1,055 research outputs found

    Desarrollo y caracterización de nuevas harinas de lenteja y quinoa fermentadas con Pleurotus ostreatus

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    Tesis por compendio[ES] La población mundial está en constante crecimiento, por lo que la soberanía alimentaria se ha convertido en un desafío crucial. Se estima que la demanda de alimentos de origen animal aumentará en un 68% para el año 2050, lo cual resulta insostenible a nivel medioambiental. Así, impulsar un mayor consumo de proteína vegetal se plantea como una de las estrategias dirigidas a promover la sostenibilidad ambiental, asegurando la disponibilidad de proteína dietética para toda la población. Si bien ciertas las legumbres y pseudocereales son una excelente fuente de nutrientes y en particular de proteína, también contienen ciertos antinutrientes que pueden limitar su digestibilidad. Este aspecto es especialmente relevante en aquellos grupos poblaciones con alteraciones gastrointestinales, como las que pueden aparecer con la edad en población sénior. En este contexto, el objetivo general de esta tesis doctoral es aplicar la fermentación en estado sólido (FES) como bioestrategia para la obtención de harinas de lenteja y quinoa con digestibilidad y bioaccesibilidad mejoradas. Para alcanzar este objetivo, se llevó a cabo la fermentación con el hongo Pleurotus ostreatus, en dos variedades de lentejas y quinoa, y se estabilizaron posteriormente mediante secado por aire caliente a diferentes temperaturas, además de la liofilización como método de referencia. Posteriormente, las harinas fermentadas fueron digeridas in vitro simulando el proceso digestivo de un adulto sano (estándar de referencia), así como en condiciones alteradas del adulto mayor. Los resultados obtenidos evidenciaron un incremento de proteína total, así como de la actividad inhibidora de la enzima convertidora de angiotensina (ECA), conjuntamente con una disminución del contenido en ácido fítico, todo ello como resultado de la actividad metabólica del hongo sobre el sustrato. A pesar de que la FES también ocasionó una reducción de la actividad antioxidante, el posterior secado por aire caliente, especialmente a 70 °C incrementó este parámetro. Asimismo, la FES y el secado por aire caliente promovieron cambios en el perfil fenólico, disminuyendo algunos compuestos e incrementando otros como el ácido gálico hasta 5 veces su contenido inicial. En cuanto al perfil volátil de las harinas fermentadas, este se caracterizó por un aroma dulce, afrutado y con matices a cacao, acompañado de notas de setas y sustratos cocidos, debido a las concentraciones de benzaldehído, hexanal, nonanal, furfural y 1-octen-3-ol que se generaron durante la fermentación. Por otro lado, con respecto a la digestibilidad de las harinas fermentadas en condiciones estándar de adulto sano, la FES y el secado a 70 °C incrementó la hidrólisis de las proteínas, así como la liberación de aminoácidos hidrófobos y aminoácidos cargados negativamente. La FES también disminuyó la actividad inhibidora de la ECA en los digeridos, sin embargo, ésta aumentó después del secado a 70 °C debido a las melanoidinas generadas durante el secado. Además, las propiedades antioxidantes y la bioaccesibilidad de minerales también se vieron incrementados con la FES y el posterior secado a 70 °C. Finalmente, la simulación de las alteraciones gastrointestinales que comúnmente se dan en el adulto mayor, indicaron que estas impactaban negativamente en la mayoría de los parámetros evaluados, a excepción de la bioaccesibilidad del magnesio, hierro y calcio en comparación con el modelo estándar. En conclusión, se ha logrado mejorar el perfil nutricional y funcional de las nuevas harinas obtenidas por fermentación con el hongo P. ostreatus, y posterior secado por aire caliente en comparación con las harinas obtenidas a partir de sustrato no fermentado, conduciendo esto a una mejora significativa en la digestibilidad y la bioaccesibilidad de los nutrientes, lo que puede ser especialmente relevante para el diseño de alimentos orientados a grupos de población con alta demanda de proteína de fácil digestión.