The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.

No abstract

An efficient depth map generation method is presented for static scenes with moving objects. Firstly, static background scene is reconstructed. Depth map of the reconstructed static background scene is extracted by linear perspective. Then, moving objects are segmented precisely. Depth values are assigned to the segmented moving objects according to their positions in the static scene. Finally, the depth values of the static background scene and the moving objects are integrated into one depth map. Experimental results show that the proposed method can generate smooth and reliable depth maps.

No abstract