|
[1] J. R. Gleason, “An accurate, non-iterative approximation for studentized range quantiles,” vol. 31, no. 2, pp. 147–158, 1999. ix, 35 [2] L. SCIENCE, “Blind people have superior memory skills,” 2017. 1 [3] W. H. Organization, “Vision impairment and blindness,” 2017. 1 [4] Z. Yu, S. J. Horvath, A. Delazio, J. Wang, R. Almasi, R. Klatzky, J. Galeotti, and G. D. Stetten, “Palmsight: an assistive technology helping the blind to locate and grasp objects,” Tech. Rep. CMU-RI-TR-16-59, Carnegie Mellon University, Pittsburgh, PA, December 2016. 1, 2, 5, 6, 39 [5] P. A. Zientara, S. Lee, G. H. Smith, R. Brenner, L. Itti, M. B. Rosson, J. M. Carroll, K. M. Irick, and V. Narayanan, “Third eye: A shopping assistant for the visually impaired,” Computer, vol. 50, pp. 16–24, Feb 2017. 1, 5, 6, 39 [6] K. A. Thakoor, N. Mante, C. Zhang, C. Siagian, J. D. Weiland, L. Itti, and G. G. Medioni, “A system for assisting the visually impaired in localization and grasp of desired objects,” in ECCV Workshops, 2014. 1, 2, 5, 6, 39
[7] M. Eckert, M. Blex, and C. Friedrich, “Object detection featuring 3d audio local- ization for microsoft hololens - a deep learning based sensor substitution approach
for the blind,” pp. 555–561, 01 2018. 1, 2, 5, 6, 39
[8] S. et al, “Dlwv2: a deep learning-based wearable vision-system with vibrotactile- feedback for visually impaired people to reach objects,” International Conference
on Intelligent Robots and Systems, IROS, submission accepted to IROS 2018. 1, 5, 6, 29, 39 [9] D. G. Lowe, “Object recognition from local scale-invariant features,” in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol. 2, pp. 1150–1157, Ieee, 1999. 1, 7 [10] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1, pp. 886–893, IEEE, 2005. 1, 7 [11] S. Nanayakkara, R. Shilkrot, K. P. Yeo, and P. Maes, “Eyering: A finger-worn input device for seamless interactions with our surroundings,” in Proceedings of the 4th Augmented Human International Conference, AH ’13, (New York, NY, USA), pp. 13–20, ACM, 2013. 2, 5, 6 [12] B. Söveny, G. Kovács, and Z. T. Kardkovács, “Blind guide - a virtual eye for guid- ing indoor and outdoor movement,” in 2014 5th IEEE Conference on Cognitive
Infocommunications (CogInfoCom), pp. 343–347, Nov 2014. 5, 6 [13] J. Bai, S. Lian, Z. Liu, K. Wang, and D. Liu, “Smart guiding glasses for visually
impaired people in indoor environment,” IEEE Transactions on Consumer Elec- tronics, vol. 63, pp. 258–266, August 2017. 5, 6
[14] M. Gharat, R. Patanwala, and A. Ganaparthi, “Audio guidance system for blind,” in 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 1, pp. 381–384, April 2017. 6 [15] J. R. Blum, M. Bouchard, and J. R. Cooperstock, “What’s around me? spatialized
audio augmented reality for blind users with a smartphone,” in Mobile and Ubiq- uitous Systems: Computing, Networking, and Services (A. Puiatti and T. Gu, eds.),
(Berlin, Heidelberg), pp. 49–62, Springer Berlin Heidelberg, 2012. 6 [16] K. Matsuda and K. Kondo, “Towards a navigation system for the visually impaired using 3d audio,” in 2016 IEEE 5th Global Conference on Consumer Electronics, pp. 1–2, Oct 2016. 6 [17] J. Sánchez and M. Sáenz, “3d sound interactive environments for blind children problem solving skills,” Behaviour & Information Technology, vol. 25, no. 4, pp. 367–378, 2006. 6 [18] R. Shilkrot, J. Huber, J. Steimle, S. Nanayakkara, and P. Maes, “Digital digits: A comprehensive survey of finger augmentation devices,” ACM Computing Surveys (CSUR), vol. 48, no. 2, p. 30, 2015. 7 [19] L. Chan, R.-H. Liang, M.-C. Tsai, K.-Y. Cheng, C.-H. Su, M. Y. Chen, W.-H.
