Comparison between EM and FCM algorithms in skin tone extraction
This study aims to investigate implementing EM and FCM algorithms for skin color extraction. The capabilities of three well-known color spaces, namely, RGB, HSV, and YCbCr for skin-tone extraction are assessed by using statistical modeling of skin tones using EM and FCM algorithms. The results show that utilizing a Gaussian mixture model for parametric modeling of skin tones using EM algorithm works well in HSV color space when all three components of the color vector are used. In spite of discarding the luminance components in YCbCr and HSV color spaces, EM algorithm provides the best results. The results of the detailed comparisons are explained in the conclusion.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
True to Tone? Quantifying Skin Tone Fidelity and Bias in Photographic-to-Virtual Human Pipelines
Accurate reproduction of facial skin tone is essential for realism, identity preservation, and fairness in Virtual Human (VH) rendering. However, most accessible avatar creation pipelines rely on photographic inputs that…
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
Access to dermatological care is a major issue, with an estimated 3 billion people lacking access to care globally. Artificial intelligence (AI) may aid in triaging skin diseases. However, most AI models have not been ri…
Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed befor…
Lesion ClassificationSkin Lesion ClassificationColors Matter: AI-Driven Exploration of Human Feature Colors
This study presents a robust framework that leverages advanced imaging techniques and machine learning for feature extraction and classification of key human attributes-namely skin tone, hair color, iris color, and vein-…
ClassificationFace DetectionDDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In Machine-Assisted Skin Disease Detection
Skin tone as a demographic bias and inconsistent human labeling poses challenges in dermatology AI. We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease…
image-classificationImage Classification