1,411 research outputs found

    Recognition of License Plates and Optical Nerve Pattern Detection Using Hough Transform

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    The global technique of detection of the features is Hough transform used in image processing, computer vision and image analysis. The detection of prominent line of the object under consideration is the main purpose of the Hough transform which is carried out by the process of voting. The first part of this work is the use of Hough transform as feature vector, tested on Indian license plate system, having font of UK standard and UK standard 3D, which has ten slots for characters and numbers.So tensub images are obtained.These sub images are fed to Hough transform and Hough peaks to extract the Hough peaks information. First two Hough peaks are taken into account for the recognition purposes. The edge detection along with image rotation is also used prior to the implementation of Hough transform in order to get the edges of the gray scale image. Further, the image rotation angle is varied; the superior results are taken under consideration. The second part of this work makes the use of Hough transform and Hough peaks, for examining the optical nerve patterns of eye. An available database for RIM-one is used to serve the purpose. The optical nerve pattern is unique for every human being and remains almost unchanged throughout the life time. So the purpose is to detect the change in the pattern report the abnormality, to make automatic system so capable that they can replace the experts of that field. For this detection purpose Hough Transform and Hough Peaks are used and the fact that these nerve patterns are unique in every sense is confirmed

    Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification

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    The main contributions of this thesis are the evaluation of MPEG Compact Descriptor for Visual Search in the context of indoor robotic navigation and the introduction of a new method for training Convolutional Neural Networks with applications to object classification. The choice for image descriptor in a visual navigation system is not straightforward. Visual descriptors must be distinctive enough to allow for correct localisation while still offering low matching complexity and short descriptor size for real-time applications. MPEG Compact Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness and size. In this work, we describe how these trade-offs can be used for efficient loop-detection in a typical indoor environment. We first describe a probabilistic approach to loop detection based on the standard’s suggested similarity metric. We then evaluate the performance of CDVS compression modes in terms of matching speed, feature extraction, and storage requirements and compare them with the state of the art SIFT descriptor for five different types of indoor floors. During the second part of this thesis we focus on the new paradigm to machine learning and computer vision called Deep Learning. Under this paradigm visual features are no longer extracted using fine-grained, highly engineered feature extractor, but rather using a Convolutional Neural Networks (CNN) that extracts hierarchical features learned directly from data at the cost of long training periods. In this context, we propose a method for speeding up the training of Convolutional Neural Networks (CNN) by exploiting the spatial scaling property of convolutions. This is done by first training a pre-train CNN of smaller kernel resolutions for a few epochs, followed by properly rescaling its kernels to the target’s original dimensions and continuing training at full resolution. We show that the overall training time of a target CNN architecture can be reduced by exploiting the spatial scaling property of convolutions during early stages of learning. Moreover, by rescaling the kernels at different epochs, we identify a trade-off between total training time and maximum obtainable accuracy. Finally, we propose a method for choosing when to rescale kernels and evaluate our approach on recent architectures showing savings in training times of nearly 20% while test set accuracy is preserved

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Video Processing Acceleration using Reconfigurable Logic and Graphics Processors

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    A vexing question is `which architecture will prevail as the core feature of the next state of the art video processing system?' This thesis examines the substitutive and collaborative use of the two alternatives of the reconfigurable logic and graphics processor architectures. A structured approach to executing architecture comparison is presented - this includes a proposed `Three Axes of Algorithm Characterisation' scheme and a formulation of perfor- mance drivers. The approach is an appealing platform for clearly defining the problem, assumptions and results of a comparison. In this work it is used to resolve the advanta- geous factors of the graphics processor and reconfigurable logic for video processing, and the conditions determining which one is superior. The comparison results prompt the exploration of the customisable options for the graphics processor architecture. To clearly define the architectural design space, the graphics processor is first identifed as part of a wider scope of homogeneous multi-processing element (HoMPE) architectures. A novel exploration tool is described which is suited to the investigation of the customisable op- tions of HoMPE architectures. The tool adopts a systematic exploration approach and a high-level parameterisable system model, and is used to explore pre- and post-fabrication customisable options for the graphics processor. A positive result of the exploration is the proposal of a reconfigurable engine for data access (REDA) to optimise graphics processor performance for video processing-specific memory access patterns. REDA demonstrates the viability of the use of reconfigurable logic as collaborative `glue logic' in the graphics processor architecture

