346 research outputs found

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    The 2023 terahertz science and technology roadmap

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    Terahertz (THz) radiation encompasses a wide spectral range within the electromagnetic spectrum that extends from microwaves to the far infrared (100 GHz–∼30 THz). Within its frequency boundaries exist a broad variety of scientific disciplines that have presented, and continue to present, technical challenges to researchers. During the past 50 years, for instance, the demands of the scientific community have substantially evolved and with a need for advanced instrumentation to support radio astronomy, Earth observation, weather forecasting, security imaging, telecommunications, non-destructive device testing and much more. Furthermore, applications have required an emergence of technology from the laboratory environment to production-scale supply and in-the-field deployments ranging from harsh ground-based locations to deep space. In addressing these requirements, the research and development community has advanced related technology and bridged the transition between electronics and photonics that high frequency operation demands. The multidisciplinary nature of THz work was our stimulus for creating the 2017 THz Science and Technology Roadmap (Dhillon et al 2017 J. Phys. D: Appl. Phys. 50 043001). As one might envisage, though, there remains much to explore both scientifically and technically and the field has continued to develop and expand rapidly. It is timely, therefore, to revise our previous roadmap and in this 2023 version we both provide an update on key developments in established technical areas that have important scientific and public benefit, and highlight new and emerging areas that show particular promise. The developments that we describe thus span from fundamental scientific research, such as THz astronomy and the emergent area of THz quantum optics, to highly applied and commercially and societally impactful subjects that include 6G THz communications, medical imaging, and climate monitoring and prediction. Our Roadmap vision draws upon the expertise and perspective of multiple international specialists that together provide an overview of past developments and the likely challenges facing the field of THz science and technology in future decades. The document is written in a form that is accessible to policy makers who wish to gain an overview of the current state of the THz art, and for the non-specialist and curious who wish to understand available technology and challenges. A such, our experts deliver a 'snapshot' introduction to the current status of the field and provide suggestions for exciting future technical development directions. Ultimately, we intend the Roadmap to portray the advantages and benefits of the THz domain and to stimulate further exploration of the field in support of scientific research and commercial realisation

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Computational Imaging for Phase Retrieval and Biomedical Applications

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    In conventional imaging, optimizing hardware is prioritized to enhance image quality directly. Digital signal processing is viewed as supplementary. Computational imaging intentionally distorts images through modulation schemes in illumination or sensing. Then its reconstruction algorithms extract desired object information from raw data afterwards. Co-designing hardware and algorithms reduces demands on hardware and achieves the same or even better image quality. Algorithm design is at the heart of computational imaging, with model-based inverse problem or data-driven deep learning methods as approaches. This thesis presents research work from both perspectives, with a primary focus on the phase retrieval issue in computational microscopy and the application of deep learning techniques to address biomedical imaging challenges. The first half of the thesis begins with Fourier ptychography, which was employed to overcome chromatic aberration problems in multispectral imaging. Then, we proposed a novel computational coherent imaging modality based on Kramers-Kronig relations, aiming to replace Fourier ptychography as a non-iterative method. While this approach showed promise, it lacks certain essential characteristics of the original Fourier ptychography. To address this limitation, we introduced two additional algorithms to form a whole package scheme. Through comprehensive evaluation, we demonstrated that the combined scheme outperforms Fourier ptychography in achieving high-resolution, large field-of-view, aberration-free coherent imaging. The second half of the thesis shifts focus to deep-learning-based methods. In one project, we optimized the scanning strategy and image processing pipeline of an epifluorescence microscope to address focus issues. Additionally, we leveraged deep-learning-based object detection models to automate cell analysis tasks. In another project, we predicted the polarity status of mouse embryos from bright field images using adapted deep learning models. These findings highlight the capability of computational imaging to automate labor-intensive processes, and even outperform humans in challenging tasks.</p

