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2020 | OriginalPaper | Chapter

10. Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired

Authors : Dimitris K. Iakovidis, Dimitrios Diamantis, George Dimas, Charis Ntakolia, Evaggelos Spyrou

Published in: Technological Trends in Improved Mobility of the Visually Impaired

Publisher: Springer International Publishing

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Abstract

Visual impairment restricts everyday mobility and limits the accessibility of places, which for the non-visually impaired is taken for granted. A short walk to a close destination, such as a market or a school becomes an everyday challenge. In this chapter, we present a novel solution to this problem that can evolve into an everyday visual aid for people with limited sight or total blindness. The proposed solution is a digital system, wearable like smart-glasses, equipped with cameras. An intelligent system module, incorporating efficient deep learning and uncertainty-aware decision-making algorithms, interprets the video scenes, translates them into speech, and describes them to the user through audio. The user can almost naturally interact with the system via a speech-based user interface, which is also capable of understanding the user’s emotions. The capabilities of this system are investigated in the context of accessibility and guidance to outdoor environments of cultural interest, such as the historic triangle of Athens. A survey of relevant state-of-the-art systems, technologies and services is performed, identifying critical system components that better adapt to the goals of the system, user needs and requirements, toward a user-centered architecture design.

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Footnotes
Literature
Aladren, A., López-Nicolás, G., Puig, L., & Guerrero, J. J. (2016). Navigation assistance for the visually impaired using RGB-D sensor with range expansion. IEEE Systems Journal, 10, 922–932.CrossRef
Alkhafaji, A., Fallahkhair, S., Cocea, M., & Crellin, J. (2016). A survey study to gather requirements for designing a mobile service to enhance learning from cultural heritage. In European Conference on Technology Enhanced Learning (pp. 547–550). Cham: Springer.
Anagnostopoulos, C.-N., Iliou, T., & Giannoukos, I. (2015). Features and classifiers for emotion recognition from speech: A survey from 2000 to 2011. Artificial Intelligence Review, 43, 155–177.CrossRef
Asakawa, S., Guerreiro, J., Ahmetovic, D., Kitani, K. M., & Asakawa, C. (2018). The present and future of museum accessibility for people with visual impairments. In Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility (pp. 382–384). New York, NY: ACM.CrossRef
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481–2495.CrossRef
Baltrusaitis, T., McDuff, D., Banda, N., Mahmoud, M., el Kaliouby, R., Robinson, P., & Picard, R. (2011). Real-time inference of mental states from facial expressions and upper body gestures. In Proceedings of 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011) (pp. 909–914). Washington, DC: IEEE.
Caraiman, S., Morar, A., Owczarek, M., Burlacu, A., Rzeszotarski, D., Botezatu, N., … Moldoveanu, A. (2017). Computer vision for the visually impaired: The sound of vision system. In 2017 IEEE International Conference on Computer Vision Workshop (ICCVW) (pp. 1480–1489). Washington, DC: IEEE.CrossRef
Conradie, P., Goedelaan, G. K. de, Mioch, T., & Saldien, J. (2014). Blind user requirements to support tactile mobility. In Tactile Haptic User Interfaces for Tabletops and Tablets (TacTT 2014) (pp. 48–53).
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18, 32–80.CrossRef
Csapó, Á., Wersényi, G., Nagy, H., & Stockman, T. (2015). A survey of assistive technologies and applications for blind users on mobile platforms: A review and foundation for research. Journal on Multimodal User Interfaces, 9, 275–286.CrossRef
Cui, L. (2018). MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects. arXiv preprint arXiv:1805.07009.
Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. In Advances in Neural Information Processing Systems (pp. 379–387).
Diamantis, D., Iakovidis, D. K., & Koulaouzidis, A. (2018). Investigating cross-dataset abnormality detection in endoscopy with a weakly-supervised multiscale convolutional neural network. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 3124–3128). Washington, DC: IEEE.CrossRef
Diamantis, E. D., Iakovidis, D. K., & Koulaouzidis, A. (2019). Look-behind fully convolutional neural network for computer-aided endoscopy. Biomedical Signal Processing and Control, 49, 192–201.CrossRef
Dimas, G., Spyrou, E., Iakovidis, D. K., & Koulaouzidis, A. (2017). Intelligent visual localization of wireless capsule endoscopes enhanced by color information. Computers in Biology and Medicine, 89, 429–440.CrossRef
Elmannai, W., & Elleithy, K. (2017). Sensor-based assistive devices for visually-impaired people: Current status, challenges, and future directions. Sensors, 17, 565.CrossRef
Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014). Scalable object detection using deep neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Fang, Z., & Scherer, S. (2015). Real-time onboard 6dof localization of an indoor mav in degraded visual environments using a rgb-d camera. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5253–5259). Washington, DC: IEEE.CrossRef
Forster, C., Zhang, Z., Gassner, M., Werlberger, M., & Scaramuzza, D. (2017). Svo: Semidirect visual odometry for monocular and multicamera systems. IEEE Transactions on Robotics, 33, 249–265.CrossRef
Fryer, L. (2013). Putting it into words: The impact of visual impairment on perception, experience and presence. Doctoral dissertation, Goldsmiths, University of London.
Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017). DSSD: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659.
Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1440–1448).
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587).
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672–2680).
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29, 1645–1660.CrossRef
Haag, A., Goronzy, S., Schaich, P., & Williams, J. (2004). Emotion recognition using bio-sensors: First steps towards an automatic system. In Tutorial and Research Workshop on Affective Dialogue Systems (pp. 36–48). New York, NY: Springer.CrossRef
Handa, K., Dairoku, H., & Toriyama, Y. (2010). Investigation of priority needs in terms of museum service accessibility for visually impaired visitors. British Journal of Visual Impairment, 28, 221–234.CrossRef
Hao, M., Yu, H., & Li, D. (2015). The measurement of fish size by machine vision-a review. In International Conference on Computer and Computing Technologies in Agriculture (pp. 15–32). Cham: Springer.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2980–2988). Washington, DC: IEEE.CrossRef
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904–1916.CrossRef
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
Held, D., Thrun, S., & Savarese, S. (2016). Learning to track at 100 FPS with deep regression networks. In European Conference Computer Vision (ECCV).
Hersh, M. A., & Johnson, M. A. (2010). A robotic guide for blind people. Part 1. A multi-national survey of the attitudes, requirements and preferences of potential end-users. Applied Bionics and Biomechanics, 7, 277–288.CrossRef
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.CrossRef
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.CrossRef
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., … Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In CVPR (p. 3).
Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016). Deep networks with stochastic depth. In European Conference on Computer Vision (pp. 646–661). Cham: Springer.
Iakovidis, D. K., Georgakopoulos, S. V., Vasilakakis, M., Koulaouzidis, A., & Plagianakos, V. P. (2018). Detecting and locating gastrointestinal anomalies using deep learning and iterative cluster unification. IEEE Transactions on Medical Imaging, 37, 2196–2210.CrossRef
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50× fewer parameters and <0.5 mb model size. arXiv preprint arXiv:1602.07360.
Kaur, B., & Bhattacharya, J. (2018). A scene perception system for visually impaired based on object detection and classification using multi-modal DCNN. arXiv preprint arXiv:1805.08798.
Konda, K. R., & Memisevic, R. (2015). Learning visual odometry with a convolutional network. VISAPP, 1, 486–490.
Kovács, L., Iantovics, L., & Iakovidis, D. (2018). IntraClusTSP—An incremental intra-cluster refinement heuristic algorithm for symmetric travelling salesman problem. Symmetry, 10, 663.CrossRef
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097–1105).
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.CrossRef
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., … Twitter, W. S. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In CVPR (p. 4).
Leng, H., Lin, Y., & Zanzi, L. (2007). An experimental study on physiological parameters toward driver emotion recognition. In International Conference on Ergonomics and Health Aspects of Work with Computers (pp. 237–246). Berlin, Heidelberg: Springer.CrossRef
Li, R., Wang, S., Long, Z., & Gu, D. (2018). Undeepvo: Monocular visual odometry through unsupervised deep learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7286–7291). Washington, DC: IEEE.CrossRef
Lin, B.-S., Lee, C.-C., & Chiang, P.-Y. (2017). Simple smartphone-based guiding system for visually impaired people. Sensors, 17, 1371.CrossRef
Lin, D. T., Kannappan, A., & Lau, J. N. (2013). The assessment of emotional intelligence among candidates interviewing for general surgery residency. Journal of Surgical Education, 70, 514–521.CrossRef
Lin, S., Cheng, R., Wang, K., & Yang, K. (2018). Visual localizer: Outdoor localization based on convnet descriptor and global optimization for visually impaired pedestrians. Sensors, 18, 2476.CrossRef
Lin, S., Wang, K., Yang, K., & Cheng, R. (2018). KrNet: A kinetic real-time convolutional neural network for navigational assistance. In International Conference on Computers Helping People with Special Needs (pp. 55–62). Berlin: Springer.CrossRef
