under different environmental condition

Aspects of multi-resolutional foveal images for robot vision

Imperial Users onl

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

3D Reconstruction of Building Rooftop and Power Line Models in Right-of-Ways Using Airborne LiDAR Data

The research objectives aimed to achieve thorough the thesis are to develop methods for reconstructing models of building and PL objects of interest in the power line (PL) corridor area from airborne LiDAR data. For this, it is mainly concerned with the model selection problem for which model is more optimal in representing the given data set. This means that the parametric relations and geometry of object shapes are unknowns and optimally determined by the verification of hypothetical models. Therefore, the proposed method achieves high adaptability to the complex geometric forms of building and PL objects. For the building modeling, the method of implicit geometric regularization is proposed to rectify noisy building outline vectors which are due to noisy data. A cost function for the regularization process is designed based on Minimum Description Length (MDL) theory, which favours smaller deviation between a model and observation as well as orthogonal and parallel properties between polylines. Next, a new approach, called Piecewise Model Growing (PMG), is proposed for 3D PL model reconstruction using a catenary curve model. It piece-wisely grows to capture all PL points of interest and thus produces a full PL 3D model. However, the proposed method is limited to the PL scene complexity, which causes PL modeling errors such as partial, under- and over-modeling errors. To correct the incompletion of PL models, the inner and across span analysis are carried out, which leads to replace erroneous PL segments by precise PL models. The inner span analysis is performed based on the MDL theory to correct under- and over-modeling errors. The across span analysis is subsequently carried out to correct partial-modeling errors by finding start and end positions of PLs which denotes Point Of Attachment (POA). As a result, this thesis addresses not only geometrically describing building and PL objects but also dealing with noisy data which causes the incompletion of models. In the practical aspects, the results of building and PL modeling should be essential to effectively analyze a PL scene and quickly alleviate the potentially hazardous scenarios jeopardizing the PL system