https://arxiv.org/api/1385T0iHuQMzMGO5oKi8f9Qecn8 2026-09-18T21:02:01Z 25 40 0 http://arxiv.org/abs/2606.27604v1 2026-06-25T23:30:57Z

In this paper we present a spectral optical inversion for skin for path tracing-based rendering of subsurface scattering. Skin is a complex multilayered medium, with appearance determined by the mixture of biophysical chromophores. However, current methods rely on medium homogeneization, with optical parameters obtained via albedo inversion from a reflectance texture and hand-tuned scattering distance and anisotropy. This results into significant art-skilled manual labor for authoring, and an inaccurate scattering profile for skin. To solve these problems, we generalize existing albedo inversion techniques, and propose a framework that predicts full-spectral skin scattering parameters from a single RGB diffuse albedo. Our method builds upon a new mixture-of-media representation, that approximates the aggregated multilayered appearance of skin by mixing the aggregated scattering of three uncorrelated media. We train a chained neural decoder that maps RGB diffuse albedo to the optical properties of the mixture of media, including anisotropy, scattering radius and scattering albedo. Then, we show this mixture can be used in a random-walk-based path tracer with minimal modifications, by simply randomly selecting the medium to traverse.

2026-06-25T23:30:57Z 14 pages, 9 figures Carlos Aliaga Adrian Jarabo http://arxiv.org/abs/2604.13131v1 2026-04-14T00:12:01Z

Satellite sea surface temperature (SST) products underpin global coral bleaching monitoring, yet they measure only the ocean skin. Corals inhabit depths from the shallows to beyond 20 metres, where temperatures can be 1-3°C cooler than the surface; applying satellite SST uniformly to all depths therefore overestimates subsurface thermal stress. We present a physics-informed neural network (PINN) that fuses NOAA Coral Reef Watch SST with sparse in-situ temperature loggers within the one-dimensional vertical heat equation, enforcing SST as a hard surface boundary condition and jointly learning effective thermal diffusivity (\k{appa}) and light attenuation (Kd). Validated across four Great Barrier Reef sites (30 holdout experiments), the PINN achieves 0.25-1.38°C RMSE at unseen depths. Under extreme sparsity (three training depths), the PINN maintains 0.27°C RMSE at the 5 metre holdout and 0.32°C at the 9.1 metre holdout, where statistical baselines collapse to >1.8°C; it outperforms a physics-only finite-difference baseline in 90% of experiments. Depth-resolved Degree Heating Day (DHD) profiles show that thermal stress attenuates with depth: at Davies Reef, DHD drops from 0.29 at the surface to zero by 10.7 metres, consistent with logger observations, while satellite DHD remains constant at 0.31 across all depths. However, the PINN underestimates absolute DHD at shallow depths because its smooth predictions attenuate the short-duration peaks that drive threshold exceedances; PINN DHD values should be interpreted as conservative lower bounds on depth-resolved stress. These results demonstrate that physics-constrained fusion of satellite SST with sparse loggers can extend bleaching assessment to the depth dimension using existing observational infrastructure.

2026-04-14T00:12:01Z 23 pages, 7 figures, submitted to Remote Sensing of Environment Alzayat Saleh Mostafa Rahimi Azghadi http://arxiv.org/abs/2601.12020v1 2026-01-17T12:02:14Z

Subsurface scattering (SSS) gives translucent materials -- such as wax, jade, marble, and skin -- their characteristic soft shadows, color bleeding, and diffuse glow. Modeling these effects in neural rendering remains challenging due to complex light transport and the need for densely captured multi-view, multi-light datasets (often more than 100 views and 112 OLATs). We present DIAMOND-SSS, a data-efficient framework for high-fidelity translucent reconstruction from extremely sparse supervision -- even as few as ten images. We fine-tune diffusion models for novel-view synthesis and relighting, conditioned on estimated geometry and trained on less than 7 percent of the dataset, producing photorealistic augmentations that can replace up to 95 percent of missing captures. To stabilize reconstruction under sparse or synthetic supervision, we introduce illumination-independent geometric priors: a multi-view silhouette consistency loss and a multi-view depth consistency loss. Across all sparsity regimes, DIAMOND-SSS achieves state-of-the-art quality in relightable Gaussian rendering, reducing real capture requirements by up to 90 percent compared to SSS-3DGS.

