[1] 内閣府 Society 5.0, Retrieved July 17, 2020, from https://www8.cao.go.jp/cstp/ society5_0/
[2] 情報処理学会コンピュータビジョンとイメージメディア研究会, Retrieved June 11, 2020, from http://cvim.ipsj.or.jp/
[3] Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S. M., & Szeliski, R. (2011). Building rome in a day. Communications of the ACM, 54(10), 105–112.
[4] Park, K., Patten, T., Prankl, J., & Vincze, M. (2019). Multi-task template matching for object detection, segmentation and pose estimation using depth images. In International Conference on Robotics and Automation, 7207–7213.
[5] Xue, T., Rubinstein, M., Liu, C., & Freeman, W. T. (2015). A computational approach for obstruction-free photography. ACM Transactions on Graphics, 34(4), 1–11.
[6] Fukushima, K. (1980) Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202.
[7] Hofer, M., Maurer, M., & Bischof, H. (2014). Improving sparse 3D models for man- made environments using line-based 3D reconstruction. In 2nd International Conference on 3D Vision, 535–542.
[8] Sugiura, T., Torii, A., & Okutomi, M. (2015). 3D surface reconstruction from point- and-line cloud. In International Conference on 3D Vision, 264–272.
[9] Newcombe, R. A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., ... & Fitzgibbon, A. (2011). KinectFusion: Real-time dense surface mapping and tracking. In 10th IEEE International Symposium on Mixed and Augmented Reality, 127–136.
[10] Yonezawa, T., & Ueda, K. (2014). Real-time 3D data reduction and reproduction of spatial model using line detection in RGB image. In Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems, 727–730.
[11] Nguyen, T., Reitmayr, G., & Schmalstieg, D. (2015). Structural modeling from depth im- ages. IEEE Transactions on Visualization and Computer Graphics, 21(11), 1230–1240.
[12] Nakayama, Y., Saito, H., Shimizu, M., & Yamaguchi, N. (2016). Marker-less augmented reality framework using on-site 3D line-segment-based model generation. Electronic Imaging, 2016(14), 1–24.
[13] Von Gioi, R. G., Jakubowicz, J., Morel, J. M., & Randall, G. (2012). LSD: a line segment detector. Image Processing On Line, 2, 35–55.
[14] Nakayama, Y., Honda, T., Saito, H., Shimizu, M., & Yamaguchi, N. (2014). Accurate camera pose estimation for kinectfusion based on line segment matching by LEHF. In 22nd International Conference on Pattern Recognition, 2149–2154.
[15] Hirose, K., & Saito, H. (2012). Fast Line Description for Line-based SLAM. In the British Machine Vision Conference, 1–11.
[16] Zhang, Z., & Faugeras, O. (1990). Building a 3D world model with a mobile robot: 3D line segment representation and integration. In Proceedings of the 10th International Conference on Pattern Recognition, 38–42.
[17] 森尚平, 一刈良介, 柴田史久, 木村朝子, 田村秀行. (2011). 隠消現実感の技術的枠組と 諸問題：現実世界に実在する物体を視覚的に隠蔽・消去・透視する技術について. 日本バーチャルリアリティ学会論文誌 (< 特集 > 複合現実感 5), 16(2), 239-250.
[18] Bane, R., & Hollerer, T. (2004). Interactive tools for virtual x-ray vision in mobile augmented reality. In Third IEEE and ACM International Symposium on Mixed and Augmented Reality, 231–239.
[19] Avery, B., Sandor, C., & Thomas, B. H. (2009). Improving spatial perception for aug- mented reality x-ray vision. In IEEE Virtual Reality Conference, 79–82.
[20] Kameda, Y., Takemasa, T., & Ohta, Y. (2004). Outdoor see-through vision utilizing surveillance cameras. In Third IEEE and ACM International Symposium on Mixed and Augmented Reality, 151–160.
[21] Barnum, P., Sheikh, Y., Datta, A., & Kanade, T. (2009). Dynamic seethroughs: Synthe- sizing hidden views of moving objects. In 8th IEEE International Symposium on Mixed and Augmented Reality, 111–114.
[22] Holz, D., & Behnke, S. (2013). Fast range image segmentation and smoothing using ap- proximate surface reconstruction and region growing. In Intelligent Autonomous Systems 12, 61–73.
[23] Woodham, R. J. (1980). Photometric method for determining surface orientation from multiple images. Optical Engineering, 19(1), 191139.
[24] Cao, Y. P., Kobbelt, L., & Hu, S. M. (2018). Real-time high-accuracy three-dimensional reconstruction with consumer RGB-D cameras. ACM Transactions on Graphics, 37(5), 1–16.
