Distortion Correction for 3D Scan of Trunk Swaying Human Body Segments

We propose a method for acquiring a 3D shape of human body segments accurately. Using a light stripe triangulation range finder, we can acquire accurate 3D shape of a motionless object in dozens of seconds. If the object moves during the scanning, the acquired shape would be distorted. Naturally, humans move slightly for making balance while standing even if the subject makes an effort to stay still for avoiding the distortion in acquired shape. Our method corrects the distortion based on measured subject's motion during the scanning. Experimental results show the accuracy of the proposed method. Trunk swaying degrades the accuracy of the light stripe triangulation from 1mm to 10mm. We can keep the accuracy of as good as 2mm by applying our method

Registration of serial sections: An evaluation method based on distortions of the ground truths

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.Comment: Supplemental data available under https://zenodo.org/record/428244

ヒカリ セツダンホウ ニ ヨル サマザマナ シセイ デ ノ サンジゲン ジンタイ ケイジョウ モデリング

京都大学0048新制・課程博士博士(情報学)甲第13199号情博第243号新制||情||51(附属図書館)UT51-2007-H472京都大学大学院情報学研究科知能情報学専攻(主査)教授 美濃 導彦, 教授 松山 隆司, 教授 中村 裕一学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Intention-Sensing Recipe Guidance via User Accessing to Objects

Sensing the intention of a user’s forthcoming action is a necessary function for systems that assist human physical activity. In this article, a strategy for recipe guidance systems that can predict the forthcoming intended subtask in a cooking task is investigated. The focus is on user accessing objects, that is, touching and releasing objects. Touching can indicate the start of the forthcoming subtask and releasing can indicate the end of the task. The main difficulty lies in the fact that humans may move objects because they are in the way and use cooking tools that are unanticipated by an assistive system. In such cases, the accessed object should not indicate the forthcoming subtask. A method is proposed to track the progress of a task based on the object access history. This enables to eliminate object accesses that are out of context. Simultaneously, the method predicts the forthcoming subtask based on a combination of progress and materials rather than tools and materials. Then, a guidance system that runs as a web service is developed. In experiments, real cooking activities navigated by this system are observed. The Wizard of OZ method is utilized to simulate a system that detects object accesses. The experimental results show that 73.6% accuracy is achieved in the selection of the displayed information. This result supports the use of “access to objects” realize effective intention-sensing systems

Non-rigid registration of serial section images by blending transforms for 3D reconstruction

In this research, we propose a novel registration method for three-dimensional (3D) reconstruction from serial section images. 3D reconstructed data from serial section images provides structural information with high resolution. However, there are three problems in 3D reconstruction: non-rigid deformation, tissue discontinuity, and accumulation of scale change. To solve the non-rigid deformation, we propose a novel non-rigid registration method using blending rigid transforms. To avoid the tissue discontinuity, we propose a target image selection method using the criterion based on the blending of transforms. To solve the scale change of tissue, we propose a scale adjustment method using the tissue area before and after registration. The experimental results demonstrate that our method can represent non-rigid deformation with a small number of control points, and is robust to a variation in staining. The results also demonstrate that our target selection method avoids tissue discontinuity and our scale adjustment reduces scale change

Facile Fabrication of Ultralow-Density Transparent Boehmite Nanofiber Cryogel Monoliths and Their Application in Volumetric Three-Dimensional Displays

Low bulk density transparent porous monoliths have unique optical properties such as low refractive index and usually can be obtained via supercritical drying to prevent deformation and collapse of pore structure. We succeeded in fabricating a transparent cryogel with a bulk density of 3.5 mg cm−3 by vacuum freeze drying of a monolithic wet gel composed of boehmite nanofibers. In the case of adding a functional material into the starting sol of the gel, a composite material can be obtained. We analyzed the optical properties of transparent cryogels using image processing (direct-global separation) and applied the composite with a fluorescent molecule to volumetric three-dimensional (3D) displays

Depth from phasor distortions in fog

This paper presents a time-of-flight (ToF) measurement method for use in foggy weather. The depth measured by a ToF camera is greatly distorted in fog because the light scattered in the fog reaches the camera much faster than the target reflection. We reveal that the multi-frequency measurements contain a cue whether two arbitrary pixels have the same depth. After clustering the same depth pixels using this cue, the original depth can be recovered for each cluster by line fitting in the Cartesian coordinate frame. The effectiveness of this method is evaluated numerically via real-world and road-scale experiments