2026-01-17T12:02:14Z Guillermo Figueroa-Araneda Iris Diana Jimenez Florian Hofherr Manny Ko Hector Andrade-Loarca Daniel Cremers http://arxiv.org/abs/2512.24820v2 2026-08-31T22:55:44Z

Nanoscale mechano-optical surfaces enable electronics-free strain sensing, attractive for skin-interfaced devices, yet reported implementations require laser/spectrometer interrogation, negating this advantage. Here, we report electrically passive mechanical transduction of arterial pulsation into diffractive colour shifts read by unmodified smartphone cameras, enabled by a dual-function monolithic poly-dimethylsiloxane (PDMS) film. Using large-area double-sided nanoimprinting, we achieve a strain-sensitive nanophotonic surface on one face of the film and a bio-inspired 3D structural adhesive on the other. We measure strain-dependent optical response and reproduce it in colour-mixing optical simulations. In uniaxial cyclic loading tests, 2% strain produces a 9% RGB-intensity modulation, stable over 1000 cycles. Further, 3D structuring improves adhesive shear strength by 65% on skin over flat PDMS. Hand-held smartphone recordings in humans ($n=13$) resolve sub-beat hemodynamics in agreement with clinical reference (per-beat waveform $ρ=0.94 \pm 0.03$), exceeding established non-invasive techniques such as active reflectance photoplethysmography (PPG), imaging PPG, and piezoelectric pulse-force sensors in simultaneous recordings. Importantly, mechanical transduction at the elastomer--air interface presents an optical cardiovascular monitoring approach agnostic to dermal-melanin, a known PPG confounder. Together, these advances establish camera-readable mechanochromic elastomers as versatile materials platforms for wearable cardiovascular monitoring, point-of-care diagnostics, and electronics-free human-machine interfaces.

2025-12-31T12:14:24Z Advanced Materials (2026), e74512 Torjus Steffensen Vegar Stubberud Emilie S. Hennisen Eryk T. Winogrocki Julia Lövgren Arthur G. S. Torvund Håvard Vestad Nils K. Skjærvold Martin Steinert Angelos Xomalis 10.1002/adma.74512 http://arxiv.org/abs/2512.21988v4 2026-07-02T11:21:01Z

Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitative multi-device benchmarks remain scarce. Materials and Methods: We analyzed matched facial images from 965 Korean subjects captured by a digital single-lens reflex (DSLR) camera, a consumer tablet, and a consumer smartphone, and evaluated two calibration methods against the DSLR reference. The methods are standard global linear Color Correction Matrix (CCM) normalization and region-specific CCM trained per anatomical region, both applied in Commission Internationale de l'Eclairage Lab* (CIELAB) space. Results: Linear CCM reduced inter-device color differences by 61-74% and placed both Melanin Index (intraclass correlation coefficient [ICC] = 0.80) and Individual Typology Angle (ITA, ICC = 0.78) in the good reliability band. Region-specific CCM raised both indices into the excellent reliability band (MI ICC = 0.95, ITA ICC = 0.93), with anatomical region exceeding the source device as the largest pre-calibration variance contributor (analysis-of-variance $η^2 = 0.18$ versus 0.12). Conclusion: Consumer-device skin colorimetry therefore achieves clinically useful inter-device reliability using standard calibration, with region-aware calibration the largest remaining source of improvement.

