When creating a virtual reality space using omnidirectional images, it is desirable to use images in which the photographer does not appear in the image. This paper proposes a method to generate an omnidirectional image excluding the photographer by using multiple images taken by an omnidirectional camera, instead of processing only one image. In the proposed method, the photographer first takes multiple omnidirectional images while rotating around the omnidirectional camera. Next, feature point matching is performed on the multiple omnidirectional images, and the appearances of the images are unified using the amount of translation calculated from the feature point matching. Finally, the images are complemented and color-corrected to produce an omnidirectional image without the photographer.

In today’s online commerce, the easy distribution of counterfeit products, such as food, brand-name products, and pharmaceuticals, has become a serious social problem in terms of economy and safety.To address this problem, we propose a system for determining the authenticity of 2D codes printed by inkjet printers; it leverages the micro-scale nature of the ink sequences in the codes, which are difficult to duplicate.The proposed system combines locally likely arrangement hashing, which performs a fast search for similar images, and Accelerated-KAZE, which performs high-precision feature matching, to determine the authenticity of images taken with a smartphone.The results of our study demonstrated the high effectiveness of our system in terms of accuracy, discriminability,and speed by validating it on a large dataset of 15000 images. The system was also validated for industrial applications by verifying its scalability for low-cost industrial media printing and monochrome printing as well as its robustness to rotation,scale change, and smudging.

Analysis of the 3D structure of wires in electrical cables is an important technique for verifying various characteristics of cables. Nondestructive inspection using X-ray CT is available for this purpose. However, due to the limited resolution of X-ray CT images, each wire in the images is blurred, making it difficult to accurately extract the 3D structure of thousands of wires from thousands of CT images taken in the longitudinal direction of the cable. In this study, we propose a method for estimating the 3D structure of all wires in a cable from a 3D X-ray CT image of the cable. The proposed method first detects wires in each image using Faster R-CNN. It then tracks each wire in a set of cross-sectional images collected in the longitudinal direction of the cable to estimate the structure as a 3D trajectory of wires. The pre-trained Faster R-CNN model is fine-tuned using our synthetic image dataset that simulates the wire region. It achieves wire detection without the need of annotating the training data. The results showed that wires can be detected with an error of about 1%, and 40% to 80% of the wires can be tracked continuously over 500 frames.