[CA] La població mundial està en constant creixement, per la qual cosa la sobirania alimentària ha esdevingut un desafiament crucial. S'estima que la demanda d'aliments d'origen animal augmentarà un 68% per a l'any 2050, cosa que resulta insostenible a nivell mediambiental. Així, impulsar un consum més gran de proteïna vegetal es planteja com una de les estratègies dirigides a promoure la sostenibilitat ambiental, assegurant la disponibilitat de proteïna dietètica per a tota la població. Si bé certs els llegums i pseudocereals són una excel·lent font de nutrients i en particular de proteïna, també contenen certs antinutrients que poden limitar-ne la digestibilitat. Aquest aspecte és especialment rellevant en aquells grups de poblacions amb alteracions gastrointestinals, com les que poden aparèixer amb l'edat en població sènior. En aquest context, l'objectiu general d'aquesta tesi doctoral és aplicar la fermentació en estat sòlid (FES) com a bioestratègia per obtenir farines de llentia i quinoa amb digestibilitat i bioaccessibilitat millorades. Per assolir aquest objectiu, es va dur a terme la fermentació amb el fong Pleurotus ostreatus, en dues varietats de llenties i quinoa, i es van estabilitzar posteriorment mitjançant assecat per aire calent a diferents temperatures, a més de la liofilització com a mètode de referència. Posteriorment, les farines fermentades van ser digerides in vitro simulant el procés digestiu d'un adult sa (estàndard de referència), així com en condicions alterades de l'adult més gran. Els resultats obtinguts van evidenciar un increment de proteïna total, així com de l'activitat inhibidora de l'enzim convertidor d'angiotensina (ECA), conjuntament amb una disminució del contingut en àcid fític, com a resultat de l'activitat metabòlica del fong sobre el substrat. Tot i que la FES també va ocasionar una reducció de l'activitat antioxidant, el posterior assecat per aire calent, especialment a 70 °C va incrementar aquest paràmetre. Així mateix, la FES i l'assecatge per aire calent van promoure canvis en el perfil fenòlic, disminuint alguns compostos i incrementant-ne d'altres com l'àcid gàlic fins a 5 vegades el contingut inicial. Quant al perfil volàtil de les farines fermentades, aquest es va caracteritzar per una aroma dolça, afruitada i amb matisos a cacau, acompanyat de notes de bolets i substrats cuits, a causa de les concentracions de benzaldehid, hexanal, nonanal, furfural i 1-octen -3-ol que es van generar durant la fermentació. D'altra banda, pel que fa a la digestibilitat de les farines fermentades en condicions estàndard d'adult sa, la FES i l'assecatge a 70 °C va incrementar la hidròlisi de les proteïnes, així com l'alliberament d'aminoàcids hidròfobs i aminoàcids carregats negativament. La FES també va disminuir l'activitat inhibidora de l'ACA en els digerits, però aquesta va augmentar després de l'assecat a 70 °C a causa de les melanoïdines generades durant l'assecat. A més, les propietats antioxidants i la bioaccessibilitat de minerals també es van veure incrementats amb la FES i el posterior assecat a 70 °C. Finalment, la simulació de les alteracions gastrointestinals que comunament es donen a l'adult major, van indicar que aquestes impactaven negativament a la majoria dels paràmetres avaluats, a excepció de la bioaccessibilitat del magnesi, ferro i calci en comparació del model estàndard. En conclusió, s'ha aconseguit millorar el perfil nutricional i funcional de les noves farines obtingudes per fermentació amb el fong P. ostreatus, i posterior assecat per aire calent en comparació amb les farines obtingudes a partir de substrat no fermentat, conduint-ho a una millora significativa en la digestibilitat i la bioaccessibilitat dels nutrients, cosa que pot ser especialment rellevant per al disseny d'aliments orientats a grups de població amb alta demanda de proteïna de fàcil digestió.