2025-12-26T11:19:25Z Sungwoo Kang Jong-Kook Kim http://arxiv.org/abs/2509.12655v1 2025-09-16T04:26:24Z

Cancer is a complex sequence of disease conditions progressing gradually with generalized loss of growth control. It continues to be one of the biggest global health problems, and its etiology has given rise to a huge array of treatment outside of conventional chemotherapy. Melanoma is one of the deadliest forms of skin cancer originating from melanocytes. It is characterized by the overproduction of melanin by the increased in cell proliferation. Melanogenesis, the production of melanin is by melanocyte-stimulating hormone (MSH) which stimulates cyclic AMP (cAMP) production to increase melanocyte production. Through the use of methylxanthines, theophylline proliferation rate can be decreased by increasing cell differentiation. One of the basic principles of cell biology is the selectivity of differentiation and proliferation, where cells usually grow or differentiate but not both. This study aimed to collect baseline data of untreated B16-F10 melanoma cells to determine morphology and doubling time of untreated cells. This data was further compared to results from varied concentration treatments of theophylline. It was hypothesized that increased levels in cyclic adenosine monophosphate (cAMP) could induce differentiation of melanocytes to terminate proliferation. To test this hypothesis, we collected a series of images of B16- F10 derived from mouse melanoma cells and seeded cells into a 12-well plate to calculate cell concentration for a five-day period and doubling time for untreated cells. Theophylline levels were varied to stimulate the production of cAMP and determine its effect on melanocyte proliferation and differentiation. The observed results showed that intracellular stabilization of cAMP, via phosphodiesterase inhibition by methylxanthines like theophylline, increases cell differentiation on melanoma cells and suppresses their growth.

2025-09-16T04:26:24Z Faiza Hasib Khan Megan Robles-Artega http://arxiv.org/abs/2507.18385v1 2025-07-24T12:59:42Z

Full-body Human inverse rendering based on physically-based rendering aims to acquire high-quality materials, which helps achieve photo-realistic rendering under arbitrary illuminations. This task requires estimating multiple material maps and usually relies on the constraint of rendering result. The absence of constraints on the material maps makes inverse rendering an ill-posed task. Previous works alleviated this problem by building material dataset for training, but their simplified material data and rendering equation lead to rendering results with limited realism, especially that of skin. To further alleviate this problem, we construct a higher-quality dataset (OpenHumanBRDF) based on scanned real data and statistical material data. In addition to the normal, diffuse albedo, roughness, specular albedo, we produce displacement and subsurface scattering to enhance the realism of rendering results, especially for the skin. With the increase in prediction tasks for more materials, using an end-to-end model as in the previous work struggles to balance the importance among various material maps, and leads to model underfitting. Therefore, we design a model (HumanMaterial) with progressive training strategy to make full use of the supervision information of the material maps and improve the performance of material estimation. HumanMaterial first obtain the initial material results via three prior models, and then refine the results by a finetuning model. Prior models estimate different material maps, and each map has different significance for rendering results. Thus, we design a Controlled PBR Rendering (CPR) loss, which enhances the importance of the materials to be optimized during the training of prior models. Extensive experiments on OpenHumanBRDF dataset and real data demonstrate that our method achieves state-of-the-art performance.

2025-07-24T12:59:42Z 14 Yu Jiang Jiahao Xia Jiongming Qin Yusen Wang Tuo Cao Chunxia Xiao http://arxiv.org/abs/2507.12961v1 2025-07-17T10:00:07Z

Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recently introduced to advance research in biomedical imaging and includes DermaMNIST, a dataset for classifying pigmented lesions based on the HAM10000 dataset. This study assesses ResNet-50 and EfficientNetV2L models for multi-class classification using DermaMNIST, employing transfer learning and various layer configurations. One configuration achieves results that match or surpass existing methods. This study suggests that convolutional neural networks (CNNs) can drive progress in biomedical image analysis, significantly enhancing diagnostic accuracy.

