scholarly journals 3D Skin Texture Analysis: A Neural Network and Photometric Stereo Perspective

2012 ◽  
Author(s):  
Shahzad Anwar ◽  
Lyndon N. Smith ◽  
Melvyn L. Smith
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Photometric Stereo ◽  
Skin Texture ◽  
3D Skin

Machine vision 3D skin texture analysis for detection of melanoma

Sensor Review ◽  
2011 ◽  
Vol 31 (2) ◽  
pp. 111-119 ◽  
Author(s):  
L.N. Smith ◽  
M.L. Smith ◽  
A.R. Farooq ◽  
J. Sun ◽  
Y. Ding ◽  
...  
Keyword(s):  
Machine Vision ◽  
Texture Analysis ◽  
Skin Texture ◽  
3D Skin

Image Texture Analysis based on Color Level Methods using Neural Network

2020 ELEKTRO ◽  
2020 ◽  
Author(s):  
Miroslav Benco ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Martina Radilova ◽  
Peter Sykora
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis

scholarly journals Pay Attention to Devils: A Photometric Stereo Network for Better Details

2020 ◽  
Author(s):  
Yakun Ju ◽  
Kin-Man Lam ◽  
Yang Chen ◽  
Lin Qi ◽  
Junyu Dong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Photometric Stereo ◽  
Normal Map ◽  
Surface Normal ◽  
3D Surface ◽  
Surface Reconstructions ◽  
Higher Weights

We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.

Identification of Wool and Cashmere Based on Texture Analysis

2015 ◽  
Vol 671 ◽  
pp. 385-390 ◽  
Author(s):  
Sen Lin Yuan ◽  
Kai Lu ◽  
Yue Qi Zhong
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Texture Analysis ◽  
Texture Feature ◽  
Digital Camera ◽  
Texture Image ◽  
Feasible Method ◽  
Microscopic Images ◽  
Texture Parameters ◽  
Network Experiment

In order to separate wool from cashmere efficiently, an identification method based on texture analysis was proposed in this paper. The microscopic images captured by CCD digital camera were preprocessed as the texture image. Improved Tamura texture feature were employed to analyzing the final texture images and to attaining the texture parameters. Through a large number of samples, the mathematical modeling was completed by using neural network. Experiment results indicate that texture analysis can be a feasible method to identify cashmere and wool.

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