[EN] The world's population is constantly growing, making food sovereignty a crucial challenge. It is estimated that the demand for animal-based food will increase by 68% by 2050, which is environmentally unsustainable. Thus, encouraging greater consumption of plant protein is one of the strategies aimed to promote environmental sustainability by ensuring the availability of dietary protein for the entire population. Although certain legumes and pseudocereals are an excellent source of nutrients and in particular protein, they also contain certain anti-nutrients that can limit their digestibility. This aspect is especially relevant in those population groups with gastrointestinal disorders, such as those that may appear with age in the elderly population. In this context, the general objective of this doctoral thesis is to apply solid-state fermentation (SSF) as a biostrategy to obtain lentil and quinoa flours with improved digestibility and bioaccessibility. To achieve this objective, fermentation with the fungus Pleurotus ostreatus was carried out on two varieties of lentils and quinoa, and subsequently stabilised by hot air drying at different temperatures, in addition by freeze-drying as a reference method. Subsequently, the fermented flours were digested in vitro simulating the digestive process of a healthy adult (reference standard), as well as under altered conditions of the elderly. The results obtained evidenced an increase in total protein, as well as in angiotensin-converting enzyme (ACE) inhibitory activity, together with a decrease in phytic acid content, all as a result of the metabolic activity of the fungus on the substrate. Although SSF also caused a reduction in antioxidant activity, subsequent hot air drying, especially at 70 °C, increased this parameter. Similarly, SSF and hot air-drying promoted changes in the phenolic profile, decreasing some compounds and increasing others such as gallic acid up to 5 times its initial content. The volatile profile of the fermented flours was characterised by a sweet, fruity aroma with hints of cocoa, accompanied by notes of mushrooms and cooked substrates, due to the concentrations of benzaldehyde, hexanal, nonanal, furfural and 1-octen-3-ol that were generated during fermentation. On the other hand, regarding the digestibility of the fermented flours under standard healthy adult conditions, SSF and drying at 70 °C increased the hydrolysis of proteins, as well as the release of hydrophobic and negatively charged amino acids. SSF also decreased the ACE inhibitory activity of the digests, however, it increased after drying at 70 °C due to melanoidins generated during drying. Furthermore, antioxidant properties and mineral bioaccessibility were also increased with SSF and subsequent drying at 70 °C. Finally, simulation of gastrointestinal disturbances commonly found in the older adult indicated that these impacted negatively on most of the parameters evaluated, with the exception of the bioaccessibility of magnesium, iron and calcium compared to the standard model. In conclusion, the nutritional and functional profile of the new flours obtained by fermentation with the fungus P. ostreatus, and subsequent hot air drying has been improved compared to flours obtained from unfermented substrate, leading to a significant improvement in the digestibility and bioaccessibility of nutrients, which may be particularly relevant for the design of foods oriented to population groups with a high demand of easily digestible protein.Sánchez García, J. (2023). Desarrollo y caracterización de nuevas harinas de lenteja y quinoa fermentadas con Pleurotus ostreatus [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202014Compendi

