This paper presents an instructional support system based on augmented reality (AR). This system helps a user to work intuitively by overlaying visual information in the same way of a navigation system. In usual AR systems, the contents to be overlaid onto real space are created with 3D Computer Graphics. In most cases, such contents are newly created according to applications. However, there are many 2D videos that show how to take apart or build electric appliances and PCs, how to cook, etc. Therefore, our system employs such existing 2D videos as instructional videos. By transforming an instructional video to display, according to the user's view, and by overlaying the video onto the user's view space, the proposed system intuitively provides the user with visual guidance. In order to avoid the problem that the display of the instructional video and the user's view may be visually confused, we add various visual effects to the instructional video, such as transparency and enhancement of contours. By dividing the instructional video into sections according to the operations to be carried out in order to complete a certain task, we ensure that the user can interactively move to the next step in the instructional video after a certain operation is completed. Therefore, the user can carry on with the task at his/her own pace. In the usability test, users evaluated the use of the instructional video in our system through two tasks: a task involving building blocks and an origami task. As a result, we found that a user's visibility improves when the instructional video is transformed to display according to his/her view. Further, for the evaluation of visual effects, we can classify these effects according to the task and obtain the guideline for the use of our system as an instructional support system for performing various other tasks.

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In this paper, we present a system for visualizing temperature changes in a scene using an RGB-D camera coupled with a thermal camera. This system has applications in the context of maintenance of power equipments. We propose a two-stage approach made of with an offline and an online phases. During the first stage, after the calibration, we generate a 3D reconstruction of the scene with the color and the thermal data. We then apply the Viewpoint Generative Learning (VGL) method on the colored 3D model for creating a database of descriptors obtained from features robust to strong viewpoint changes. During the second online phase we compare the descriptors extracted from the current view against the ones in the database for estimating the pose of the camera. In this situation, we can display the current thermal data and compare it with the data saved during the offline phase.