Lin, T.-Y., Dollár, P., Girshick, R. B., He, K., Hariharan, B., & Belongie, S. J. (2017). Feature pyramid networks for object detection. In CVPR (p. 4).
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In European Conference on Computer Vision (pp. 21–37). Cham: Springer.
Liu, Y., Yu, X., Chen, S., & Tang, W. (2016). Object localization and size measurement using networked address event representation imagers. IEEE Sensors Journal, 16, 2894–2895.CrossRef
Luo, W., Li, J., Yang, J., Xu, W., & Zhang, J. (2018). Convolutional sparse autoencoders for image classification. IEEE Transactions on Neural Networks and Learning Systems, 29, 3289–3294.MathSciNet
Magnusson, C., Hedvall, P.-O., & Caltenco, H. (2018). Co-designing together with persons with visual impairments. In Mobility of visually impaired people (pp. 411–434). Switzerland: Springer.CrossRef
Maimone, M., Cheng, Y., & Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics, 24, 169–186.CrossRef
Mustafah, Y. M., Noor, R., Hasbi, H., & Azma, A. W. (2012). Stereo vision images processing for real-time object distance and size measurements. In 2012 International Conference on Computer and Communication Engineering (ICCCE) (pp. 659–663). Washington, DC: IEEE.CrossRef
Nistér, D., Naroditsky, O., & Bergen, J. (2004). Visual odometry. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004 (CVPR 2004) (pp. I652–I659). Washington, DC: IEEE.CrossRef
Pan, J., Ferrer, C. C., McGuinness, K., O’Connor, N. E., Torres, J., Sayrol, E., & Giro-i-Nieto, X. (2017). Salgan: Visual saliency prediction with generative adversarial networks. arXiv preprint arXiv:1701.01081.
Panchanathan, S., Black, J., Rush, M., & Iyer, V. (2003). iCare-a user centric approach to the development of assistive devices for the blind and visually impaired. In Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, 2003 (pp. 641–648). Washington, DC: IEEE.CrossRef
Papageorgiou, E. I., & Iakovidis, D. K. (2013). Intuitionistic fuzzy cognitive maps. IEEE Transactions on Fuzzy Systems, 21, 342–354.CrossRef
Papageorgiou, E. I., & Salmeron, J. L. (2013). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21, 66–79.CrossRef
Papakostas, M., Spyrou, E., Giannakopoulos, T., Siantikos, G., Sgouropoulos, D., Mylonas, P., & Makedon, F. (2017). Deep visual attributes vs. hand-crafted audio features on multidomain speech emotion recognition. Computation, 5, 26.CrossRef
Perakovic, D., Periša, M., & Prcic, A. B. (2015). Possibilities of applying ICT to improve safe movement of blind and visually impaired persons. In C. Volosencu (Ed.), Cutting edge research in technologies. London: IntechOpen.
Petrushin, V. (1999). Emotion in speech: Recognition and application to call centers. In Proceedings of Artificial Neural Networks in Engineering (p. 22).
Piana, S., Stagliano, A., Odone, F., Verri, A., & Camurri, A. (2014). Real-time automatic emotion recognition from body gestures. arXiv preprint arXiv:1402.5047.
Poggi, M., & Mattoccia, S. (2016). A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 208–213).
Psaltis, A., Kaza, K., Stefanidis, K., Thermos, S., Apostolakis, K. C., Dimitropoulos, K., & Daras, P. (2016). Multimodal affective state recognition in serious games applications. In 2016 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 435–439). Washington, DC: IEEE.CrossRef
Pu, L., Tian, R., Wu, H.-C., & Yan, K. (2016). Novel object-size measurement using the digital camera. In Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016 IEEE (pp. 543–548). Washington, DC: IEEE.
Ramesh, K., Nagananda, S., Ramasangu, H., & Deshpande, R. (2018). Real-time localization and navigation in an indoor environment using monocular camera for visually impaired. In 2018 Fifth International Conference on Industrial Engineering and Applications (ICIEA) (pp. 122–128). Washington, DC: IEEE.CrossRef
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788).
Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. arXiv preprint.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (pp. 91–99).
Roberts, L. G. (1963). Machine perception of three-dimensional solids. Lexington, MA: Massachusetts Institute of Technology (MIT). Lincoln Laboratory.
Schwarze, T., Lauer, M., Schwaab, M., Romanovas, M., Böhm, S., & Jürgensohn, T. (2016). A camera-based mobility aid for visually impaired people. KI-Künstliche Intelligenz, 30, 29–36.CrossRef
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.
Shah, N. F. M. N., & Ghazali, M. (2018). A systematic review on digital technology for enhancing user experience in museums. In International Conference on User Science and Engineering (pp. 35–46). Singapore: Springer.CrossRef
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Sosa-Garcia, J., & Odone, F. (2017). “Hands on” visual recognition for visually impaired users. ACM Transactions on Accessible Computing (TACCESS), 10, 8.
Spyrou, E., Vretos, N., Pomazanskyi, A., Asteriadis, S., & Leligou, H. C. (2018). Exploiting IoT technologies for personalized learning. In 2018 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 1–8). Washington, DC: IEEE.
