We have located links that may give you full text access.
Novel digital image analysis using fractal dimension for assessment of skin radiance.
Skin Research and Technology 2019 Februrary 17
BACKGROUND: Despite a strong desire to quantify skin radiance in the field of cosmetics, there does not exist a robust method to characterize it. Classical shine that quantifies the specular reflection from skin has been commonly used as the metric to characterize radiance. However, it does not always correlate with the perceived radiance as there are many other parameters that inform radiance perception including spatial distribution of shine and color homogeneity.
MATERIALS AND METHODS: In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks.
RESULTS: Our results confirm a strong correlation (R2 = 0.99) between the expert radiance rankings and the rankings by fractal dimension algorithm. The new algorithm offers an improved product differentiation compared with classical shine or color measurements.
CONCLUSION: Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.
MATERIALS AND METHODS: In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks.
RESULTS: Our results confirm a strong correlation (R2 = 0.99) between the expert radiance rankings and the rankings by fractal dimension algorithm. The new algorithm offers an improved product differentiation compared with classical shine or color measurements.
CONCLUSION: Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app