2025-07-17T10:00:07Z Nerma Kadric Amila Akagic Medina Kapo http://arxiv.org/abs/2504.04494v2 2025-06-25T07:32:50Z

This paper presents a comprehensive evaluation of skin color measurement methods from dermatoscopic images using a synthetic dataset (S-SYNTH) with controlled ground-truth melanin content, lesion shapes, hair models, and 18 distinct lighting conditions. This allows for rigorous assessment of the robustness and invariance to lighting conditions. We assess four classes of image colorimetry approaches: segmentation-based, patch-based, color quantization, and neural networks. We use these methods to estimate the Individual Typology Angle (ITA) and Fitzpatrick types from dermatoscopic images. Our results show that segmentation-based and color quantization methods yield robust, lighting-invariant estimates, whereas patch-based approaches exhibit significant lighting-dependent biases that require calibration. Furthermore, neural network models, particularly when combined with heavy blurring to reduce overfitting, can provide light-invariant Fitzpatrick predictions, although their generalization to real-world images remains unverified. We conclude with practical recommendations for designing fair and reliable skin color estimation methods.

2025-04-06T13:57:34Z Marin Benčević Robert Šojo Irena Galić http://arxiv.org/abs/2501.01980v1 2024-12-29T07:57:25Z

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.

2024-12-29T07:57:25Z ACM Transactions on Graphics 43, 6, Article 275 (December 2024) Hyunho Ha Inseung Hwang Nestor Monzon Jaemin Cho Donggun Kim Seung-Hwan Baek Adolfo Muñoz Diego Gutierrez Min H. Kim 10.1145/3687767 http://arxiv.org/abs/2408.13699v1 2024-08-25T01:29:06Z

Surgical scene understanding in Robot-assisted Minimally Invasive Surgery (RMIS) is highly reliant on visual cues and lacks tactile perception. Force-modulated surgical palpation with tactile feedback is necessary for localization, geometry/depth estimation, and dexterous exploration of abnormal stiff inclusions in subsurface tissue layers. Prior works explored surface-level tissue abnormalities or single layered tissue-tumor embeddings with more than 300 palpations for dense 2D stiffness mapping. Our approach focuses on 3D reconstructions of sub-dermal tumor surface profiles in multi-layered tissue (skin-fat-muscle) using a visually-guided novel tactile navigation policy. A robotic palpation probe with tri-axial force sensing was leveraged for tactile exploration of the phantom. From a surface mesh of the surgical region initialized from a depth camera, the policy explores a surgeon's region of interest through palpation, sampled from bayesian optimization. Each palpation includes contour following using a contact-safe impedance controller to trace the sub-dermal tumor geometry, until the underlying tumor-tissue boundary is reached. Projections of these contour following palpation trajectories allows 3D reconstruction of the subdermal tumor surface profile in less than 100 palpations. Our approach generates high-fidelity 3D surface reconstructions of rigid tumor embeddings in tissue layers with isotropic elasticities, although soft tumor geometries are yet to be explored.

2024-08-25T01:29:06Z 8 pages, 6 figures, accepted to IROS 2024 Raghava Uppuluri Abhinaba Bhattacharjee Sohel Anwar Yu She http://arxiv.org/abs/2406.13227v1 2024-06-19T05:28:52Z

Face retouching aims to remove facial blemishes, such as pigmentation and acne, and still retain fine-grain texture details. Nevertheless, existing methods just remove the blemishes but focus little on realism of the intermediate process, limiting their use more to beautifying facial images on social media rather than being effective tools for simulating changes in facial pigmentation and ance. Motivated by this limitation, we propose our Controllable and Gradual Face Retouching (CGFR). Our CGFR is based on physical modelling, adopting Sum-of-Gaussians to approximate skin subsurface scattering in a decomposed melanin and haemoglobin color space. Our CGFR offers a user-friendly control over the facial blemishes, achieving realistic and gradual blemishes retouching. Experimental results based on actual clinical data shows that CGFR can realistically simulate the blemishes' gradual recovering process.

