Skip to main content

Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired

  • Chapter
  • First Online:
Technological Trends in Improved Mobility of the Visually Impaired

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://eyesynth.com.

  2. 2.

    https://www.orcam.com.

  3. 3.

    https://www.esighteyewear.com.

  4. 4.

    https://aira.io/.

  5. 5.

    https://realsense.intel.com/intel-realsense-downloads/.

References

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cui, L. (2018). MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects. arXiv preprint arXiv:1805.07009.

    Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Elmannai, W., & Elleithy, K. (2017). Sensor-based assistive devices for visually-impaired people: Current status, challenges, and future directions. Sensors, 17, 565.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fryer, L. (2013). Putting it into words: The impact of visual impairment on perception, experience and presence. Doctoral dissertation, Goldsmiths, University of London.

    Google Scholar 

  • Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017). DSSD: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659.

    Google Scholar 

  • Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1440–1448).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • Held, D., Thrun, S., & Savarese, S. (2016). Learning to track at 100 FPS with deep regression networks. In European Conference Computer Vision (ECCV).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.

    Article  Google Scholar 

  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In CVPR (p. 3).

    Google Scholar 

  • 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.

    Google Scholar 

  • Iakovidis, D. K., Dimas, G., Karargyris, A., Bianchi, F., Ciuti, G., & Koulaouzidis, A. (2018). Deep endoscopic visual measurements. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2018.2853987

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • International Organization for Standardization. (2010). ISO 9241-210:2010. https://www.iso.org/standard/52075.html.

  • 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.

    Google Scholar 

  • Konda, K. R., & Memisevic, R. (2015). Learning visual odometry with a convolutional network. VISAPP, 1, 486–490.

    Google Scholar 

  • Kovács, L., Iantovics, L., & Iakovidis, D. (2018). IntraClusTSP—An incremental intra-cluster refinement heuristic algorithm for symmetric travelling salesman problem. Symmetry, 10, 663.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Lin, B.-S., Lee, C.-C., & Chiang, P.-Y. (2017). Simple smartphone-based guiding system for visually impaired people. Sensors, 17, 1371.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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).

    Google Scholar 

  • Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2018). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2018.2858826

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Maimone, M., Cheng, Y., & Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics, 24, 169–186.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Papageorgiou, E. I., & Iakovidis, D. K. (2013). Intuitionistic fuzzy cognitive maps. IEEE Transactions on Fuzzy Systems, 21, 342–354.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Papakostas, M., & Giannakopoulos, T. (2018). Speech-music discrimination using deep visual feature extractors. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.05.016

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Petrushin, V. (1999). Emotion in speech: Recognition and application to call centers. In Proceedings of Artificial Neural Networks in Engineering (p. 22).

    Google Scholar 

  • Piana, S., Stagliano, A., Odone, F., Verri, A., & Camurri, A. (2014). Real-time automatic emotion recognition from body gestures. arXiv preprint arXiv:1402.5047.

    Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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).

    Google Scholar 

  • Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. arXiv preprint.

    Google Scholar 

  • 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).

    Google Scholar 

  • Roberts, L. G. (1963). Machine perception of three-dimensional solids. Lexington, MA: Massachusetts Institute of Technology (MIT). Lincoln Laboratory.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

    Google Scholar 

  • Sosa-Garcia, J., & Odone, F. (2017). “Hands on” visual recognition for visually impaired users. ACM Transactions on Accessible Computing (TACCESS), 10, 8.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (4th ed.). Boston: Academic Press.

    MATH  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • Wang, H., Hu, J., & Deng, W. (2018). Face feature extraction: A complete review. IEEE Access, 6, 6001–6039.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • WHO: World Health Organization. (2018). Blindness and visual impairement. http://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment.

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications, 9, 149–184.

    Article  MathSciNet  Google Scholar 

  • Zeng, L. (2015). A survey: outdoor mobility experiences by the visually impaired. In Mensch und Computer 2015–Workshopband.

    Google Scholar 

  • Zhang, J., Kaess, M., & Singh, S. (2017). A real-time method for depth enhanced visual odometry. Autonomous Robots, 41, 31–43.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Zhang, X., Zhou, X., Lin, M., & Sun, J. (2017). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. ArXiv e-prints.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

Download references

Acknowledgments

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK-02070).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dimitris K. Iakovidis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Iakovidis, D.K., Diamantis, D., Dimas, G., Ntakolia, C., Spyrou, E. (2020). Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired. In: Paiva, S. (eds) Technological Trends in Improved Mobility of the Visually Impaired. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-16450-8_10

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16449-2

  • Online ISBN: 978-3-030-16450-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics