Neural network-based shape retrieval using moment invariants and Zernike moments.

Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Never a \u27needless\u27 suicide: An empirical test of Shneidman\u27s theory of psychological needs, psychological pain, and suicidality (Edwin Shneidman).

The phenomenology of suicidal thoughts and behaviour has been an area of increased interest in recent years. One particular area of focus is psychological pain, or psychache. In this dissertation, Edwin Shneidman\u27s psychological theory of suicide was studied. Shneidman has theorized that psychological needs are associated with the development of psychological pain, which in turn leads to suicide as an escape from pain. Two hundred and fifty-seven undergraduate students completed the Personality Research Form, the Psychache Scale, the Orbach and Mikulincer Mental Pain Scale, two items from Shneidman\u27s Psychological Pain Assessment Scale, as well as demographic and suicide history items. Measures of psychological pain demonstrated convergent validity. Low need for affiliation and high impulsivity were significantly related to psychological pain. All measures of psychological pain were associated with suicidal ideation and history of suicide attempts. Possible gender differences emerged. This study provides some evidence for Shneidman\u27s theory, although not all identified needs were supported. The importance of understanding the role of psychological pain in the phenomenology of suicidal thinking and behaviour is emphasized.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .D375. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6267. Thesis (Ph.D.)--University of Windsor (Canada), 2005

A Location-Aware Middleware Framework for Collaborative Visual Information Discovery and Retrieval

This work addresses the problem of scalable location-aware distributed indexing to enable the leveraging of collaborative effort for the construction and maintenance of world-scale visual maps and models which could support numerous activities including navigation, visual localization, persistent surveillance, structure from motion, and hazard or disaster detection. Current distributed approaches to mapping and modeling fail to incorporate global geospatial addressing and are limited in their functionality to customize search. Our solution is a peer-to-peer middleware framework based on XOR distance routing which employs a Hilbert Space curve addressing scheme in a novel distributed geographic index. This allows for a universal addressing scheme supporting publish and search in dynamic environments while ensuring global availability of the model and scalability with respect to geographic size and number of users. The framework is evaluated using large-scale network simulations and a search application that supports visual navigation in real-world experiments

Towards Learning Representations in Visual Computing Tasks

abstract: The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss. In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Loop closure for topological mapping and navigation with omnidirectional images

Dans le cadre de la robotique mobile, des progrès significatifs ont été obtenus au cours des trois dernières décennies pour la cartographie et la localisation. La plupart des projets de recherche traitent du problème de SLAM métrique. Les techniques alors développées sont sensibles aux erreurs liées à la dérive ce qui restreint leur utilisation à des environnements de petite échelle. Dans des environnements de grande taille, l utilisation de cartes topologiques, qui sont indépendantes de l information métrique, se présentent comme une alternative aux approches métriques.Cette thèse porte principalement sur le problème de la construction de cartes topologiques pour la navigation de robots mobiles dans des environnements urbains de grande taille, en utilisant des caméras omnidirectionnelles. La principale contribution de cette thèse est la résolution efficace et avec précision du problème de fermeture de boucles, problème qui est au coeur de tout algorithme de cartographie topologique. Le cadre de cartographie topologique éparse / hiérarchique proposé allie une approche de partionnement de séquence d images (ISP) par regroupement des images visuellement similaires dans un noeud avec une approche de détection de fermeture de boucles permettant de connecter ces noeux. Le graphe topologique alors obtenu représente l environnement du robot. L algorithme de fermeture de boucle hiérarchique développé permet d extraire dans un premier temps les noeuds semblables puis, dans un second temps, l image la plus similaire. Cette détection de fermeture de boucles hiérarchique est rendue efficace par le stockage du contenu des cartes éparses sous la forme d une structure de données d indexation appelée fichier inversé hiérarchique (HIF). Nous proposons de combiner le score de pondération TFIDF avec des contraintes spatiales et la fréquence des amers détectés pour obtenir une meilleur robustesse de la fermeture de boucles. Les résultats en terme de densité et précision des cartes obtenues et d efficacité sont évaluées et comparées aux résultats obtenus avec des approches de l état de l art sur des séquences d images omnidirectionnelles acquises en milieu extérieur. Au niveau de la précision des détections de boucles, des résultats similaires ont été observés vis-à-vis des autres approches mais sans étape de vérification utilisant la géométrie épipolaire. Bien qu efficace, l approche basée sur HIF présente des inconvénients comme la faible densité des cartes et le faible taux de détection des boucles. Une seconde technique de fermeture de boucle a alors été développée pour combler ces lacunes. Le problème de la faible densité des cartes est causé par un sur-partionnement de la séquence d images. Celui-ci est résolu en utilisant des vecteurs de descripteurs agrégés localement (VLAD) lors de l étape de ISP. Une mesure de similarité basée sur une contrainte spatiale spécifique à la structure des images omnidirectionnelles a également été développée. Des résultats plus précis sont obtenus, même en présence de peu d appariements. Les taux de réussite sont meilleurs qu avec FABMAP 2.0, la méthode la plus utilisée actuellement, sans étape supplémentaire de vérification géométrique.L environnement est souvent supposé invariant au cours du temps : la carte de l environnement est construite lors d une phase d apprentissage puis n est pas modifiée ensuite. Une gestion de la mémoire à long terme est nécessaire pour prendre en compte les modifications dans l environnement au cours du temps. La deuxième contribution de cette thèse est la formulation d une approche de gestion de la mémoire visuelle à long terme qui peut être utilisée dans le cadre de cartes visuelles topologiques et métriques. Les premiers résultats obtenus sont encourageants. (...)Over the last three decades, research in mobile robotic mapping and localization has seen significant progress. However, most of the research projects these problems into the SLAM framework while trying to map and localize metrically. As metrical mapping techniques are vulnerable to errors caused by drift, their ability to produce consistent maps is limited to small scale environments. Consequently, topological mapping approaches which are independent of metrical information stand as an alternative to metrical approaches in large scale environments. This thesis mainly deals with the loop closure problem which is the crux of any topological mapping algorithm. Our main aim is to solve the loop closure problem efficiently and accurately using an omnidirectional imaging sensor. Sparse topological maps can be built by representing groups of visually similar images of a sequence as nodes of a topological graph. We propose a sparse / hierarchical topological mapping framework which uses Image Sequence Partitioning (ISP) to group visually similar images of a sequence as nodes which are then connected on occurrence of loop closures to form a topological graph. A hierarchical loop closure algorithm that can first retrieve the similar nodes and then perform an image similarity analysis on the retrieved nodes is used. An indexing data structure called Hierarchical Inverted File (HIF) is proposed to store the sparse maps to facilitate an efficient hierarchical loop closure. TFIDF weighting is combined with spatial and frequency constraints on the detected features for improved loop closure robustness. Sparsity, efficiency and accuracy of the resulting maps are evaluated and compared to that of the other two existing techniques on publicly available outdoor omni-directional image sequences. Modest loop closure recall rates have been observed without using the epi-polar geometry verification step common in other approaches. Although efficient, the HIF based approach has certain disadvantages like low sparsity of maps and low recall rate of loop closure. To address these shortcomings, another loop closure technique using spatial constraint based similarity measure on omnidirectional images has been proposed. The low sparsity of maps caused by over-partitioning of the input sequence has been overcome by using Vector of Locally Aggregated Descriptors (VLAD) for ISP. Poor resolution of the omnidirectional images causes fewer feature matches in image pairs resulting in reduced recall rates. A spatial constraint exploiting the omnidirectional image structure is used for feature matching which gives accurate results even with fewer feature matches. Recall rates better than the contemporary FABMAP 2.0 approach have been observed without the additional geometric verification. The second contribution of this thesis is the formulation of a visual memory management approach suitable for long term operability of mobile robots. The formulated approach is suitable for both topological and metrical visual maps. Initial results which demonstrate the capabilities of this approach have been provided. Finally, a detailed description of the acquisition and construction of our multi-sensor dataset is provided. The aim of this dataset is to serve the researchers working in the mobile robotics and vision communities for evaluating applications like visual SLAM, mapping and visual odometry. This is the first dataset with omnidirectional images acquired on a car-like vehicle driven along a trajectory with multiple loops. The dataset consists of 6 sequences with data from 11 sensors including 7 cameras, stretching 18 kilometers in a semi-urban environmental setting with complete and precise ground-truth.CLERMONT FD-Bib.électronique (631139902) / SudocSudocFranceF