2024-06-19T05:28:52Z 7 pages, 6 figures. The paper has been accepted by the IEEE Conference on Multimedia Expo 2024 Chenhao Shuai Rizhao Cai Bandara Dissanayake Amanda Newman Dayan Guan Dennis Sng Ling Li Alex Kot http://arxiv.org/abs/2405.15284v1 2024-05-24T07:17:23Z

Modeling underground temperatures provides a practical application of the one-dimensional heat equation. In this work, the one-dimensional heat equation in surface soil is extended to include heat carried by the vertical flow of rainwater through the soil. Analytical solutions, with and without water flow, illustrate the influence of rainwater circulation on the sub-surface propagation of seasonal temperature variations, an important effect that is generally neglected in textbooks. The surface temperature variations are damped by the soil, and this effect was used by the Troglodytae in Egypt or the Petra in South Jordan to insulate against extreme temperatures. For a realistic case of horizontally layered geology, a finite difference Python code was developed for the same purpose. Subsurface temperatures were also measured over a full year at depths up to 1.8 m and used to estimate the thermal skin depth and thermal wavelength. This study provides students with a practical example of how a textbook physics problem can be modified to extract information of contemporary importance in geophysics and global warming.

2024-05-24T07:17:23Z Mathis Caprais Oriane Shviro Ugo Pensec Hermann Zeyen 10.1119/5.0196139 http://arxiv.org/abs/2404.00552v3 2024-05-07T05:32:11Z

Decomposition of skin pigment plays an important role in medical fields. Human skin can be decomposed into two primitive components, hemoglobin and melanin. It is our goal to apply these results for diagnosis of skin cancer. In this paper, various methods for skin pigment decomposition are reviewed comparatively and the performance of each method is evaluated both theoretically and experimentally. In addition, isometric feature mapping (Isomap) is introduced in order to improve the dimensionality reduction performance in context of skin pigment decomposition.

2024-03-31T03:53:45Z 5 pages, 7 figures Hao Gong Michel Desvignes http://arxiv.org/abs/2402.01952v1 2024-02-02T23:17:15Z

We present best-fit values of porosity -- and the corresponding effective thermal inertiae -- determined from three different depths in Europa's near-subsurface (~1-20 cm). The porosity of the upper ~20 cm of Europa's subsurface varies between 75-50% ($Γ_{eff}\approx50-140$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) on the leading hemisphere and 50-40% ($Γ_{eff}\approx140-180$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) on the trailing hemisphere. Residual maps produced by comparison with these models reveal thermally anomalous features that cannot be reproduced by globally homogeneous porosity models. These regions are compared to Europa's surface terrain and known compositional variations. We find that some instances of warm thermal anomalies are co-located with known geographical or compositional features on both the leading and trailing hemisphere; cool temperature anomalies are well correlated with surfaces previously observed to contain pure, crystalline water ice and the expansive rays of Pwyll crater. Anomalous regions correspond to locations with subsurface properties different from those of our best-fit models, such as potentially elevated thermal inertia, decreased emissivity, or more porous regolith. We also find that ALMA observations at ~3 mm sound below the thermal skin depth of Europa (~10-15 cm) for a range of porosity values, and thus do not exhibit features indicative of diurnal variability or residuals similar to other frequency bands. Future observations of Europa at higher angular resolution may reveal additional locations of variable subsurface thermophysical properties, while those at other wavelengths will inform our understanding of the regolith compaction length and the effects of external processes on the shallow subsurface.

2024-02-02T23:17:15Z Accepted for publication in the Planetary Science Journal on 01/31/2024. 34 pages, 10 Figures, 4 tables A. E. Thelen K. de Kleer M. Camarca A. Akins M. Gurwell B. Butler I. de Pater http://arxiv.org/abs/2401.08459v1 2024-01-16T16:08:20Z

In the current paper, we report the results of surface modification of cubic Y2O3 films employing carbon-ion implantation. The characterization results demonstrate the formation of a stable yttrium oxalate-based structure with cavities filled with carbon clusters. Theoretical simulations demonstrate that the incorporation of eighteen-atom carbon clusters into the cavities of Y2(C2O4)3 does not lead to valuable changes in the crystal structure of yttrium oxalate. X-ray diffraction and optical measurements demonstrate that the subsurface bulk area of cubic yttrium oxide remains unperturbed. The oxalate "skin" thickness with embedded carbon clusters is estimated to be approximately 10 nm. The prospective employing the method to manage optical properties and increase the biocompatibility of yttria and lanthanide oxides are discussed.

