Improving segmentation results of 3D meshes representing obese human body silhouettes with transfer learning technique

<p>The latest and most refined segmentation methods of 3D meshes representing human body silhouettes utilize a machine-learning approach. State-of-the-art models trained on broad datasets perform at a very high accuracy level and robustness, which is satisfactory for most applications in animation, medicine or the garment industry. However, for body shapes that vary from standard silhouette, results tend to be lower, especially for obese subjects with crotch, armpits and thigh areas that are difficult to analyse. The paper presents a process of preparing an additional dataset and labelling segments to increase the effectiveness of correct segmentation for these types of silhouettes. The authors provide a transfer learning technique for a convolutional neural network model benefiting from mapping 3D objects onto two-dimensional space that allows significant enhancement by over a 3-point percentage of mean accuracy. The outcomes surpass general methods, exceeding 96,56% of points correctly classified with a distinctively visible improvement in problematic areas.</p>