In deep learning, hyperparameters can severely affect the learning model performance. The Nelder-Mead (NM) method is known for showing a superior performance for hyperparameter optimization in deep learning. An initial simplex, one of the initial NM method's values, is usually determined randomly while the search performance strongly depends on the shape of the initial simplex. Therefore, it is necessary to determine a proper initial simplex as previous researchers have proposed methods to construct an initial simplex from one starting point in the bounded search space. In this study, we verified how these methods for constructing an initial simplex contribute to improving the result of hyperparameter optimization in deep learning, by using a simple model and a complicated model. A smaller initial simplex may fail to optimization by bad local minima because there are some bad local minima in both learning models. We concluded that the starting point is not necessarily located close to the origin, and that a larger initial simplex contributes to improving the result of hyperparameter optimization in deep learning.

This paper proposes a depth measurement error model for consumer depth cameras such as the Microsoft Kinect, and a corresponding calibration method. These devices were originally designed as video game interfaces, and their output depth maps usually lack sufficient accuracy for 3D measurement. Models have been proposed to reduce these depth errors, but they only consider camera-related causes. Since the depth sensors are based on projector-camera systems, we should also consider projector-related causes. Also, previous models require disparity observations, which are usually not output by such sensors, so cannot be employed in practice. We give an alternative error model for projector-camera based consumer depth cameras, based on their depth measurement algorithm, and intrinsic parameters of the camera and the projector; it does not need disparity values. We also give a corresponding new parameter estimation method which simply needs observation of a planar board. Our calibrated error model allows use of a consumer depth sensor as a 3D measuring device. Experimental results show the validity and effectiveness of the error model and calibration procedure.

We have developed a screen sharing system to share contents between two persons, and applicable to one-to-one remote teaching. The screen of a PC at one side is shared with the screen of a tablet at the other side through the network to convey instructions from an operator to a collaborator. This system makes it possible to arbitrarily select a part of the screen of the PC at the operator side. The selected screen is presented in the tablet at the collaborator side. The collaborator can adjust the scale of contents, and capture the screen. By analyzing such operations, the system can understand and record which parts of the contents the collaborator paid attention to. In addition, the camera of the tablet can be used as a simple scanner to digitize paper documents easily. Further, characters and symbols drawn with a finger or a pen on the tablet screen can be presented on the PC at the other side.

We describe a method for generating an informative wide-view image using images captured by a moving camera. The generated image allows for events in the scene observed by the camera to be understood easily. Our method does not use 3D shape information explicitly. Instead, it employs the trajectory of feature points across multiple images and generates a composite image by taking into account the distribution of the trajectories of the feature points.

Large-print textbooks are used by low vision students in school. Because these books are mainly prepared by volunteers and almost all steps in the preparation process are performed manually, they cannot be mass-produced. The chronic shortage of these books has been a social problem in Japan. The procedure for preparing a large-print textbook involves (1) converting the size of figures and characters in the original textbook to one suitable for low vision students and (2) positioning them appropriately on the pages. In this paper, we propose a novel method for automatically optimizing a layout by employing a context structure. We represent the context structure by using a graph called a context structure graph. The proposed method first allocates each material to an appropriate page, and then optimizes the layout of each page by using the sequence-pair method. Throughout these operations, we employ an objective function derived from the context structure graph to ensure that the context in the original textbook is preserved in the large-print textbook prepared.

This paper proposes an effective method for compensating inter-reflection in immersive projection displays (IPDs). Because IPDs project images onto a screen, which surrounds a viewer, we have perform out both geometric and photometric corrections. Our method compensates inter-reflection on the screen. It requires no special device, and approximates both diffuse and specular reflections on the screen using block-based photometric calibration.