[25] Popov, S., Bauszat, P., & Ferrari, V. (2020). CoReNet: Coherent 3D scene reconstruction from a single RGB image. arXiv preprint arXiv:2004.12989.
[26] Gotardo, P. F., Simon, T., Sheikh, Y., & Matthews, I. (2015). Photogeometric scene flow for high-detail dynamic 3D reconstruction. In Proceedings of the IEEE International Conference on Computer Vision, 846–854.
[27] Sohaib, A., Farooq, A. R., Atkinson, G. A., Smith, L. N., Smith, M. L., & Warr, R. (2013). In vivo measurement of skin microrelief using photometric stereo in the presence of interreflections. Journal of the Optical Society of America A, 30(3), 278–286.
[28] Chung, H. S., & Jia, J. (2008). Efficient photometric stereo on glossy surfaces with wide specular lobes. In IEEE Conference on Computer Vision and Pattern Recognition, 1–8.
[29] Hernández, C., Vogiatzis, G., & Cipolla, R. (2010). Overcoming shadows in 3-source photometric stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2), 419–426.
[30] Barsky, S., & Petrou, M. (2003). The 4-source photometric stereo technique for three- dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1239–1252.
[31] Chandraker, M., Agarwal, S., & Kriegman, D. (2007). Shadowcuts: Photometric stereo with shadows. In IEEE Conference on Computer Vision and Pattern Recognition, 1–8.
[32] Ikehata, S., Wipf, D., Matsushita, Y., & Aizawa, K. (2012). Robust photometric stereo using sparse regression. In IEEE Conference on Computer Vision and Pattern Recogni- tion, 318–325.
[33] Dulac, A., Martedi, S., Saito, H., Tezuka, K., & Shimizu, M. (2014). Combination method: photometric stereo with shadows. In Proceedings of the Irish Machine Vision and Image Processing, 137–142.
[34] Miyazaki, D., & Ikeuchi, K. (2010). Photometric stereo using graph cut and M-estimation for a virtual tumulus in the presence of highlights and shadows. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 70–77.
[35] Miyazaki, D., Hara, K., & Ikeuchi, K. (2010). Median photometric stereo as applied to the segonko tumulus and museum objects. International Journal of Computer Vision, 86(2-3), 229–242.
[36] Xu, J. & Pachauri, D. Project 3: Photometric stereo, Retrieved January 17, 2015, from http://pages.cs.wisc.edu/jiaxu/projects/Stereo/
[37] Xiong, Y., Chakrabarti, A., Basri, R., Gortler, S. J., Jacobs, D. W., & Zickler, T. (2014). From shading to local shape. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(1), 67–79.
[38] ARTIST-3D.COM, Josea: Clay vase garden pottery, Retrieved January 21, 2015, from http://artist-3d.com/free_3d_models/dnm/model_disp.php?uid=3792
[39] POV-Ray, Retrieved January 21, 2015, from http://www.povray.org/
[40] World Federation of Occupational Therapists. (2019). Definitions of occupational ther- apy from member organisations.
[41] Ayres, A. J. (1979). Sensory integration and the child. Los Angeles: Western Psycho- logical Services.
[42] Green, D., Charman, T., Pickles, A., Chandler, S., Loucas, T. O. M., Simonoff, E., & Baird, G. (2009). Impairment in movement skills of children with autistic spectrum disorders. Developmental Medicine & Child Neurology, 51(4), 311–316.
[43] Iwanaga, R., Kawasaki, C., & Tsuchida, R. (2000). Brief report: Comparison of sensory- motor and cognitive function between autism and Asperger syndrome in preschool children. Journal of Autism and Developmental Disorders, 30(2), 169–174.
[44] Whyatt, C. P., & Craig, C. M. (2012). Motor skills in children aged 7–10 years, diagnosed with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42(9), 1799–1809.
[45] Konicarova, J., Bob, P., & Raboch, J. (2014). Balance deficits and ADHD symptoms in medication-naïve school-aged boys. Neuropsychiatric Disease and Treatment, 10, 85–88.
[46] Bucci, M. P., Stordeur, C., Acquaviva, E., Peyre, H., & Delorme, R. (2016). Postu- ral instability in children with ADHD is improved by methylphenidate. Frontiers in Neuroscience, 10, 163.
[47] Cherng, R. J., Hsu, Y. W., Chen, Y. J., & Chen, J. Y. (2007). Standing balance of children with developmental coordination disorder under altered sensory conditions. Human Movement Science, 26(6), 913–926.
[48] Fong, S. S., Lee, V. Y., & Pang, M. Y. (2011). Sensory organization of balance con- trol in children with developmental coordination disorder. Research in Developmental Disabilities, 32(6), 2376–2382.