2024-01-16T16:08:20Z 20 pages, 5 figures, accepted to the Materials Chemistry and Physics journal D. W. Boukhvalov D. A. Zatsepin D. Yu. Biryukov Yu. V. Shchapova N. V. Gavrilov A. F. Zatsepin http://arxiv.org/abs/2312.11587v2 2024-07-26T13:16:28Z

Creating a controllable and relightable digital avatar from multi-view video with fixed illumination is a very challenging problem since humans are highly articulated, creating pose-dependent appearance effects, and skin as well as clothing require space-varying BRDF modeling. Existing works on creating animatible avatars either do not focus on relighting at all, require controlled illumination setups, or try to recover a relightable avatar from very low cost setups, i.e. a single RGB video, at the cost of severely limited result quality, e.g. shadows not even being modeled. To address this, we propose Relightable Neural Actor, a new video-based method for learning a pose-driven neural human model that can be relighted, allows appearance editing, and models pose-dependent effects such as wrinkles and self-shadows. Importantly, for training, our method solely requires a multi-view recording of the human under a known, but static lighting condition. To tackle this challenging problem, we leverage an implicit geometry representation of the actor with a drivable density field that models pose-dependent deformations and derive a dynamic mapping between 3D and UV spaces, where normal, visibility, and materials are effectively encoded. To evaluate our approach in real-world scenarios, we collect a new dataset with four identities recorded under different light conditions, indoors and outdoors, providing the first benchmark of its kind for human relighting, and demonstrating state-of-the-art relighting results for novel human poses.

2023-12-18T14:30:13Z Accepted to ECCV 2024. Project page: https://vcai.mpi-inf.mpg.de/projects/RNA/ Diogo Luvizon Vladislav Golyanik Adam Kortylewski Marc Habermann Christian Theobalt http://arxiv.org/abs/2312.06085v2 2025-03-15T07:51:12Z

In this study, we introduce a novel two-stage technique for decomposing and reconstructing facial features from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual. To synthesize 3D facial models more realistically, we endeavor to decouple key facial attributes from the RGB color, including geometry, diffuse reflectance, and specular reflectance. Specifically, we design a Sparse-view Face Decomposition Model (SFDM): 1) In the first stage, we create a general facial template from a wide array of individual faces, encapsulating essential geometric and reflectance characteristics. 2) Guided by this template, we refine a specific facial model for each individual in the second stage, considering the interaction between geometry and reflectance, as well as the effects of subsurface scattering on the skin. With these advances, our method can reconstruct high-quality facial representations from as few as three images. The comprehensive evaluation and comparison reveal that our approach outperforms existing methods by effectively disentangling geometric and reflectance components, significantly enhancing the quality of synthesized novel views, and paving the way for applications in facial relighting and reflectance editing.

2023-12-11T03:14:58Z CVPR25 Daisheng Jin Jiangbei Hu Baixin Xu Yuxin Dai Chen Qian Ying He http://arxiv.org/abs/2310.17911v1 2023-10-27T06:10:35Z

