Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance

A comparison of integration architectures

This paper presents GenSIF, a Generic Systems Integration Framework. GenSIF features a pre-planned development process on a domain-wide basis and facilitates system integration and project coordination for very large, complex and distributed systems. Domain analysis, integration architecture design and infrastructure design are identified as the three main components of GenSIF. In the next step we map Beilcore\u27s OSCA interoperability architecture, ANSA, IBM\u27s SAA and Bull\u27s DCM into GenSIF. Using the GenSIF concepts we compare each of these architectures. GenSIF serves as a general framework to evaluate and position specific architecture. The OSCA architecture is used to discuss the impact of vendor architectures on application development. All opinions expressed in this paper, especially with regard to the OSCA architecture, are the opinions of the author and do not necessarily reflect the point of view of any of the mentioned companies

Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance

Exploring the Sea: Heterogenous Geo-Referenced Data Repository

O ambiente marinho é objeto de uma atenção crescente e constitui um ambiente dinâmico e multidimensional que é muito exigente para a recolha e atualização dos dados, requerendo grandes quantidades de dados. Estes dados são únicos uma vez que as campanhas onde foram recolhidos são irrepetíveis, devido à existência de diversos fatores que estão fora do controlo dos investigadores. Além disso, o financiamento para estas campanhas pode ser difícil de conseguir. No entanto, estes conjuntos de dados são muitas vezes subutilizados ao não estarem disponíveis para todas as partes envolvidas, ou por terem formatos não interoperáveis. Atualmente, os metadados e alguns dados são registados em formulários de papel que são posteriormente digitalizados ou transcritos para folhas de cálculo, dando os investigadores mais ênfase às publicações do que à gestão dos dados recolhidos. Os dados provêm de amostras de solo, água e biológicas, sensores, rotas de navios, fotografias, vídeos, sons e análises laboratoriais. Este problema reflete-se no projeto BIOMETORE, um projeto de grandes dimensões que envolve várias equipas de investigadores marinhos liderados pelo Instituto Português do Mar e da Atmosfera (IPMA). O objetivo final do BIOMETORE é a obtenção e manutenção do bom estado ambiental (Good Environmental Status, GES) das águas marinhas europeias. Este projeto contém oito campanhas que produzem grandes quantidades de dados marinhos; estes devem ser organizados de modo a permitir a sua reutilização pelas diferentes partes interessadas. Por outro lado, o projeto SeaBioData, liderado pelo INESC TEC, visa o desenvolvimento de uma base de dados georreferenciada para o BIOMETORE, que possa reunir todos os dados disponíveis e que implemente as normas existentes de interoperabilidade de dados, conforme especificado em diretivas como a INSPIRE. A construção da base de dados é essencial para permitir que investigadores locais e comunidade internacional tenham um acesso uniforme aos dados e, ao mesmo tempo, para reduzir o esforço na gestão de dados, promovendo resultados científicos mais rápidos e precisos.De modo a respeitar a diretiva INSPIRE, adotamos o modelo de dados do OGC Sensor Observation Service. Este modelo foi adotado pela comunidade internacional, o que garante que a implementação reside numa abordagem interoperável. Analisamos as opções tecnológicas disponíveis, bem como os dados fornecidos pelo IPMA. Assim, decidimos pela implementação open source do 52º North, uma vez que suporta a maioria dos modelos de conceitos do SOS e fornece uma API REST e Serviços Web nativos. O modelo de dados do 52º North não suporta o armazenamento de todos os dados exigidos pelo IPMA para uso interno. Um dos principais desafios na modelação de dados foi estender o modelo existente, sem alterar as tabelas originais, centralizando assim os dados e, assegurando que o modelo é compatível com os serviços existentes. Tivemos de seguir a estrutura de metadados definido pelo SNIMAR, o que implicou o estudo e implementação do perfil de metadados do SNIMAR. Seguimos também o standard Darwin Core, de modo a armazenar mais detalhes sobre a classificação taxonómica das espécies. Para além disso, adicionamos uma extensão ao modelo de dados do 52º North de modo a responder às necessidades locais do BIOMETORE, uma vez que o modelo do SOS apenas armazena dados relativos às observações, ignorando entidades como as equipas, campanhas, utilizadores, documentos ou responsáveis.The marine environment is subject of an increasing attention and constitutes a dynamic and multidimensional environment, that is very demanding for data collection and update, requiring large amounts of data. This data is unique since the campaigns where it is gathered are unrepeatable, due to the existence of a wide range of factors outside the researchers' control. Moreover, funding for these campaigns can be hard to come by. Nevertheless, these datasets are often underused as they are not available to all the involved stakeholders, or involve non-interoperable formats.Currently, metadata and some of the data are registered in paper-based forms, which are later digitalized or transcribed to spreadsheets, with researchers placing emphasis in the publications rather than in the management of the collected data. Data provenance often relates to soil, water and biological samples, as well as sensors, ship routes, photos, videos, sounds and laboratorial analyses.This problem is reflected in the large BIOMETORE project that involves several teams of marine researchers lead by Instituto Português do Mar e da Atmosfera. The ultimate goal of the BIOMETORE is the achievement and maintenance of the Good Environmental Status (GES) of the European Marine Waters. This project has eight campaigns, producing large amounts of marine data that should be organized in order to enable reusability by different stakeholders. On the other hand, the SeaBioData project, lead by INESC TEC, aims at developing a georeferenced database for the BIOMETORE, that can integrate all available data and implement existing standards for data interoperability, as specified in directives such as INSPIRE. Building the database is essential to allow uniform data access by local researchers as well as the international community and, at the same time, reduce the required effort allocated to data management, promoting faster and more accurate scientific results.In order to respect the INSPIRE directive, we adopted the data model from the OGC Sensor Observation Service. This data model has already been adopted by the international community, which ensures that the implementation relies on an interoperable approach. We surveyed available technological options, as well as the datasets supplied by IPMA. We decided on the open source implementation from 52º North, since it supports the majority of the SOS model's concepts and provides a native REST API and Web Services. The 52º North data model does not support the storage of all of the data required by IPMA for internal usage. One of the main data modelling challenges was to extend the existing data model without altering the original tables, thus centralizing the data, while ensuring that the model is compliant with existing services. We had to follow the metadata structure defined by SNIMAR, which implied the study and implementation of SNIMAR's metadata profile. We followed the Darwin Core standard, in order to store more details of the taxonomic rank of the species. Furthermore, we have extended the 52º North data model, in order to address the local needs of the BIOMETORE, since the SOS model simply stored data concerning the observations, disregarding information about entities such as teams, campaigns, users, documents or responsible parties

