Assessment of digital image correlation measurement errors: methodology and results

Optical full-field measurement methods such as Digital Image Correlation (DIC) are increasingly used in the field of experimental mechanics, but they still suffer from a lack of information about their metrological performances. To assess the performance of DIC techniques and give some practical rules for users, a collaborative work has been carried out by the Workgroup “Metrology” of the French CNRS research network 2519 “MCIMS (Mesures de Champs et Identification en Mécanique des Solides / Full-field measurement and identification in solid mechanics, http://www.ifma.fr/lami/gdr2519)”. A methodology is proposed to assess the metrological performances of the image processing algorithms that constitute their main component, the knowledge of which being required for a global assessment of the whole measurement system. The study is based on displacement error assessment from synthetic speckle images. Series of synthetic reference and deformed images with random patterns have been generated, assuming a sinusoidal displacement field with various frequencies and amplitudes. Displacements are evaluated by several DIC packages based on various formulations and used in the French community. Evaluated displacements are compared with the exact imposed values and errors are statistically analyzed. Results show general trends rather independent of the implementations but strongly correlated with the assumptions of the underlying algorithms. Various error regimes are identified, for which the dependence of the uncertainty with the parameters of the algorithms, such as subset size, gray level interpolation or shape functions, is discussed

Customizing Experiences for Mobile Virtual Reality

A criação manual de conteúdo para um jogo é um processo demorado e trabalhoso que requer um conjunto de habilidades diversi cado (normalmente designers, artistas e programadores) e a gestão de diferentes recursos (hardware e software especializados). Dado que o orçamento, tempo e recursos são frequentemente muito limitados, os projetos poderiam bene ciar de uma solução que permitisse poupar e investir noutros aspectos do desenvolvimento. No contexto desta tese, abordamos este desa o sugerindo a criação de pacotes especí cos para a geração de conteúdo per sonalizável, focados em aplicações de Realidade Virtual (RV) móveis. Esta abordagem divide o problema numa solução com duas facetas: em primeiro lugar, a Geração Procedural de Conteúdo, alcançada através de métodos convencionais e pela utilização inovadora de Grandes Modelos de Lin guagem (normalmente conhecidos por Large Language Models). Em segundo lugar, a Co-Criação de Conteúdo, que enfatiza o desenvolvimento colaborativo de conteúdo. Adicionalmente, dado que este trabalho se foca na compatibilidade com RV móvel, as limitações de hardware associadas a capacetes de RV autónomos (standalone VR Headsets) e formas de as ultrapassar são também abordadas. O conteúdo será gerado utilizando métodos actuais em geração procedural e facilitando a co-criação de conteúdo pelo utilizador. A utilização de ambas estas abordagens resulta em ambi entes, objectivos e conteúdo geral mais re-jogáveis com muito menos desenho. Esta abordagem está actualmente a ser aplicada no desenvolvimento de duas aplicações de RV distintas. A primeira, AViR, destina-se a oferecer apoio psicológico a indivíduos após a perda de uma gravidez. A se gunda, EmotionalVRSystem, visa medir as variações nas respostas emocionais dos participantes induzidas por alterações no ambiente, utilizando tecnologia EEG para leituras precisas

A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required