We introduce Hyper-Skin, a hyperspectral dataset covering wide range of wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR) spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial skin-spectra reconstruction. By reconstructing skin spectra from RGB images, our dataset enables the study of hyperspectral skin analysis, such as melanin and hemoglobin concentrations, directly on the consumer device. Overcoming limitations of existing datasets, Hyper-Skin consists of diverse facial skin data collected with a pushbroom hyperspectral camera. With 330 hyperspectral cubes from 51 subjects, the dataset covers the facial skin from different angles and facial poses. Each hyperspectral cube has dimensions of 1024$\times$1024$\times$448, resulting in millions of spectra vectors per image. The dataset, carefully curated in adherence to ethical guidelines, includes paired hyperspectral images and synthetic RGB images generated using real camera responses. We demonstrate the efficacy of our dataset by showcasing skin spectra reconstruction using state-of-the-art models on 31 bands of hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin dataset would be a valuable resource to NeurIPS community, encouraging the development of novel algorithms for skin spectral reconstruction while fostering interdisciplinary collaboration in hyperspectral skin analysis related to cosmetology and skin's well-being. Instructions to request the data and the related benchmarking codes are publicly available at: \url{https://github.com/hyperspectral-skin/Hyper-Skin-2023}.

2023-10-27T06:10:35Z Skin spectral dataset Pai Chet Ng Zhixiang Chi Yannick Verdie Juwei Lu Konstantinos N. Plataniotis http://arxiv.org/abs/2309.12287v2 2023-10-02T09:31:56Z

In this article, we propose a new BSS approach for identifying skin diseases from RGB images that proceeds in two steps. We begin by separating the three main chromophores (oxyhemoglobin, deoxyhemoglobin and melanin) using Non-negative Matrix Factorization (NMF). For this purpose, we propose a special initialization of the solution matrices based on the sparsity of the chromophores, instead of initializing them with random matrices as is the case for basic versions of NMF. We then propose a new disease identification criterion that exploits the three contributions of each chromophore on the three spectral bands of our RGB dermatological image. To validate our approach, we used an open access database containing RGB images of melanoma and neavus. The results obtained showed good performance for our approach in terms of chromophore separation, compared to the most commonly used method in the literature, as well as disease identification compared to identification based on the most popular criterion.

2023-09-21T17:50:57Z There are a few errors in handwritten latex Mustapha Zokay Hicham Saylani http://arxiv.org/abs/2309.12274v1 2023-09-21T17:31:59Z

In this paper we propose a new approach to identify melanoma diseases by identifying the distribution of its main skin chromophores (melanin, oxyhemoglobin and deoxyhemoglobin) from multispectral dermatological images. Based on Blind Source Separation (BSS), our approach takes into account the shading present in most of the images. Assuming that the multispectral images have at least 4 spectral bands, it allows to estimate the distribution of each chromophore in addition to the shading without any a priori information, contrary to all existing methods that use 3 bands, i.e. RGB images. Indeed, the fact of neglecting the shading degrades their performance. To validate our method, we used a database of real multispectral dermatological images of skin affected by melanoma cancer. To measure our performance, in addition to the classical criterion of visually analyzing the estimated distributions with referring to the physiological knowledge of the disease, we proposed a new criterion that is based on our independence hypothesis. Using these two criteria, we could see that our approach is very efficient for the identification of melanoma.

2023-09-21T17:31:59Z Mustapha Zokay Hicham Saylani http://arxiv.org/abs/2304.05472v1 2023-04-11T19:54:50Z

Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural rendering, we want to develop a physically-based neural shader to eliminate device dependency and significantly boost performance. However, no existing lighting and material models in the current neural rendering approaches can accurately represent the comprehensive lighting models and BRDFs properties required by the PBR process. Thus, this paper proposes a novel lighting representation that models direct and indirect light locally through a light sampling strategy in a learned light sampling field. We also propose BRDF models to separately represent surface/subsurface scattering details to enable complex objects such as translucent material (i.e., skin, jade). We then implement our proposed representations with an end-to-end physically-based neural face skin shader, which takes a standard face asset (i.e., geometry, albedo map, and normal map) and an HDRI for illumination as inputs and generates a photo-realistic rendering as output. Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.

