35 skills found · Page 2 of 2
Solido / MixboxMixbox is a library for natural color mixing based on real pigments.
MarPolar / CHEMTAXChemtax analysis for HPLC photosynthetic pigment data
JulienPradet / Pigment StorePigment Store is a styleguide generator that works as a living documentation for React Components. It also aims at providing automatic visual testing.
oldandangry / MegajuicerFilm‑Inspired Subtractive Split Toning DCTL for DaVinci Resolve — A scene‑referred, wide‑gamut split‑toning effect that uses OKLab balance masks, physically‑accurate Beer–Lambert dye attenuation, and Kubelka–Munk‑inspired pigment mixing for realistic, density‑dependent hue separation.
abe33 / PigmentsA color model for atom packages
NIH-NEI / REShAPEImage Analysis and Cell Morphometry Measurements on Retinal Pigment Epithelium cells.
Triginarsa / Skin CancerDPS 4B: Automated Diagnosis of Pigmented Skin Lesions With MOBILENET V.2
Mukulthakur17 / Skin Pigment AnalysisAnalyzing the human skin pigments to detect the disease which is causing that abnormality in the skin cells.
eoplus / Pigment RatiosThis repository documents available information on pigment ratios and pigment based groups within taxonomic groups, mainly to aid analysis of chemical taxonomy like CHEMTAX or BCE).
virtualritz / Pigment Mixing RsRust wrapper around Pigment-Mixing/Mixbox
alisonpchase / Rrs Inversion PigmentsMATLAB function to estimate phytoplankton pigments from remote-sensing reflectance
harvardartmuseums / ColorToSoundConvert pigment to pixel to sound
ChaoticByte / Fragmentedsee https://github.com/ChaoticByte/Pigment
refetaliyalcin / Effective Medium Theory Maxwell GarnettSolution of Maxwell Garnett effective medium theory. Takes volume fraction and refractive indices of pigment and host as inputs. Gives refractive index of the effective medium as the output
tanvisenjaliya / SkinCancerThe small size and lack of diversity of the available dataset of dermatoscopic pictures make it difficult to train neural networks for automated identification of pigmented skin lesions. The HAM10000 ("Human Against Machine with 10000 training images") dataset addresses this issue. It consist of dermatoscopic images from various populations, which were captured and preserved using various modalities. There are 10015 dermatoscopic images in the final dataset, which can be used as a training set for academic machine learning. The cases include a diverse range of pigmented lesions, including actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc). The dataset includes lesions with multiple images, which can be tracked by the lesion_id-column within the HAM10000_metadata file. The International Skin Imaging Collaboration (ISIC), a multinational partnership that has created the world's biggest public archive of dermoscopic pictures of skin, held the world's largest skin image analysis challenge. In 2018, the challenge was held in Granada, Spain, at the Medical Image Computing and Computer Assisted Intervention conference. Over 12,500 photos were included in the dataset, which was divided into three jobs. 900 individuals signed up for data download, with 115 completing the lesion segmentation job, 25 the lesion attribute detection task, and 159 the illness classification task
