There are currently over 138, 000 known macromolecular structures deposited in the wwPDB (Worldwide Protein Data Bank) database. While all the macromolecular structure files contain information about a particular structure, the collection of these files also allows combining the macromolecular structures to obtain statistical information about macromolecules in general. This fact has been the basis for many structural biology methods including the molecular replacement method used in X-ray crystallography or homologous structure restraints in the refinement methods. With the success of methods based on prior information, it is feasible that novel methods could be developed and current methods improved using further prior information; more specifically, by using the structure density-map shape similarity instead of sequence or model similarity. Therefore, this project introduces a mathematical framework for computing three different measures of macromolecular three-dimensional shape similarity and demonstrates how these descriptors can be applied in symmetry detection and protein-domain clustering. The ability to detect cyclic (C), dihedral (D), tetrahedral (T), octahedral (O) and icosahedral (I) symmetry groups as well as computing all associated symmetry elements has direct applications in map averaging and reducing the storage requirements by storing only the asymmetric information. Moreover, by having the capacity to find structures with similar shape, it was possible to reduce the size of the BALBES protein domain database by more than 18.7% and thus achieve proportional speed-up in the searching parts of its applications. Finally, the development of the method described in this project has many possible applications throughout structural biology. The method could, for example, facilitate matching and fitting of protein domains into the density maps produced by the electron-microscopy techniques, or it could allow for molecular-replacement candidate search using shape instead of sequence similarity. To allow for the development of any further applications, software for applying the methods described here is also presented and released for the community.

Sound source localization depends on the auditory system to identify, recognize, and segregate elements of salient sources over distracting noise. My research investigates sensory mechanisms involved in these auditory processing tasks of an insect hearing specialist, to isolate individual sound sources of interest over noise. I first developed quantitative methods to determine signal features that the acoustic parasitoid fly Ormia ochracea (Diptera: Tachinidae) evaluate for host cricket song recognition. With flies subjected to a no-choice paradigm and forced to track a switch in the broadcast location of test songs, I describe several response features (distance, steering velocity, and angular orientation) that vary with song pulse rate preferences. I incorporate these response measures in a phonotaxis performance index that is sensitive to capturing response variation that may underlie song recognition. I demonstrate that Floridian O. ochracea exhibit phonotaxis to a combination of pulse durations and interpulse intervals that combine to a range of accepted pulse periods. Under complex acoustic conditions of multiple coherent cricket songs that overlap in time and space, O. ochracea may experience a phantom source illusion and localize a direction between actual source locations. By varying the temporal overlap between competing sources, I demonstrate that O. ochracea are able to resolve this illusion via the precedence effect: exploitation of small time differences between competing sources to selectively localize the leading over lagging sources. An increase in spatial separation between cricket song and masking noise does not reduce song detection thresholds nor improve song localization accuracy. Instead, walking responses are diverted away from both song and noise. My findings support the idea that the ears of O. ochracea function as bilateral symmetry detectors to balance sound intensity, sound arrive time differences, and temporal pattern input to both sides of the auditory system. Asymmetric acoustic input result in corrective turning behaviour to re-establish balance for successful source localization.

Sound source localization depends on the auditory system to identify, recognize, and segregate elements of salient sources over distracting noise. My research investigates sensory mechanisms involved in these auditory processing tasks of an insect hearing specialist, to isolate individual sound sources of interest over noise. I first developed quantitative methods to determine signal features that the acoustic parasitoid fly Ormia ochracea (Diptera: Tachinidae) evaluate for host cricket song recognition. With flies subjected to a no-choice paradigm and forced to track a switch in the broadcast location of test songs, I describe several response features (distance, steering velocity, and angular orientation) that vary with song pulse rate preferences. I incorporate these response measures in a phonotaxis performance index that is sensitive to capturing response variation that may underlie song recognition. I demonstrate that Floridian O. ochracea exhibit phonotaxis to a combination of pulse durations and interpulse intervals that combine to a range of accepted pulse periods. Under complex acoustic conditions of multiple coherent cricket songs that overlap in time and space, O. ochracea may experience a phantom source illusion and localize a direction between actual source locations. By varying the temporal overlap between competing sources, I demonstrate that O. ochracea are able to resolve this illusion via the precedence effect: exploitation of small time differences between competing sources to selectively localize the leading over lagging sources. An increase in spatial separation between cricket song and masking noise does not reduce song detection thresholds nor improve song localization accuracy. Instead, walking responses are diverted away from both song and noise. My findings support the idea that the ears of O. ochracea function as bilateral symmetry detectors to balance sound intensity, sound arrive time differences, and temporal pattern input to both sides of the auditory system. Asymmetric acoustic input result in corrective turning behaviour to re-establish balance for successful source localization.