    The 2023 terahertz science and technology roadmap

    Get PDF
    Terahertz (THz) radiation encompasses a wide spectral range within the electromagnetic spectrum that extends from microwaves to the far infrared (100 GHz–∼30 THz). Within its frequency boundaries exist a broad variety of scientific disciplines that have presented, and continue to present, technical challenges to researchers. During the past 50 years, for instance, the demands of the scientific community have substantially evolved and with a need for advanced instrumentation to support radio astronomy, Earth observation, weather forecasting, security imaging, telecommunications, non-destructive device testing and much more. Furthermore, applications have required an emergence of technology from the laboratory environment to production-scale supply and in-the-field deployments ranging from harsh ground-based locations to deep space. In addressing these requirements, the research and development community has advanced related technology and bridged the transition between electronics and photonics that high frequency operation demands. The multidisciplinary nature of THz work was our stimulus for creating the 2017 THz Science and Technology Roadmap (Dhillon et al 2017 J. Phys. D: Appl. Phys. 50 043001). As one might envisage, though, there remains much to explore both scientifically and technically and the field has continued to develop and expand rapidly. It is timely, therefore, to revise our previous roadmap and in this 2023 version we both provide an update on key developments in established technical areas that have important scientific and public benefit, and highlight new and emerging areas that show particular promise. The developments that we describe thus span from fundamental scientific research, such as THz astronomy and the emergent area of THz quantum optics, to highly applied and commercially and societally impactful subjects that include 6G THz communications, medical imaging, and climate monitoring and prediction. Our Roadmap vision draws upon the expertise and perspective of multiple international specialists that together provide an overview of past developments and the likely challenges facing the field of THz science and technology in future decades. The document is written in a form that is accessible to policy makers who wish to gain an overview of the current state of the THz art, and for the non-specialist and curious who wish to understand available technology and challenges. A such, our experts deliver a 'snapshot' introduction to the current status of the field and provide suggestions for exciting future technical development directions. Ultimately, we intend the Roadmap to portray the advantages and benefits of the THz domain and to stimulate further exploration of the field in support of scientific research and commercial realisation

    Nano-film functionalized exposed core fibers enabling resonance-driven dispersive wave tailoring

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    Light sources with specific optical properties are the backbone of optical technologies such as spectroscopy or hyperspectral imaging. Yet, the creation of broadband, stable, and spectrally flat light sources, especially at low pump energies, remains a particular challenge. Supercontinuum generation (SCG) is a well-established method for broadband light generation in optical fibers. For tailorable SCG spectra, it is essential to accurately design and precisely control the dispersion of fibers with new methods. This thesis aims to explore nonlinear frequency conversion in resonance-enhanced fibers to create tunable broadband light sources with tailored properties at low pump energies. By depositing high refractive index nano-films with different thicknesses on the surface of the exposed fiber core, the dispersion of the fibers and thus the output spectrum of SCG can be tuned. Different nano-film geometries are investigated, featuring TiO2 nano-films with a uniform thickness, Ta2O5 nano-films with a gradually increasing thickness along the fiber length, and periodically structured Ta2O5 nano-films. Experiments and simulations reveal the advantages of a longitudinally varying dispersion over uniformly coated fibers concerning an enhanced spectral flatness and an enlarged bandwidth. Furthermore, periodically structured nano-films lead to multi-color tailorable higher-order dispersive waves via quasi phase-matching, which are outside of the wavelength range of classical soliton-based SCG. Resonance-based modifications of the fiber dispersion by using nano-films are a powerful new tool to efficiently shape nonlinear frequency conversion in SCG even at low pump energies. It has high technological potential for the realization of novel, ultrafast, broadband, and stable nonlinear light sources for biophotonics, environmental, life sciences, medical diagnostics, and metrology

    Face recognition using skin texture

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    In today's society where information technology is depended upon throughout homes, educational establishments and workplaces the challenge of identity management is ever growing. Advancements in image processing and biometric feature based identification have provided a means for computer software to accurately identify individuals from increasingly vast databases of users. In the quest to improve the performance of such systems in varying environmental conditions skin texture is here proposed as a biometric feature. This thesis presents and discusses a hypothesis for the use of facial skin texture regions taken from 2-dimensional photographs to accurately identify individuals using three classifiers (neural network, support vector machine and linear discriminant). Gabor wavelet filters are primarily used for feature extraction and arc supported in later chapters by the grey-level cooccurrence probability matrix (GLCP) to strengthen the system by providing supplementary high-frequency features. Various fusion techniques for combining these features are presented and their perfonnance is compared including both score and feature fusion and various permutations of each. Based on preliminary results from the BioSecure Multimodal Database (BMDB) , the work presented indicates that isolated texture regions of the human face taken from under the eye may provide sufficient information to discriminately identify an individual with an equal error rate (EER) of under 1% when operating in greyscale. An analysis of the performance of the algorithm against image resolution investigates the systems performance when faced with lower resolution training images and discusses optimal resolutions for classifier training. The system also shows a good degree of robustness when the probe image resolution is reduced indicating that the algorithm provides some level of scale invariance. Scope for future work is laid out and a review of the evaluation is also presented

    Blind Hyperspectral Unmixing Using Autoencoders

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    The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds.Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning) og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar. Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og útfærslur á þeim. í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með sjálfkóðurum. Megin framlag ritgerðarinnar er eftirfarandi: - Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm til að aðgreina grennd af rófum í einu. - Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches) og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina. - Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum.The Icelandic Research Fund under Grants 174075-05 and 207233-05
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