Cheng, and B.-Y. Chen, “Fingerpad: private and subtle interaction using finger- tips,” in Proceedings of the 26th annual ACM symposium on User interface soft- ware and technology, pp. 255–260, ACM, 2013. 7
[20] L. Chan, Y.-L. Chen, C.-H. Hsieh, R.-H. Liang, and B.-Y. Chen, “Cyclopsring: Enabling whole-hand and context-aware interactions through a fisheye ring,” in Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, pp. 549–556, ACM, 2015. 7
[21] D. Ashbrook, P. Baudisch, and S. White, “Nenya: subtle and eyes-free mobile in- put with a magnetically-tracked finger ring,” in Proceedings of the SIGCHI Con- ference on Human Factors in Computing Systems, pp. 2043–2046, ACM, 2011.
7 [22] S. Nanayakkara, R. Shilkrot, K. P. Yeo, and P. Maes, “Eyering: a finger-worn input device for seamless interactions with our surroundings,” in Proceedings of the 4th Augmented Human International Conference, pp. 13–20, ACM, 2013. 7 [23] M. Ogata, Y. Sugiura, H. Osawa, and M. Imai, “iring: intelligent ring using infrared reflection,” in Proceedings of the 25th annual ACM symposium on User interface software and technology, pp. 131–136, ACM, 2012. 7 [24] L. Stearns, R. Du, U. Oh, C. Jou, L. Findlater, D. A. Ross, and J. E. Froehlich,
“Evaluating haptic and auditory directional guidance to assist blind people in read- ing printed text using finger-mounted cameras,” ACM Transactions on Accessible
Computing (TACCESS), vol. 9, no. 1, p. 1, 2016. 7
[25] W. Kienzle and K. Hinckley, “Lightring: always-available 2d input on any sur- face,” in Proceedings of the 27th annual ACM symposium on User interface soft- ware and technology, pp. 157–160, ACM, 2014. 7
[26] L. Stearns, U. Oh, L. Findlater, and J. E. Froehlich, “Touchcam: Realtime recog- nition of location-specific on-body gestures to support users with visual impair- ments,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous
Technologies, vol. 1, no. 4, p. 164, 2018. 7 [27] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object
detection with region proposal networks,” in Advances in neural information pro- cessing systems, pp. 91–99, 2015. 8
[28] J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018. 8 [29] T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007, 2017. 8, 16 [30] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna,
Y. Song, S. Guadarrama, et al., “Speed/accuracy trade-offs for modern convolu- tional object detectors,” in IEEE CVPR, vol. 4, 2017. 8
[31] K. Chen, J. Wang, S. Yang, X. Zhang, Y. Xiong, C. C. Loy, and D. Lin, “Optimizing video object detection via a scale-time lattice,” in CVPR, 2018. 8
[32] K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang, “Object detec- tion in videos with tubelet proposal networks,” in Proc. CVPR, vol. 2, p. 7, 2017.
8 [33] J. P. Lewis, “Fast template matching,” in Vision interface, vol. 95, pp. 15–19, 1995. 8, 20 [34] Itseez, “Open source computer vision library.” https://github.com/itseez/ opencv, 2015. 14 [35] A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761–769, 2016. 16
[36] S. R. Bulo, G. Neuhold, and P. Kontschieder, “Loss maxpooling for semantic im- age segmentation,” CVPR), July, vol. 7, 2017. 16
[37] C. Peng, T. Xiao, Z. Li, Y. Jiang, X. Zhang, K. Jia, G. Yu, and J. Sun, “Megdet: A large mini-batch object detector,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6181–6189, 2018. 16 [38] J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learn- ing and stochastic optimization,” Journal of Machine Learning Research, vol. 12,
no. Jul, pp. 2121–2159, 2011. 21 [39] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” pp. 448–456, 2015. 21 [40] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and
C. L. Zitnick, “Microsoft coco: Common objects in context,” in European confer- ence on computer vision, pp. 740–755, Springer, 2014. 21
[41] C. Vondrick, D. Patterson, and D. Ramanan, “Efficiently scaling up crowdsourced video annotation,” International Journal of Computer Vision, vol. 101, no. 1, pp. 184–204, 2013. 24 |