Several generative models have been proposed for image segmentation. The latest diffusion models exhibit generation abilities and segmentation superior to those of previous models based on variational autoencoders or generative adversarial nets. However, diffusion models generally require large structures for the reverse diffusion process as well as considerable memory. For the segmentation of three-dimensional (3D) medical volume data, the original data are typically decomposed into multiple two-dimensional (2D) slices or smaller patches as the input for the segmentation model to reduce computational costs. However, this method can limit the receptive field to capture the structure of the original volume data. We propose a novel two-stage diffusion-based segmentation model that retains its original structure with reduced computational costs. In the first stage, coarse detection and segmentation are performed on the entire volume. In the second stage, finer segmentation is performed on each small patch. Furthermore, the prediction map obtained in the first stage is used as a given condition during the denoising process, thus global information is considered for fine segmentation. The proposed method is applied for brain tumor segmentation on the BraTS 2021 dataset and achieved an improved accuracy for multiple types of tumor regions. When focusing on the segmentation of smaller enhancing tumors (ET), the proposed method achieved Dice score of 88.0%, which is 1.6% higher than that of the baseline model, Diff-UNet. This performance signifies a substantial improvement over previous model, especially in the challenging task of brain tumor segmentation.

In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM. Employing an RS camera model with linearized pure rotation, we show that the RS distortion can be approximately expressed by two internal parameters of an imaginary camera plus one-parameter nonlinear transformation similar to lens distortion. We then reformulate the problem as self-calibration of the imaginary camera, in which its skew and aspect ratio are unknown and varying in the image sequence. In the formulation, we derive a general representation of CMSs. We also show that our method can explain the CMS that was recently reported in the literature, and then present a new remedy to deal with the degeneracy. Our theoretical results agree well with experimental results, it explains degeneracies observed when we employ naive bundle adjustment, and how they are resolved by our method.

Consumer Generated Media (CGM) is gaining huge popularity. The authors are particularly interested in the intercultural comprehension of movie contents made in foreign countries. This paper focuses on the website bilibili.tv as a test case to analyze how Japanese movie contents are watched in China. The authors analyze all tags and how foreign tags are introduced and translated into Chinese. They propose a simple statistical method to identify whether a word is a loanword or not, if the word is represented by Chinese characters. They also analyze the trends of tags in bilibili.

Kyushu University installed a private cloud system, named “campus cloud system”, using VCL and CloudStack. For a graduate school exercise course on web search engine, the authors prepared a virtual machine on VCL, which had apache web server and GETA indexer preinstalled. This paper introduces an outline of the cloud system, the exercise, and also reports advantages and disadvantages of cloud based education.

Nowadays, a lot of people post various contents to the video sharing services. To find good contents, it is necessary something high-quality content search and contents classification method, and the method should understand user's taste and user's context. In this paper, we propose a ranking method based on viewer's comments, especially amount of "funny" feelings comments given by consumer. We also evaluate the questionnaire for our method. Our proposed method is assumed to be applicable to all types of content, if it given a lot of comments from people.

It is one of the central issues in augmented reality and computer vision to track a planar object moving relatively to a camera in an accurate and robust manner. In previous studies, it was pointed out that there are several factors making the tracking difficult, such as illumination change and motion blur, and effective solutions were proposed for them. In this paper, we point out that degradation in effective image resolution can also deteriorate tracking performance, which typically occurs when the plane being tracked has an oblique pose with respect to the viewing direction, or when it moves to a distant location from the camera. The deterioration tends to become significantly large for extreme configurations, e.g., when the planar object has nearly a right angle with the viewing direction. Such configurations can frequently occur in AR applications targeted at ordinary users. To cope with this problem, we model the sampling and reconstruction process of images, and present a tracking algorithm that incorporates the model to correctly handle these configurations. We show through several experiments that the proposed method shows better performance than conventional methods.