Scalar fields are used to represent physical quantities measured over a domain of interest. Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights into the properties of the underlying phenomenon. This thesis proposes three methods to detect symmetry in scalar fields. The first method models symmetry detection as a subtree matching problem in the contour tree, which is a topological graph abstraction of the scalar field. The contour tree induces a hierarchical segmentation of features at different scales and hence this method can detect symmetry at different scales. The second method identifies symmetry by comparing distances between extrema from each symmetric region. The distance is computed robustly using a topological abstraction called the extremum graph. Hence, this method can detect symmetry even in the presence of significant noise. The above methods compare pairs of regions to identify symmetry instead of grouping the entire set of symmetric regions as a cluster. This motivates the third method which uses a clustering analysis for symmetry detection. In this method, the contours of a scalar field are mapped to points in a high-dimensional descriptor space such that points corresponding to similar contours lie in close proximity to each other. Symmetry is identified by clustering the points in the descriptor space. We show through experiments on real world data sets that these methods are robust in the presence of noise and can detect symmetry under different types of transformations. Extraction of symmetry information helps users in visualization and data analysis. We design novel applications that use symmetry information to enhance visualization of scalar field data and to facilitate their exploration.

This thesis is concerned with the utilization of object symmety as a cue for segmentation and object recognition. In particular it investigates the problem of detecting 3D bilaterally symmetric objects from affine views. The first part of the thesis investigates the problem of detecting 3D bilateral symmetry within a scene from known point correspondences across two or more affine views. We begin by extending the notion of skewed symmetry to three dimensions, and give a definition in terms of degenerate structure that applies equally to an affine 3D structure or to point correspondences across two or more affine views. We then consider the effects of measurement errors on symmetry detection, and derive an optimal statistical test of degenerate structure, and thereby of 3D-skewed symmetry. We then move on to the problem of searching for 3D skewed symmetric sets within a larger scene. We discuss two approaches to the problem, both of which we have implemented, and we demonstrate fully automatic detection of 3D skewed symmetry on images of uncluttered scenes. We conclude the first part by investing means of verifying the presence of bilateral rather than skewed symmetry in the Euclidean space, by enforcing mutual consistency between multiple skewed symmetric sets, and by drawing on partial knowledge about the camera calibration. The second part of the thesis is concerned with the problem of obtaining feature correspondences across multiple affine views, as required for the detection of symmetry. In particular we investigate the geometric matching constraints that exist between affine views. We start by specilizing the four projective multifocal tensors to the affine case, and use these to carry the bulk of all known projective multi-view matching relations to affine views, unearthing some new relations in the process. Having done that, we address the problem of estimating the affine tensors. We provide a minimal set of constraints on the affine trifocal tensor, and search for ways of estimating the affine tensors from point and line correspondences.

This thesis aims to present a method for detection of faults (burn marks) on insulator using only image processing algorithms. It is accomplished by extracting the insulator from the background image and then detecting the burn marks on the segmented image. Apart from several other challenges encountered during the detection phase, the main challenge was to eliminate the connector marks which might be detected as burn-marks. The technique discussed in this thesis work is one of a kind and not much research has been done in areas of burn mark detection on the insulator surface. Several algorithms have been pondered upon before coming up with a set of algorithms applied in a particular manner. The first phase of the work emphasizes on detection of the insulator from the image. Apart from pre-processing and other segmentation techniques, Symmetry detection and adaptive GrabCut are the main algorithms used for this purpose. Efficient and powerful algorithms such as feature detection and matching were considered before arriving at this method, based on pros and cons. The second phase is the detection of burn marks on the extracted image while eliminating the connector marks. Algorithms such as Blob detection and Contour detection, adapted in a particular manner, have been used for this purpose based on references from medical image processing. The elimination of connector marks is obtained by applying a set of mathematical calculations. The entire project is implemented in Visual Studio using OpenCV libraries. Result obtained is cross-validated across an image data set.

The goal of General Game Playing (GGP) is to develop a system, that is able to automatically play previously unseen games well, solely by being given the rules of the game. In contrast to traditional game playing programs, a general game player cannot be given game specific knowledge. Instead, the program has to discover this knowledge and use it for effectively playing the game well without human intervention. In this thesis, we present a such a program and general methods that solve a variety of knowledge discovery problems in GGP. Our main contributions are methods for the automatic construction of heuristic evaluation functions, the automated discovery of game structures, a system for proving properties of games, and symmetry detection and exploitation for general games.

