Oracle bone inscriptions (OBIs) are invaluable materials for recovering the economic and social forms for Shang Dynasty, one of the most ancient dynasties in China. It is very important to get the original OBIs from scanned images of oracle bone rubbings. To this end, researchers have to employ a very time-consuming method that they follow the inscriptions by handwritten tools, pixel by pixel and image by image. In this paper, an image segmentation method was proposed to overcome this limitation based on fully convolutional networks (FCN). In order to speed up training as well as boost the segmentation performance, a simple FCN with only convolutional layers was designed, where batch normalization was incorporated. The proposed method was tested on a real OBI image set (320 samples). Experimental results show that the proposed method is effective enough to get the OBIs from scanned images of oracle bone rubbings.
Automatic road extraction from High Resolution Remote Sensing Image is a challenging problem. In this paper we present a new approach for road automatically extraction which is based on topological derivative and mathematical morphology. This approach for road extraction can be divided into three main steps: using topological derivative for image segmentation, using mathematical morphology for road network identification and filtering. The experimental results show that this approach can effectively extract roads from high-resolution remote sensing image.
In the field of remote-sensed image segmentation, it is very important to obtain semantic results. However, in high resolution remote sensed images, different complex patterns always have components with the same spectrum, which makes it rather difficult to extract such patterns only through traditional clustering methods. In this paper, a novel multi-stage region-level clustering method is proposed to solve this problem. Firstly, the initial oversegmentation is obtained by using the Mean Shift algorithm, based on which a region adjacent graph (RAG) is built; Then, FCM is employed to get the spectral-based segmentation result; After that, the context clues for each region is calculated according to the label and size of neighboring regions, followed by the second FCM clustering on each set of regions with the same label to distinct regions with the same spectrum but belongs to different objects; Rearranging all of these clustering results to form the finial processing unit, this algorithm goes a step further to calculate more accurate context clues, and use the third FCM to obtain the final segmentation result. Experiments on the high resolution remote-sensed images have shown the superiority to the competitions.