Three dimensional (3D) scanned point clouds offer an invaluable tool for the digital preservation of cultural heritage, capturing intricate and complex 3D structures. Transparent visualization, i.e., see-through visualization, is essential for effectively understanding these data. However, since 3D scanned point clouds do not include normal vector information, applying shading effects to reveal complex shapes is challenging. In this paper, we present a method for assigning normal vectors to 3D scanned points using a statistical approach based on principal component analysis. To resolve the ambiguity of normal vector orientations, specifically the front or back of local surfaces, we exploit the characteristics of transparent visualization. Each normal vector is oriented toward the viewpoint, enabling consistent shading across all visible points. We then apply dynamic shading by varying the light source position sequentially, which highlights different regions of the visualized object. This approach allows for a shift in focus between surface features and deeper internal structures, enhancing the understanding of the overall structure of complex 3D cultural heritage objects. We demonstrate the effectiveness of dynamic shading through its application to real 3D scanned data of cultural heritage objects with intricate internal structures.

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.

Precise segmentation of retinal vessels is crucial for a computer-aided diagnosis system of retinal fundus images. However, this task remains challenging due to large variations in scales and poor segmentation of capillary vessels. In this paper, we propose a semantics and multi-scale aggregation network to address these difficulties. It includes semantics aggregation blocks that are designed for aggregating stronger high-level semantic information. These carefully designed blocks produce more semantic feature representation that is helpful for capillary vessel identification and vessel connection. Besides, a multi-scale aggregation block is designed by employing parallel dilated convolutional filters with different dilation rates to fully exploit the multi-scale information. We evaluate the network by using two public databases of retinal vessel segmentation and compare its performance with several leading methods published in the past several years. Extensive evaluations show that the proposed network has achieved the state-of-the-art performance on the public CHASE DB1 and HRF datasets.

Rakkan is a Japanese word for the signature of the artist for a completed painting. In this paper, we propose a system that helps the users to identify and retrieve rakkan images of ukiyo-e paintings. The users can select the rakkan region from a digitalized ukiyo-e image via a graphical user interface. The selected rakkan area is binarized using k-means clustering in L*a*b* color space and normalized for retrieval process as a query image. Histogram of oriented gradients and cross-correlation are used for retrieval. The proposed system can be adapted to ukiyo-e authorship identification and ukiyo-e retrieval system for ukiyo-e study and archiving.

We tried to build a virtual Yamahoko Parade experiencing system as a digital museum research for Kyoto Gion Festival. We collected route data and acceleration data of Hayashibutai, the performance stage of the Yamahoko float, with a GPS logger and acceleration sensors during the parade. We adopted the data to reproduce the vibration using a vibration platform and integrated vibration, sound, and 3D screen to build an experimental system. The analysis of the reproduced acceleration data and the original acceleration data showed that the virtual experiencing system reproduced the vibration of the Hayashibutai. We also received positive feedbacks from the experts participated in our experiment.