[49] Rogers, J. C., & Holm, M. B. (1994). Accepting the challenge of outcome research: Examining the effectiveness of occupational therapy practice. American Journal of Occupational Therapy, 48(10), 871–876.
[50] Henderson, S. E., Sugden, D. A., & Barnett, A. L. (2007) Movement assessment battery for children-second edition. London: The Psychological Corporation.
[51] Bruininks, R. H., & Bruininks, B. D. (2005) Bruininks-oseretsky test of motor proficiency-second edition. Minneapolis, MN: Pearson Assessment.
[52] Japanese Academy of Sensory Integration (2011) The Japanese playful assessment for neuropsychological abilities: examiner’s manual. Osaka: Pacific Supply.
[53] Iwanaga, R., Tsuchida, R., Kato, T., Nagai, Y., Ohta, A., Hida, K., & Yamada, T. (2010). Development of Japanese playful assessment of neuropsychological abilities: Correlations between test scores and age in equilibrium tests, antigravity posture tests and somatosensory tests. In Proceedings of the 15th World Congress of the World Federation of Occupational Therapists.
[54] Kato, T., Tsuchida, R., Nagai, Y., Ohta, A., Hida, K., Iwanaga, R., & Yamada, T. (2010). Development of Japanese playful assessment of neuropsychological abilities correlations between test scores and age in eye-hand coordination and visual perception tests, and praxis tests. In Proceedings of the 15th World Congress of the World Federation of Occupational Therapists.
[55] DeMatteo, C., Law, M., Russell, D., Pollock, N., Rosenbaum, P., & Walter, S. (1993). The reliability and validity of the Quality of Upper Extremity Skills Test. Physical & Occupational Therapy in Pediatrics, 13(2), 1–18.
[56] Bonnechère, B., Jansen, B., & Jan, S. V. S. (2016). Cost-effective (gaming) motion and balance devices for functional assessment: Need or hype?. Journal of Biomechanics, 49(13), 2561–2565.
[57] Alberts, J. L., Hirsch, J. R., Koop, M. M., Schindler, D. D., Kana, D. E., Linder, S. M., ... & Thota, A. K. (2015). Using accelerometer and gyroscopic measures to quantify postural stability. Journal of Athletic Training, 50(6), 578–588.
[58] French, B., Sycamore, N. J., McGlashan, H. L., Blanchard, C. C., & Holmes, N. P. (2018). Ceiling effects in the Movement Assessment Battery for Children-2 (MABC-2) suggest that non-parametric scoring methods are required. PloS One, 13(6).
[59] Sellers, J. S. (1988). Relationship between antigravity control and postural control in young children. Physical Therapy, 68(4), 486–490.
[60] Odunaiya, N. A., Oladeji, O. M., & Oguntibeju, O. O. (2009). Assessment of antigravity and postural control in health children in Ibadan. Nigeria. Pakistan Journal of Medica Sciences, 25(4), 583–590.
[61] Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7291–7299.
[62] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, 2961–2969.
[63] Habibie, I., Xu, W., Mehta, D., Pons-Moll, G., & Theobalt, C. (2019). In the wild human pose estimation using explicit 2D features and intermediate 3D representations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 10905–10914.
[64] Yang, W., Ouyang, W., Wang, X., Ren, J., Li, H., & Wang, X. (2018). 3D human pose estimation in the wild by adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5255–5264.
[65] Kocabas, M., Athanasiou, N., & Black, M. J. (2020). VIBE: Video inference for human body pose and shape estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[66] Chen, H., Luo, X., Zheng, Z., & Ke, J. (2019). A proactive workers’ safety risk evaluation framework based on position and posture data fusion. Automation in Construction, 98, 275–288.
[67] Xue, D., Sayana, A., Darke, E., Shen, K., Hsieh, J. T., Luo, Z., ... & Fei-Fei, L. (2018). Vision-based gait analysis for senior care. arXiv preprint arXiv:1812.00169.
[68] Tao, L., Paiement, A., Damen, D., Mirmehdi, M., Hannuna, S., Camplani, M., ... & Craddock, I. (2016). A comparative study of pose representation and dynamics modelling for online motion quality assessment. Computer Vision and Image Understanding, 148, 136–152.
[69] Campbell, K., Carpenter, K. L., Hashemi, J., Espinosa, S., Marsan, S., Borg, J. S., ... & Tepper, M. (2019). Computer vision analysis captures atypical attention in toddlers with autism. Autism, 23(3), 619–628.