Text Detection in Natural Scenes and Technical Diagrams with Convolutional Feature Learning and Cascaded Classification

An enormous amount of digital images are being generated and stored every day. Understanding text in these images is an important challenge with large impacts for academic, industrial and domestic applications. Recent studies address the difficulty of separating text targets from noise and background, all of which vary greatly in natural scenes. To tackle this problem, we develop a text detection system to analyze and utilize visual information in a data driven, automatic and intelligent way. The proposed method incorporates features learned from data, including patch-based coarse-to-fine detection (Text-Conv), connected component extraction using region growing, and graph-based word segmentation (Word-Graph). Text-Conv is a sliding window-based detector, with convolution masks learned using the Convolutional k-means algorithm (Coates et. al, 2011). Unlike convolutional neural networks (CNNs), a single vector/layer of convolution mask responses are used to classify patches. An initial coarse detection considers both local and neighboring patch responses, followed by refinement using varying aspect ratios and rotations for a smaller local detection window. Different levels of visual detail from ground truth are utilized in each step, first using constraints on bounding box intersections, and then a combination of bounding box and pixel intersections. Combining masks from different Convolutional k-means initializations, e.g., seeded using random vectors and then support vectors improves performance. The Word-Graph algorithm uses contextual information to improve word segmentation and prune false character detections based on visual features and spatial context. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems, and producing highly accurate text detection masks at the pixel level. To investigate the utility of our feature learning approach for other image types, we perform tests on 8- bit greyscale USPTO patent drawing diagram images. An ensemble of Ada-Boost classifiers with different convolutional features (MetaBoost) is used to classify patches as text or background. The Tesseract OCR system is used to recognize characters in detected labels and enhance performance. With appropriate pre-processing and post-processing, f-measures of 82% for part label location, and 73% for valid part label locations and strings are obtained, which are the best obtained to-date for the USPTO patent diagram data set used in our experiments. To sum up, an intelligent refinement of convolutional k-means-based feature learning and novel automatic classification methods are proposed for text detection, which obtain state-of-the-art results without the need for strong prior knowledge. Different ground truth representations along with features including edges, color, shape and spatial relationships are used coherently to improve accuracy. Different variations of feature learning are explored, e.g. support vector-seeded clustering and MetaBoost, with results suggesting that increased diversity in learned features benefit convolution-based text detectors

Proceedings of the 1st European conference on disability, virtual reality and associated technologies (ECDVRAT 1996)

The proceedings of the conferenc