108 research outputs found

    Investigation of iris recognition in the visible spectrum

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    mong the biometric systems that have been developed so far, iris recognition systems have emerged as being one of the most reliable. In iris recognition, most of the research was conducted on operation under near infrared illumination. For unconstrained scenarios of iris recognition systems, the iris images are captured under visible light spectrum and therefore incorporate various types of imperfections. In this thesis the merits of fusing information from various sources for improving the state of the art accuracies of colour iris recognition systems is evaluated. An investigation of how fundamentally different fusion strategies can increase the degree of choice available in achieving certain performance criteria is conducted. Initially, simple fusion mechanisms are employed to increase the accuracy of an iris recognition system and then more complex fusion architectures are elaborated to further enhance the biometric system’s accuracy. In particular, the design process of the iris recognition system with reduced constraints is carried out using three different fusion approaches: multi-algorithmic, texture and colour fusion and multiple classifier systems. In the first approach, one novel iris feature extraction methodology is proposed and a multi-algorithmic iris recognition system using score fusion, composed of 3 individual systems, is benchmarked. In the texture and colour fusion approach, the advantages of fusing information from the iris texture with data extracted from the eye colour are illustrated. Finally, the multiple classifier systems approach investigates how the robustness and practicability of an iris recognition system operating on visible spectrum images can be enhanced by training individual classifiers on different iris features. Besides the various fusion techniques explored, an iris segmentation algorithm is proposed and a methodology for finding which colour channels from a colour space reveal the most discriminant information from the iris texture is introduced. The contributions presented in this thesis indicate that iris recognition systems that operate on visible spectrum images can be designed to operate with an accuracy required by a particular application scenario. Also, the iris recognition systems developed in the present study are suitable for mobile and embedded implementations

    A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)

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    Since the invention of low cost camera, it has been widely incorporated into the sensor node in Wireless Sensor Network (WSN) to form the Visual Sensor Network (VSN). However, the use of camera is bringing with it a set of new challenges, because all the sensor nodes are powered by batteries. Hence, energy consumption is one of the most critical issues that have to be taken into consideration. In addition to this, the use of batteries has also limited the resources (memory, processor) that can be incorporated into the sensor node. The life time of a VSN decreases quickly as the image is transferred to the destination. One of the solutions to the aforementioned problem is to reduce the data to be transferred in the network by using image compression. In this paper, a comprehensive survey and analysis of distributed coding algorithms that can be used to encode images in VSN is provided. This also includes an overview of these algorithms, together with their advantages and deficiencies when implemented in VSN. These algorithms are then compared at the end to determine the algorithm that is more suitable for VSN

    3D Medical Image Lossless Compressor Using Deep Learning Approaches

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    The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling

    Characterization of Alzheimer MRI Image based on Image Compression Techniques

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    The motive of this work is to develop an Algorithm for compression with high compression and good value of PSNR. The target presently changed by including appropriate transform and encoding strategies to accomplish the task. The ideal of image compression is chosen depending on a trade off among PSNR and CR. The goal is additionally discovering an ideal algorithm for medical image compression. The MR imaging are used in this work. To guarantee the nature of the planned methods, the component extraction strategies, which are the pre-solicitation of the grouping calculations, have been discovered and the outcomes uncover that the use of image doesn't modify the portrayal conduct of the medical images

    Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

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    Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

    Analysis and techniques of data compression in smart grids in the context of IEC 61850 communication protocol

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    A smart grid is characterized by a two-way communication between the generation and the loads in addition to the distributed energy resources, which dictated the integration of smart monitoring devices to achieve full observability of the network. In smart grid operation, the monitoring and the measuring devices such as smart meters, phasor measurement units (PMUs) and the intelligent electronic devices (IEDs) typically record the data and share the information across each level of the grid. In distribution substations, the data and hence the information is then further transferred throughout the supervisory control and data acquisition (SCADA) and the data control center using the communication protocols. As a result, a large amount of data is transferred among different monitoring devices, data control centers, and SCADA within the smart grid, which calls for new requirements for the communication channels and the storage capacities. Data compression is considered promising techniques to reduce the burden on the communication channels as well as the storage in particular during the smart grid operation. This thesis focuses on studying the data compression techniques when applied to power system disturbances in the context of the IEC 61850 communication protocol, which is extensively used in smart distribution substation automation. A proposed approach for data compression is introduced in this work and it is based on a combined wavelet-surrogate binary regression tree and a hybrid thresholding method. The proposed approach for data compression is tested experimentally in real-time in the context of IEC 61850 communication protocol and the performance is compared to the existing approaches for data compression. The results have shown that the implementation of the proposed data compression approach may lead to significant reduction in the number and sizes of the Generic Object-Oriented Substation Event (GOOSE) messages, which are exchanged between the IEDS and the SCADA within the smart distribution substation automation in smart grids

    Development of algorithm for identification of maligant growth in cancer using artificial neural network

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    The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The numerous algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the irregular growth of lung tissues. Higher probability of detection is taken as a goal to get an automated tool, with great accuracy. The finest feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary Binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy
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