2023-04-11T19:54:50Z ICLR 2023 Poster Jing Yang Hanyuan Xiao Wenbin Teng Yunxuan Cai Yajie Zhao http://arxiv.org/abs/2210.04301v1 2022-10-09T16:52:19Z

Purpose: Visualization and monitoring of capillary loops in dermis and mucosa are interesting for various clinical applications, including rheumatology, early cancer, and shock detection. However, the limitations of existing imaging technologies are not well understood. Therefore, this study aimed to elucidate peculiarities of the subsurface defect visualization in realistic skin imaging geometries. Methods: We used a perturbation approach for the light propagation in turbid tissues with mismatched boundaries. Defects were considered as negative light sources immersed in homogeneous media, which was described using diffuse approximation. The contrast ratio was used as an image quality metric. Results: We have developed the single point subsurface defect model and extended it to horizontally- and vertically-arranged linear inhomogeneities. In particular, we have obtained explicit analytical expressions for the single point defect and the infinite linear defect buried at a certain depth (horizontally-arranged), which allows direct experimental verification. Conclusions: The developed approach can be used for quick rough estimates while designing and optimizing imaging systems.

2022-10-09T16:52:19Z Gennadi Saiko Alexandre Douplik http://arxiv.org/abs/2209.12208v1 2022-09-25T12:31:40Z

The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition Systems (AFRSs) are possible if the depth information can be fully utilized. However, in existing studies, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction based on depth information are treated as two independent branches, resulting in high computation and complexity of AFRS building.Thus, this paper proposes a uniform representation model for OCT-based fingerprint PAD and subsurface fingerprint reconstruction. Firstly, we design a novel semantic segmentation network which only trained by real finger slices of OCT-based fingerprints to extract multiple subsurface structures from those slices (also known as B-scans). The latent codes derived from the network are directly used to effectively detect the PA since they contain abundant subsurface biological information, which is independent with PA materials and has strong robustness for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to reconstruct multiple subsurface 2D fingerprints. Recognition can be easily achieved by using existing mature technologies based on traditional 2D fingerprints. Extensive experiments are carried on our own established database, which is the largest public OCT-based fingerprint database with 2449 volumes. In PAD task, our method can improve 0.33% Acc from the state-of-the-art method. For reconstruction performance, our method achieves the best performance with 0.834 mIOU and 0.937 PA. By comparing with the recognition performance on surface 2D fingerprints, the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved.

2022-09-25T12:31:40Z 13 pages, 8 figures Wentian Zhang Haozhe Liu Feng Liu Raghavendra Ramachandra http://arxiv.org/abs/2204.07166v1 2022-04-14T07:07:41Z

Currently, free flaps and pedicled flaps are assessed for reperfusion in post-operative care using colour, capillary refill, temperature, texture and Doppler signal (if available). While these techniques are effective, they are prone to error due to their qualitative nature. In this research, we explore using different wavelengths of light to quantify the response of ischaemic tissue. The assessment provides us with indicators that are key to our goal of developing a point-of-care diagnostics device, capable of observing reduced perfusion quantitatively. We set up a detailed optical model of the layers of the skin. The layers of the model are given appropriate optical properties of the tissue, with due consideration of melanin and haemoglobin concentrations. We simulate 24 models of healthy, perfused tissue and perfusion-deprived tissue to assess the responses when illuminated with visible and near-infrared wavelengths of light. In addition to detailed fluence maps of photon propagation, we propose a simple mathematical model to assess the differential propagation of photons in tissue; the optical reperfusion factor (ORF). Our results show clear advantages of using light at longer wavelengths (red, near-infrared) and the inferences drawn from the simulations hold significant clinical relevance. The simulated scenarios and results consolidate the belief of a multi-wavelength, point-of-care diagnostics device and inform its design for quantifying blood flow in transplanted tissue. The modelling approach is applicable beyond the current research, wherein other medical conditions that can be mathematically represented in the skin can be investigated. Through these, additional inferences and approaches to other point-of-care devices can be realised.

2022-04-14T07:07:41Z 2 figures, 4 supplementary tables, 2 supplementary figures Mark Main Richard JJ Pilkington Graham M Gibson Akhil Kallepalli 10.1016/j.bjoms.2022.03.004