40 research outputs found

    Image Hash Minimization for Tamper Detection

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    Tamper detection using image hash is a very common problem of modern days. Several research and advancements have already been done to address this problem. However, most of the existing methods lack the accuracy of tamper detection when the tampered area is low, as well as requiring long image hashes. In this paper, we propose a novel method objectively to minimize the hash length while enhancing the performance at low tampered area.Comment: Published at the 9th International Conference on Advances in Pattern Recognition, 201

    Object Recognition

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    Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

    Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

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    This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

    Finger Vein Verification with a Convolutional Auto-encoder

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    Identification through Finger Bone Structure Biometrics

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    Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux, May 20-21, TU Eindhoven

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    Expanding the Frame: Realising Engagement Through an Interactive, Visualisation-Based Search in Digital Humanities Research Environments

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    AstheDigitalHumanitiesexpandsitsmodesofinterrogation,ithascontinuedtodevelop new ways of researching and exploring text. As a result, visualisations have risen to prominence as scholars have begun to explore concepts behind Moretti’s Distant Reading and Jocker’s Macroanalysis. While the study of these types of visualisations has largely focused on their ability to provide higher-level insights, little exploration has been conducted concerning their effectiveness within the context of a learning or research environment. Drawing on discussions from fields of psychology (specifically the role of working memory), education (modes and frameworks of learning), and computer science (usability and interaction design), this thesisattemptstodiscovertheeffectsofinteractivevisualisationscomparedtostandard keyword search approaches on a user’s engagement with the overall system, as well as the effect on learning as a direct result of engagement. Centred around an 18th century manuscript detailing the expenditures of the Royal Irish College at Alcalá de Heneres, this thesis presents the design and implementation of the Alcalá Record Books and discusses a case study that was conducted to explore the effects of the visualisation-based search. Ultimately, this thesis advocates for the inclusion of an interactive, visualisation-based search as a complement to existing keyword searches, highlighting the advantages such searches bring to engagement, learning, and overall satisfaction with the system as a whole

    AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

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    © 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

    A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

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    Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature

    Generalized averaged Gaussian quadrature and applications

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    A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal
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