The goal of General Game Playing (GGP) is to develop a system, that is able to automatically play previously unseen games well, solely by being given the rules of the game. In contrast to traditional game playing programs, a general game player cannot be given game specific knowledge. Instead, the program has to discover this knowledge and use it for effectively playing the game well without human intervention. In this thesis, we present a such a program and general methods that solve a variety of knowledge discovery problems in GGP. Our main contributions are methods for the automatic construction of heuristic evaluation functions, the automated discovery of game structures, a system for proving properties of games, and symmetry detection and exploitation for general games.:1. Introduction 2. Preliminaries 3. Components of Fluxplayer 4. Game Tree Search 5. Generating State Evaluation Functions 6. Distance Estimates for Fluents and States 7. Proving Properties of Games 8. Symmetry Detection 9. Related Work 10. Discussion

La symétrie est une propriété géométrique importante en perception visuelle qui traduit notre perception des correspondances entre les différents objets ou formes présents dans une scène. Elle est utilisée comme élément caractéristique dans de nombreuses applications de la vision par ordinateur (comme par exemple la détection, la segmentation ou la reconnaissance d'objets) mais également comme une caractéristique formelle en sciences de l'art (ou en analyse esthétique). D’importants progrès ont été réalisés ces dernières décennies pour la détection de la symétrie dans les images mais il reste encore de nombreux verrous à lever. Dans cette thèse, nous nous intéressons à la détection des symétries de réflexion, dans des images réelles, à l'échelle globale. Nos principales contributions concernent les étapes d'extraction de caractéristiques et de représentation globale des axes de symétrie. Nous proposons d'abord une nouvelle méthode d'extraction de segments de contours à l'aide de bancs de filtres de Gabor logarithmiques et une mesure de symétrie intersegments basée sur des caractéristiques locales de forme, de texture et de couleur. Cette méthode a remporté la première place à la dernière compétition internationale de symétrie pour la détection mono- et multi-axes. Notre deuxième contribution concerne une nouvelle méthode de représentation des axes de symétrie dans un espace linéaire-directionnel. Les propriétés de symétrie sont représentées sous la forme d'une densité de probabilité qui peut être estimée, de manière non-paramétrique, par une méthode à noyauxbasée sur la distribution de Von Mises-Fisher. Nous montrons que la détection des axes dominants peut ensuite être réalisée à partir d'un algorithme de type "mean-shift” associé à une distance adaptée. Nous introduisons également une nouvelle base d'images pour la détection de symétrie mono-axe dans des photographies professionnelles issue de la base à grande échelle AVA (Aestetic Visual Analysis). Nos différentes contributions obtiennent des résultats meilleurs que les algorithmes de l'état de l'art, évalués sur toutes les bases disponibles publiquement, spécialement dans le cas multi-axes. Nous concluons que les propriétés de symétrie peuvent être utilisées comme des caractéristiques visuelles de niveau sémantique intermédiaire pour l'analyse et la compréhension de photographies. / Symmetry is a fundamental principle of the visual perception to feel the equally distributed weights within foreground objects inside an image. It is used as a significant visual feature through various computer vision applications (i.e. object detection and segmentation), plus as an important composition measure in art domain (i.e. aesthetic analysis). The development of symmetry detection has been improved rapidly since last century. In this thesis, we mainly aim to propose new approaches to detect reflection symmetry inside real-world images in a global scale. In particular, our main contributions concern feature extraction and globalrepresentation of symmetry axes. First, we propose a novel approach that detects global salient edges inside an image using Log-Gabor filter banks, and defines symmetry oriented similarity through textural and color around these edges. This method wins a recent symmetry competition worldwide in single and multiple cases.Second, we introduce a weighted kernel density estimator to represent linear and directional symmetrical candidates in a continuous way, then propose a joint Gaussian-vonMises distance inside the mean-shift algorithm, to select the relevant symmetry axis candidates along side with their symmetrical densities. In addition, we introduce a new challenging dataset of single symmetry axes inside artistic photographies extracted from the large-scale Aesthetic Visual Analysis (AVA) dataset. The proposed contributions obtain superior results against state-of-art algorithms among all public datasets, especially multiple cases in a global scale. We conclude that the spatial and context information of each candidate axis inside an image can be used as a local or global symmetry measure for further image analysis and scene understanding purposes.