Shape from shading with interreflections under proximal light source - 3D shape reconstruction of unfolded book surface from a scanner image

We address the problem to recover the 3D shape of an unfolded book surface from the shading information in a scanner image. From a technical point of view, this shape from shading problem in real world environments is characterized by (1) proximal light source, (2) interreflections, (3) moving light source, (4) specular reflection, and (5) nonuniform albedo distribution. Taking all these factors into account, we first formulate the problem based on an iterative nonlinear optimization scheme. Then we introduce piecewise polynomial models of the 3D shape. Image restoration experiments for a real book surface demonstrated that geometric and photometric distortions are almost completely removed by the proposed method</p

Adaptive Texture Alignment for Japanese Kimono Design

A yukata is a type of traditional Japanese kimono. An alignment of its texture pattern is an important factor of the yukata design. There are rules in the texture alignment of the yukata. The rules are comparratively simple. However, the texture alignment is difficult for the designer because the texture alignment should be performed with consideration to the rules and the wearer's taste, Additioanlly, it is necessary for the designer to create the cutting pattern from a limited length of the kimono cloth. Consequently, a design support system for the yukata is required. We have developed the image processing algorithm to simulate the condition of the texture alignment. It becomes possible to perform the texture alignment based on the traditioonal rule automatically. However, some cutting pattern becomes over length of a standard kimono cloth. In this paper, we describe a multi-agent system for supporting the texture alignment of the yukata. We developed texture alignment agents, and a management agent. The management agent acts management of the wearer's body sizes, the condition of the texture alignment and the cutting pattern and orders to the texture alignment agents to carry out the texture alignment according to the wearer's taste. By repeating trial and error, the realistic texture alignment became possible. </p

3D Shape Measurement System by Integration of Partial Shapes and Automatic Defects Detection

We discuss a 3D shape reconstruction method to obtain a whole shape of an object for the digital archive. We propose an integration method of partial shapes measured from different viewpoints. But, the integrated shape often includes defects such as holes or chips. So, we also propose defect detection and re-measurement method. From experiments, we can show the effectiveness of our proposed method

3D shape reconstruction of Japanese traditional puppet head from CT images by graph cut and machine learning methods

In this study, we discuss the digital archiving of Japanese traditional puppets. We propose two methods for extracting the puppet head shape from computed tomography (CT) images. The first is the graph cut method, and the second is a machine learning method based on U-Net. According to the experimental results of the extraction of puppet heads from CT images, the U-Net-based method can extract puppet heads more accurately than the graph cut method. Moreover, the U-Net-based method can extract puppet heads with multiple materials. However, the extraction of metal parts is inaccurate because of metal artefacts in the X-ray CT images and insufficient learning data

Indoor UAV Navigation System Using LED Panels and QR Codes

In this study, we propose an unmanned aerial vehicle (UAV) navigation system using LED panels and QR codes as markers in an indoor environment. An LED panel can display various patterns; hence, we use it as a command presentation device for UAVs, and a QR code can embed various pieces of information, which is used as a sign to estimate the location of the UAV on the way of the flight path. In this paper, we present a navigation method from departure to destination positions in which an obstacle lies between them. In addition, we investigate the effectiveness of our proposed method using an actual UAV

LED Panel Detection and Pattern Discrimination Using UAV

The UAV flight control method we propose uses LED panels and a video camera on the UAV. Specifically, the LED panel displays patterns related to the UAV commands and blinking patterns for panel detection. A panel detection process based in UAV video camera images uses the frequency of green blinking as a cue for panel detection, then command patterns are distinguished and the UAV performs tasks based on this pattern (command). In experiments we performed for panel detection and discrimination using the UAV, we confirm the effectiveness of the proposed method for autoflight control

イメージスキャナを用いた陰影情報解析に基づく3次元物体の形状復元

京都大学0048新制・論文博士博士(情報学)乙第11255号論情博第40号新制||情||21(附属図書館)UT51-2003-H917(主査)教授 松山 隆司, 教授 乾 敏郎, 教授 美濃 導彦学位規則第4条第2項該当Doctor of InformaticsKyoto UniversityDFA

UAV manipulation by hand gesture recognition

In this study, we discuss a unmanned aerial vehicle operation system by recognizing human gestures. Here, we focus on both dynamic and static gestures, such as moving the right hand repeatedly or holding it in a certain position. And, we propose two methods, one is a feature-based (FB) method to detect the position of the right hand in an image and identify the gesture form features estimated by FFT, and the other is a machine learning (ML) method to detect the position of the right hand in an image and identify the gesture by the framework of the ML. In experiments, we compare the results of gesture recognition by each method. As a result, the recognition rate of the FB method is higher than that of the ML method under the conditions assumed in the FB method. But, in other cases, the ML method is higher than that of the FB method. The ML method is also effective in terms of extensibility, such as adding more types of gestures

UAV manipulation by hand gesture recognition

In this study, we discuss a unmanned aerial vehicle operation system by recognizing human gestures. Here, we focus on both dynamic and static gestures, such as moving the right hand repeatedly or holding it in a certain position. And, we propose two methods, one is a feature-based (FB) method to detect the position of the right hand in an image and identify the gesture form features estimated by FFT, and the other is a machine learning (ML) method to detect the position of the right hand in an image and identify the gesture by the framework of the ML. In experiments, we compare the results of gesture recognition by each method. As a result, the recognition rate of the FB method is higher than that of the ML method under the conditions assumed in the FB method. But, in other cases, the ML method is higher than that of the FB method. The ML method is also effective in terms of extensibility, such as adding more types of gestures