G. Olmschenk

Zhu, Z. ., Nair, V. ., Olmschenk, G. ., & Seiple, W. H. (2021). ASSIST: Assistive Sensor Solutions for Independent and Safe Travel of Blind and Visually Impaired People. In IJCAI AI for Social Good Workshop. Retrieved from https://par.nsf.gov/biblio/10286824

Olmschenk, G. ., Chen, J. ., Tang, H. ., & Zhu, Z. . (2019). Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks. In IEEE Conference on Computer Vision and Pattern Recognition: Learning with Imperfect Data Workshop (p. 8). Retrieved from https://par.nsf.gov/biblio/10110611

Nair, V. ., Budhai, M. ., Olmschenk, G. ., Seiple, W. H., & Zhu, Z. . (2019). ASSIST: Personalized Indoor Navigation via Multimodal Sensors and High-Level Semantic Information. In Leal-Taixé L., Roth S. (eds) Computer Vision – ECCV 2018 Workshops. ECCV 2018. Lecture Notes in Computer Science (Vol. 11134, p. 16). http://doi.org/10.1007/978-3-030-11024-6_9

Nair, V. ., Tsangouri, C. ., Xiao, B. ., Olmschenk, G. ., Seiple, W. H., & Zhu, Z. . (2018). A Hybrid Indoor Positioning System for Blind and Visually Impaired Using Bluetooth and Google Tango. Journal on Technology and Persons With Disabilities, 6, 21. Retrieved from https://par.nsf.gov/biblio/10065143