Recuperação de informação multimodal em repositórios de imagem médica

The proliferation of digital medical imaging modalities in hospitals and other diagnostic facilities has created huge repositories of valuable data, often not fully explored. Moreover, the past few years show a growing trend of data production. As such, studying new ways to index, process and retrieve medical images becomes an important subject to be addressed by the wider community of radiologists, scientists and engineers. Content-based image retrieval, which encompasses various methods, can exploit the visual information of a medical imaging archive, and is known to be beneficial to practitioners and researchers. However, the integration of the latest systems for medical image retrieval into clinical workflows is still rare, and their effectiveness still show room for improvement. This thesis proposes solutions and methods for multimodal information retrieval, in the context of medical imaging repositories. The major contributions are a search engine for medical imaging studies supporting multimodal queries in an extensible archive; a framework for automated labeling of medical images for content discovery; and an assessment and proposal of feature learning techniques for concept detection from medical images, exhibiting greater potential than feature extraction algorithms that were pertinently used in similar tasks. These contributions, each in their own dimension, seek to narrow the scientific and technical gap towards the development and adoption of novel multimodal medical image retrieval systems, to ultimately become part of the workflows of medical practitioners, teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais, clínicas e outros centros de diagnóstico, levou à criação de enormes repositórios de dados, frequentemente não explorados na sua totalidade. Além disso, os últimos anos revelam, claramente, uma tendência para o crescimento da produção de dados. Portanto, torna-se importante estudar novas maneiras de indexar, processar e recuperar imagens médicas, por parte da comunidade alargada de radiologistas, cientistas e engenheiros. A recuperação de imagens baseada em conteúdo, que envolve uma grande variedade de métodos, permite a exploração da informação visual num arquivo de imagem médica, o que traz benefícios para os médicos e investigadores. Contudo, a integração destas soluções nos fluxos de trabalho é ainda rara e a eficácia dos mais recentes sistemas de recuperação de imagem médica pode ser melhorada. A presente tese propõe soluções e métodos para recuperação de informação multimodal, no contexto de repositórios de imagem médica. As contribuições principais são as seguintes: um motor de pesquisa para estudos de imagem médica com suporte a pesquisas multimodais num arquivo extensível; uma estrutura para a anotação automática de imagens; e uma avaliação e proposta de técnicas de representation learning para deteção automática de conceitos em imagens médicas, exibindo maior potencial do que as técnicas de extração de features visuais outrora pertinentes em tarefas semelhantes. Estas contribuições procuram reduzir as dificuldades técnicas e científicas para o desenvolvimento e adoção de sistemas modernos de recuperação de imagem médica multimodal, de modo a que estes façam finalmente parte das ferramentas típicas dos profissionais, professores e investigadores da área da saúde.Programa Doutoral em Informátic

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

Front Matter - Soft Computing for Data Mining Applications

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic

Attribute-based Image Retrieval: Towards Bridging the Semantic and Intention Gaps

Ph.DDOCTOR OF PHILOSOPH