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Neural Architecture Search for Image Segmentation and Classification

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    Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments. This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate DNNs

    Algorithms for light applications: from theoretical simulations to prototyping

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    [eng] Although the first LED dates to the middle of the 20th century, it has not been until the last decade that the market has been flooded with high efficiency and high durability LED solutions compared to previous technologies. In addition, luminaires that include types of LEDs differentiated in hue or color have already appeared. These luminaires offer new possibilities to reach colorimetric or non-visual capabilities not seen to date. Due to the enormous number of LEDs on the market, with very different spectral characteristics, the use of the spectrometer as a measuring device for determining LEDs properties has become popular. Obtaining colorimetric information from a luminaire is a necessary step to commercialize it, so it is a tool commonly used by many LED manufacturers. This doctoral thesis advances the state-of-the-art and knowledge of LED technology at the level of combined spectral emission, as well as applying innovative spectral reconstruction techniques to a commercial multichannel colorimetric sensor. On the one hand, new spectral simulation algorithms that allow obtaining a very high number of results have been developed, being able to obtain optimized values of colorimetric and non-visual parameters in multichannel light sources. MareNostrum supercomputer has been used and new relationships between colorimetric and non-visual parameters in commercial white LED datasets have been found through data analysis. Moreover, the functional improvement of a multichannel colorimetric sensor has been explored by providing it with a neural network for spectral reconstruction. A large amount of data has been generated, which has allowed simulations and statistical studies on the error committed in the spectral reconstruction process using different techniques. This improvement has led to an increase in the spectral resolution measured by the sensor, allowing better accuracy in the calculation of colorimetric parameters. Prototypes of the light sources and the colorimetric sensor have been developed in order to experimentally demonstrate the theoretical framework generated. All the prototypes have been characterized and the errors generated with respect to the theoretical models have been evaluated. The results obtained have been validated through the application of different industry standards by comparison with calibrated commercial devices.[cat] Aquesta tesi doctoral realitza un avançament en l’estat de l’art i en el coneixement sobre la tecnologia LED a nivell d’emissió espectral combinada, a més d’aplicar tècniques innovadores de reconstrucció espectral a un sensor colorimètric multicanal comercial. Per una banda, s’han desenvolupat nous algoritmes de simulació espectral que permeten obtenir un nombre molt elevat de resultats, sent capaços d’obtenir valors optimitzats de paràmetres colorimètrics i no-visuals en fonts de llum multicanal. S’ha fet ús del supercomputador MareNostrum i s’han trobat noves relacions entre paràmetres colorimètrics i no visuals en conjunts de LEDs blancs comercials a través de l’anàlisi de dades. Per altra banda, s’ha explorat la millora funcional d’un sensor colorimètric multicanal, dotant-lo d’una xarxa neuronal per a la reconstrucció espectral. S’han generat una gran quantitat de dades que han permès realitzar simulacions i estudis estadístics sobre l’error comès en el procés de reconstrucció espectral utilitzant diferents tècniques. Aquesta millora ha implicat un augment de la resolució espectral mesurada pel sensor, permetent obtenir una millor precisió en el càlcul de paràmetres colorimètrics. S’han desenvolupat prototips de les fonts de llum i del sensor colorimètric amb l’objectiu de demostrar experimentalment el marc teòric generat. Tots els prototips han estat caracteritzats i s’han avaluat els errors generats respecte els models teòrics. Els resultats obtinguts s’han validat a través de l’aplicació de diferents estàndards de la indústria o a través de la comparativa amb dispositius comercials calibrats

    Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies

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    Detecting subtle defects in window frames, including dents and scratches, is vital for upholding product integrity and sustaining a positive brand perception. Conventional machine vision systems often struggle to identify these defects in challenging environments like construction sites. In contrast, modern vision systems leveraging machine and deep learning (DL) are emerging as potent tools, particularly for cosmetic inspections. However, the promise of DL is yet to be fully realized. A few manufacturers have established a clear strategy for AI integration in quality inspection, hindered mainly by issues like scarce clean datasets and environmental changes that compromise model accuracy. Addressing these challenges, our study presents an innovative approach that amplifies defect detection in DL models, even with constrained data resources. The paper proposes a new defect detection pipeline called InspectNet (IPT-enhanced UNET) that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset and a Unet model tuned for window frame defect detection and segmentation. Experiments were carried out using a Spot Robot doing window frame inspections . 16 variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that, on average, across all proposed evaluation measures, Unet outperformed all other algorithms when IPT-enhanced augmentations were applied. In particular, when using the best dataset, the average Intersection over Union (IoU) values achieved were IPT-enhanced Unet, reaching 0.91 of mIoU

    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

    Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space

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    Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%

    Deep learning for characterizing full-color 3D printers: accuracy, robustness, and data-efficiency

