Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.

Digital archiving of three-dimensional cultural heritage assets has increased the demand for visualization of large-scale point clouds of cultural heritage assets acquired by laser scanning. We proposed a fused transparent visualization method that visualizes a point cloud of a cultural heritage asset in an environment using a photographic image as the background. We also proposed lightness adjustment and color enhancement methods to deal with the reduced visibility caused by the fused visualization. We applied the proposed method to a laser-scanned point cloud of a high-valued cultural festival float with complex inner and outer structures. Experimental results demonstrate that the proposed method enables high-quality transparent visualization of the cultural asset in its surrounding environment.

Transparent visualization is used in many fields because it can visualize not only the frontal object but also other important objects behind it. Although in many situations, it would be very important for the 3D structures of the visualized transparent images to be perceived as they are simulated, little is known quantitatively as to how such transparent 3D structures are perceived. To address this question, in the present study, we conducted a psychophysical experiment in which the observers reported the perceived depth magnitude of a transparent object in medical images, presented with a multi-view 3D display. For the visualization, we employed a stochastic point-based rendering (SPBR) method, which was developed recently as a technique for efficient transparent-rendering. Perceived depth of the transparent object was smaller than the simulated depth. We found, however, that such depth underestimation can be alleviated to some extent by (1) applying luminance gradient inherent in the SPBR method, (2) employing high opacities, and (3) introducing binocular disparity and motion parallax produced by a multi-view 3D display.

The preservation and analysis of tangible cultural heritage sites have attracted enormous interest worldwide. Recently, establishing three-dimensional (3D) digital archives has emerged as a critical strategy for the permanent preservation and digital analysis of cultural sites. For extant parts of cultural sites, 3D scanning is widely used for efficient and accurate digitization. However, in many historical sites, many parts that have been damaged or lost by natural or artificial disasters are unavailable for 3D scanning. The remaining available data sources for these destroyed parts are photos, computer-aided design (CAD) drawings, written descriptions, etc. In this paper, we achieve an integrated digital archive of a UNESCO World Heritage site, namely, the Borobudur temple, in which buried reliefs and internal foundations are not available for 3D scanning. We introduce a digitizing framework to integrate three different kinds of data sources and to create a unified point-cloud-type digital archive. This point-based integration enables us to digitally record the entire 3D structure of the target cultural heritage site. Then, the whole site is visualized by stochastic point-based rendering (SPBR) precisely and comprehensibly. The proposed framework is widely applicable to other large-scale cultural sites.