    Hardware accelerated real-time Linux video anonymizer

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    Dissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresOs Sistemas Embebidos estão presentes atualmente numa variada gama de equipamentos do quotidiano do ser humano. Desde TV-boxes, televisões, routers até ao indispensável telemóvel. O Sistema Operativo Linux, com a sua filosofia de distribuição ”one-size-fits-all” tornou-se uma alternativa viável, fornecendo um vasto suporte de hardware, técnicas de depuração, suporte dos protocolos de comunicação de rede, entre outros serviços, que se tornaram no conjunto standard de requisitos na maioria dos sistemas embebidos atuais. Este sistema operativo torna-se apelativo pela sua filosofia open-source que disponibiliza ao utilizador um vasto conjunto de bibliotecas de software que possibilitam o desenvolvimento num determinado domínio com maior celeridade e facilidade de integração de software complexo. Os algoritmos deMachine Learning são desenvolvidos para a automização de tarefas e estão presentes nas mais variadas tecnologias, desde o sistema de foco de imagem nosmartphone até ao sistema de deteção dos limites de faixa de rodagem de um sistema de condução autónoma. Estes são algoritmos que quando compilados para as plataformas de sistemas embebidos, resultam num esforço de processamento e de consumo de recursos, como o footprint de memória, que na maior parte dos casos supera em larga escala o conjunto de recursos disponíveis para a aplicação do sistema, sendo necessária a implementação de componentes que requerem maior poder de processamento através de elementos de hardware para garantir que as métricas tem porais sejam satisfeitas. Esta dissertação propõe-se, por isso, à criação de um sistema de anonimização de vídeo que adquire, processa e manipula as frames, com o intuito de garantir o anonimato, mesmo na transmissão. A sua implementação inclui técnicas de Deteção de Objectos, fazendo uso da combinação das tecnologias de aceleração por hardware: paralelização e execução em hardware especial izado. É proposta então uma implementação restringida tanto temporalmente como no consumo de recursos ao nível do hardware e software.Embedded Systems are currently present in a wide range of everyday equipment. From TV-boxes, televisions and routers to the indispensable smartphone. Linux Operating System, with its ”one-size-fits-all” distribution philosophy, has become a viable alternative, providing extensive support for hardware, debugging techniques, network com munication protocols, among other functionalities, which have become the standard set of re quirements in most modern embedded systems. This operating system is appealing due to its open-source philosophy, which provides the user with a vast set of software libraries that enable development in a given domain with greater speed and ease the integration of complex software. Machine Learning algorithms are developed to execute tasks autonomously, i.e., without human supervision, and are present in the most varied technologies, from the image focus system on the smartphone to the detection system of the lane limits of an autonomous driving system. These are algorithms that, when compiled for embedded systems platforms, require an ef fort to process and consume resources, such as the memory footprint, which in most cases far outweighs the set of resources available for the application of the system, requiring the imple mentation of components that need greater processing power through elements of hardware to ensure that the time metrics are satisfied. This dissertation proposes the creation of a video anonymization system that acquires, pro cesses, and manipulates the frames, in order to guarantee anonymity, even during the transmis sion. Its implementation includes Object Detection techniques, making use of the combination of hardware acceleration technologies: parallelization and execution in specialized hardware. An implementation is then proposed, restricted both in time and in resource consumption at hardware and software levels

    FUZZY KERNEL REGRESSION FOR REGISTRATION AND OTHER IMAGE WARPING APPLICATIONS

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    In this dissertation a new approach for non-rigid medical im- age registration is presented. It relies onto a probabilistic framework based on the novel concept of Fuzzy Kernel Regression. The theoric framework, after a formal introduction is applied to develop several complete registration systems, two of them are interactive and one is fully automatic. They all use the composition of local deforma- tions to achieve the final alignment. Automatic one is based onto the maximization of mutual information to produce local affine aligments which are merged into the global transformation. Mutual Information maximization procedure uses gradient descent method. Due to the huge amount of data associated to medical images, a multi-resolution topology is embodied, reducing processing time. The distance based interpolation scheme injected facilitates the similairity measure op- timization by attenuating the presence of local maxima in the func- tional. System blocks are implemented on GPGPUs allowing efficient parallel computation of large 3d datasets using SIMT execution. Due to the flexibility of Mutual Information, it can be applied to multi- modality image scans (MRI, CT, PET, etc.). Both quantitative and qualitative experiments show promising results and great potential for future extension. Finally the framework flexibility is shown by means of its succesful application to the image retargeting issue, methods and results are presented
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