[70] Dawson, G., Campbell, K., Hashemi, J., Lippmann, S. J., Smith, V., Carpenter, K., ... & Sapiro, G. (2018). Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder. Scientific Reports, 8, 17008.
[71] Ayres, A. J. (1989). Sensory integration and praxis test: SIPT manual. Los Angeles: Western Psychological Services.
[72] Miyake, Y., Kobayashi, R., Kelepecz, D., & Nakajima, M. (2013). Core exercises elevate trunk stability to facilitate skilled motor behavior of the upper extremities. Journal of Bodywork and Movement Therapies, 17(2), 259–265.
[73] McWilliams, S. (2005). Developmental coordination disorder and self-esteem: Do occu- pational therapy groups have a positive effect? British Journal of Occupational Therapy, 68(9), 393–400.
[74] Poulsen, A. A., & Ziviani, J. M. (2004). Can I play too? Physical activity engagement of children with developmental coordination disorders. Canadian Journal of Occupational Therapy, 71(2), 100–107.
[75] Missiuna, C., Cairney, J., Pollock, N., Campbell, W., Russell, D. J., Macdonald, K., ... & Cousins, M. (2014). Psychological distress in children with developmental coordi- nation disorder and attention-deficit hyperactivity disorder. Research in Developmental Disabilities, 35(5), 1198–1207.
[76] Piek, J. P., Rigoli, D., Pearsall-Jones, J. G., Martin, N. C., Hay, D. A., Bennett, K. S., & Levy, F. (2007). Depressive symptomatology in child and adolescent twins with attention-deficit hyperactivity disorder and/or developmental coordination disorder. Twin Research and Human Genetics : The Oflcial Journal of the International Society for Twin Studies, 10(4), 587–596.
[77] Tsai, C. L., Wu, S. K., & Huang, C. H. (2008). Static balance in children with develop- mental coordination disorder. Human Movement Science, 27(1), 142–153.
[78] Doumas, M., McKenna, R., & Murphy, B. (2016). Postural control deficits in autism spectrum disorder: The role of sensory integration. Journal of Autism and Developmen- tal Disorders, 46(3), 853–861.
[79] Lim, Y. H., Partridge, K., Girdler, S., & Morris, S. L. (2017). Standing postural control in individuals with autism spectrum disorder: systematic review and meta-analysis. Journal of Autism and Developmental Disorders, 47(7), 2238–2253.
[80] TOYOTA. TOYOTA HSR, Retrieved July 23, 2020, from https://global.toyota/jp/album/ images/29064680/
[81] LaValle, S. M. (2006). Planning algorithms. Cambridge University Press.
[82] Borenstein, J., & Koren, Y. (1991). The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation, 7(3), 278–288.
[83] Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics and Automation Magazine, 4(1), 23–33.
[84] 新田次郎, 佐々木洋子, 溝口博, 加賀美聡. (2015). 1P2-S01 人環境で移動するロボットの通行者情報地図による経路計画手法. ロボティクス・メカトロニクス講演会講演概要集, 2015, 1P2-S01(1)–1P2-S01(4).
[85] 佐々木毅, 橋本秀紀. (2008). 歩行経路観測に基づく重要点抽出による移動ロボットナ ビゲーション. 計測自動制御学会論文集, 44(4), 368–376.
[86] 田村雄介, 濱崎峻資, 山下淳, 淺間一. (2013). 環境に応じた人間の移動予測に基づく移動ロボットの人物回避. 日本機械学会論文集 C 編, 79(799), 617–628.
[87] Noguchi, H., Yamada, T., Mori, T., & Sato, T. (2012). Mobile robot path planning using human prediction model based on massive trajectories. In 2012 Ninth International Conference on Networked Sensing, 1–7.
[88] Müller, J., Stachniss, C., Arras, K. O., & Burgard, W. (2008). Socially inspired motion planning for mobile robots in populated environments. In Proceedings of International Conference on Cognitive Systems.
[89] 伊達央, 滝田好宏, 大川真弥. (2012). 自律移動ロボットの歩行者の流れに合わせたナ ビゲーション. 日本ロボット学会学術講演会予稿集. 30, 2F3–4.
[90] Chung, S. Y., & Huang, H. P. (2012). Incremental learning of human social behav- iors with feature-based spatial effects. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2417–2422.
[91] 北陽電機株式会社. 3 次元測域センサ YVT-X002 [3D-URG], Retrieved January 26, 2017, from http://www.hokuyo-aut.co.jp/search/single.php?serial=150
[92] 寺田耕志, 高岡豊, 山内実, 山本貴史. (2015). 小自由度アームと全方位台車を活用した移動把持ロボット HSR-2015, 第 33 回日本ロボット学会学術講演会, 3F1-02.
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