{ "id": "1904.00275", "version": "v1", "published": "2019-03-30T19:27:33.000Z", "updated": "2019-03-30T19:27:33.000Z", "title": "Prediction Model for Semitransparent Watercolor Pigment Mixtures Using Deep Learning with a Dataset of Transmittance and Reflectance", "authors": [ "Mei-Yun Chen", "Ya-Bo Huang", "Sheng-Ping Chang", "Ming Ouhyoung" ], "comment": "26 pages and 25 figures", "categories": [ "cs.LG", "cs.GR", "stat.ML" ], "abstract": "Learning color mixing is difficult for novice painters. In order to support novice painters in learning color mixing, we propose a prediction model for semitransparent pigment mixtures and use its prediction results to create a Smart Palette system. Such a system is constructed by first building a watercolor dataset with two types of color mixing data, indicated by transmittance and reflectance: incrementation of the same primary pigment and a mixture of two different pigments. Next, we apply the collected data to a deep neural network to train a model for predicting the results of semitransparent pigment mixtures. Finally, we constructed a Smart Palette that provides easily-followable instructions on mixing a target color with two primary pigments in real life: when users pick a pixel, an RGB color, from an image, the system returns its mixing recipe which indicates the two primary pigments being used and their quantities.", "revisions": [ { "version": "v1", "updated": "2019-03-30T19:27:33.000Z" } ], "analyses": { "subjects": [ "97R60" ], "keywords": [ "semitransparent watercolor pigment mixtures", "prediction model", "primary pigment", "deep learning", "semitransparent pigment mixtures" ], "note": { "typesetting": "TeX", "pages": 26, "language": "en", "license": "arXiv", "status": "editable" } } }