Attribute Operators for Color Images: Image Segmentation Improved by the Use of Unsupervised Segmentation Evaluation Methods

摘要

Attribute openings and thinnings are morphological connected operators that remove structures from images according to a given criterion. These operators were successfully extended from binary to grayscale images, but such extension to color images is not straight-forward. This paper proposes color attribute operators by a combination of color gradients and thresholding decomposition. In this approach, not only structural criteria may be applied, but also criteria based on color features and statistics. This work proposes, in a segmentation framework, two criteria based on unsupervised segmentation evaluation for improvement of color segmentation. Segmentation using our operators performed better than two state-of-the-art methods in 80% of the experiments done using 300 images.