Suresh, A., Arora, C., Laha, D., Gaba, D., & Bhambri, S. (2017). Intelligent smart glass for visually impaired using deep learning machine vision techniques and robot operating system (ROS). In International Conference on Robot Intelligence Technology and Applications (pp. 99–112). Switzerland: Springer.
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI (p. 12).
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–9).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818–2826).
Tapu, R., Mocanu, B., & Zaharia, T. (2017). DEEP-SEE: Joint object detection, tracking and recognition with application to visually impaired navigational assistance. Sensors, 17, 2473.CrossRef
Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (4th ed.). Boston: Academic Press.MATH
Tsatsou, D., Pomazanskyi, A., Hortal, E., Spyrou, E., Leligou, H. C., Asteriadis, S., … Daras, P. (2018). Adaptive learning based on affect sensing. In International Conference on Artificial Intelligence in Education (pp. 475–479). Switzerland: Springer.CrossRef
Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International Journal of Computer Vision, 104, 154–171.CrossRef
Vašcák, J., & Hvizdoš, J. (2016). Vehicle navigation by fuzzy cognitive maps using sonar and RFID technologies. In 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 75–80). Washington, DC: IEEE.CrossRef
Vasilakakis, M. D., Diamantis, D., Spyrou, E., Koulaouzidis, A., & Iakovidis, D. K. (2018). Weakly supervised multilabel classification for semantic interpretation of endoscopy video frames. Evolving Systems, 1–13.
Wang, H., Hu, J., & Deng, W. (2018). Face feature extraction: A complete review. IEEE Access, 6, 6001–6039.CrossRef
Wang, H.-C., Katzschmann, R. K., Teng, S., Araki, B., Giarré, L., & Rus, D. (2017). Enabling independent navigation for visually impaired people through a wearable vision-based feedback system. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6533–6540). Washington, DC: IEEE.CrossRef
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., & Xu, W. (2016). CNN-RNN: A unified framework for multi-label image classification. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Wang, S., Clark, R., Wen, H., & Trigoni, N. (2017). Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2043–2050). Washington, DC: IEEE.CrossRef
Wang, X., Gao, L., Song, J., & Shen, H. (2017). Beyond frame-level CNN: Saliency-aware 3-D CNN with LSTM for video action recognition. IEEE Signal Processing Letters, 24, 510–514.CrossRef
Xiao, J., Joseph, S. L., Zhang, X., Li, B., Li, X., & Zhang, J. (2015). An assistive navigation framework for the visually impaired. IEEE Transactions on Human-Machine Systems, 45, 635–640.CrossRef
Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5987–5995). Washington, DC: IEEE.CrossRef
Yang, K., Wang, K., Bergasa, L. M., Romera, E., Hu, W., Sun, D., … López, E. (2018). Unifying terrain awareness for the visually impaired through real-time semantic segmentation. Sensors, 18, 1506.CrossRef
Yang, K., Wang, K., Zhao, X., Cheng, R., Bai, J., Yang, Y., & Liu, D. (2017). IR stereo realsense: Decreasing minimum range of navigational assistance for visually impaired individuals. Journal of Ambient Intelligence and Smart Environments, 9, 743–755.CrossRef
Yang, Z., Duarte, M. F., & Ganz, A. (2018). A novel crowd-resilient visual localization algorithm via robust PCA background extraction. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1922–1926). Washington, DC: IEEE.CrossRef
Yu, X., Yang, G., Jones, S., & Saniie, J. (2018). AR marker aided obstacle localization system for assisting visually impaired. In 2018 IEEE International Conference on Electro/Information Technology (EIT) (pp. 271–276). Washington, DC: IEEE.CrossRef
Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications, 9, 149–184.MathSciNetCrossRef
Zeng, L. (2015). A survey: outdoor mobility experiences by the visually impaired. In Mensch und Computer 2015–Workshopband.
Zhang, J., Kaess, M., & Singh, S. (2017). A real-time method for depth enhanced visual odometry. Autonomous Robots, 41, 31–43.CrossRef
Zhang, J., Ong, S., & Nee, A. (2008). Navigation systems for individuals with visual impairment: A survey. In Proceedings of the Second International Convention on Rehabilitation Engineering & Assistive Technology (pp. 159–162). Singapore: Singapore Therapeutic, Assistive & Rehabilitative Technologies (START) Centre.
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2017). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. ArXiv e-prints.
Zowghi, D., & Coulin, C. (2005). Requirements elicitation: A survey of techniques, approaches, and tools. In Engineering and managing software requirements (pp. 19–46). Berlin, Heidelberg: Springer.CrossRef
Metadata
Title
Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired
Authors
Dimitris K. Iakovidis
Dimitrios Diamantis
George Dimas
Charis Ntakolia
Evaggelos Spyrou
Copyright Year
2020
Book
Technological Trends in Improved Mobility of the Visually Impaired

Print ISBN: 978-3-030-16449-2

Electronic ISBN: 978-3-030-16450-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-16450-8_10