Using graph cuts in GPUs for color based human skin segmentation
In this paper we propose a new method to deal with the problem of automatic human skin segmentation in RGB color space model. The problem is modeled as a minimum cost graph cut problem on a graph whose vertices represent the image color characteristics. Skin and non-skin elements are assigned by evaluating label costs of vertices associated to the weight edges of the graph. A novel approach based on an energy function defined in terms of a database of skin and non-skin tones is used to define the costs of the edges of the graph. Finally, the graph cut problem is solved in Graphics Processing Units (GPU) using the Compute Unified Device Architecture (CUDA) technology yielding very promising skin segmentation results for standard resolution video sequences. Our method was evaluated under several conditions, indicating when correct or incorrect results are generated. The overall experiments have shown that this automatic method is simple, efficient, and yields very reliable results.
graph cuts skin segmentation rgb color space gpu computing pixel-based classification push-relabel algorithm seeds database