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    High-fidelity color and appearance reproduction via multi-material-jetting full-color 3D printing has seen increasing applications, including art and cultural artifacts preservation, product prototypes, game character figurines, stop-motion animated movie, and 3D-printed prostheses such as dental restorations or prosthetic eyes. To achieve high-quality appearance reproduction via full-color 3D printing, a prerequisite is an accurate optical printer model that is a predicting function from an arrangement or ratio of printing materials to the optical/visual properties (e.g. spectral reflectance, color, and translucency) of the resulting print. For appearance 3D printing, the model needs to be inverted to determine the printing material arrangement that reproduces distinct optical/visual properties such as color. Therefore, the accuracy of optical printer models plays a crucial role for the final print quality. The process of fitting an optical printer model's parameters for a printing system is called optical characterization, which requires test prints and optical measurements. The objective of developing a printer model is to maximize prediction performance such as accuracy, while minimizing optical characterization efforts including printing, post-processing, and measuring. In this thesis, I aim at leveraging deep learning to achieve holistically-performant optical printer models, in terms of three different performance aspects of optical printer models: 1) accuracy, 2) robustness, and 3) data efficiency. First, for model accuracy, we propose two deep learning-based printer models that both achieve high accuracies with only a moderate number of required training samples. Experiments show that both models outperform the traditional cellular Neugebauer model by large margins: up to 6 times higher accuracy, or, up to 10 times less data for a similar accuracy. The high accuracy could enhance or even enable color- and translucency-critical applications of 3D printing such as dental restorations or prosthetic eyes. Second, for model robustness, we propose a methodology to induce physically-plausible constraints and smoothness into deep learning-based optical printer models. Experiments show that the model not only almost always corrects implausible relationships between material arrangement and the resulting optical/visual properties, but also ensures significantly smoother predictions. The robustness and smoothness improvements are important to alleviate or avoid unacceptable banding artifacts on textures of the final printouts, particularly for applications where texture details must be preserved, such as for reproducing prosthetic eyes whose texture must match the companion (healthy) eye. Finally, for data efficiency, we propose a learning framework that significantly improves printer models' data efficiency by employing existing characterization data from other printers. We also propose a contrastive learning-based approach to learn dataset embeddings that are extra inputs required by the aforementioned learning framework. Experiments show that the learning framework can drastically reduce the number of required samples for achieving an application-specific prediction accuracy. For some printers, it requires only 10% of the samples to achieve a similar accuracy as the state-of-the-art model. The significant improvement in data efficiency makes it economically possible to frequently characterize 3D printers to achieve more consistent output across different printers over time, which is crucial for color- and translucency-critical individualized mass production. With these proposed deep learning-based methodologies significantly improving the three performance aspects (i.e. accuracy, robustness, and data efficiency), a holistically-performant optical printer model can be achieved, which is particularly important for color- and translucency-critical applications such as dental restorations or prosthetic eyes

    Explaining Individual Differences

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    Most psychological research aims to uncover generalizations about the mind that hold across subjects. Philosophical discussions of scientific explanation have focused on such generalizations, but in doing so, have often overlooked an important phenomenon: variation. Variation is ubiquitous in psychology and many other domains, and an important target of explanation in its own right. Here I characterize explananda that concern individual differences and formulate an account of what it takes to explain them. I argue that the notion of actual difference making, the only causal concept in the literature that explicitly addresses variation, cannot be used to ground such an account. Instead, I propose a view on which explaining individual differences involves identifying causes that could be intervened on to reduce the variability in the population. This account provides criteria of success for explaining variation and deepens our understanding of causal explanation

    Accurate Colour Reproduction of Human Face using 3D Printing Technology

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    The colour of the face is one of the most significant factors in appearance and perception of an individual. With the rapid development of colour 3D printing technology and 3D imaging acquisition techniques, it is possible to achieve skin colour reproduction with the application of colour management. However, due to the complicated skin structure with uneven and non-uniform surface, it is challenging to obtain accurate skin colour appearance and reproduce it faithfully using 3D colour printers. The aim of this study was to improve the colour reproduction accuracy of the human face using 3D printing technology. A workflow of 3D colour image reproduction was developed, including 3D colour image acquisition, 3D model manipulation, colour management, colour 3D printing, postprocessing and colour reproduction evaluation. Most importantly, the colour characterisation methods for the 3D imaging system and the colour 3D printer were comprehensively investigated for achieving higher accuracy
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