Project Details

Topografická analýza obrazu s využitím metod hlubokého učení

Project Period: 1. 7. 2019 – 30. 6. 2022

Project Type: grant

Code: LTAIZ19004

Agency: Ministerstvo školství, mládeže a tělovýchovy ČR

Program: INTER-EXCELLENCE - Podprogram INTER-ACTION

English title
Deep-Learning Approach to Topographical Image Analysis
Type
grant
Keywords

image geo-localization, topographic information, image registration, deep-learning, computer vision

Abstract

The project focuses on the current problems of computer vision, especially on visual localization in the natural environment. The visual location of the camera in the outdoor environment is not a fixed issue today, although it offers a wide range of attractive applications from automatic image comprehension, to expanded reality applications to navigation of self-governing vehicles and airplanes. The project aims to research new methods for locating cameras based on the multimodal data registration, especially photographic information, synthetic rendered images, depth information and field models using current machine learning methods, especially deep neural networks (DNN). In addition to the use of terrain data in the form of graphical models, an alternative of predictive depth information from an input photograph will be explored. The CPhoto @ FIT Group has been dealing with the long-standing problem and has deep experience in research and application. The Israeli partner also offers unique data sets indispensable for DNN training.

Team members
Čadík Martin, doc. Ing., Ph.D. (DCGM) – research leader
Brejcha Jan, Ing., Ph.D. (RG CPHOTO)
Lysek Tomáš, Ing.
Polášek Tomáš, Ing. (DCGM)
Tomešek Jan, Ing. (DCGM)
Publications

2023

  • BOBÁK, P.; ČMOLÍK, L.; ČADÍK, M. Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2023, p. 1-14. ISSN: 1077-2626. Detail
  • POLÁŠEK, T.; ČADÍK, M.; KELLER, Y.; BENEŠ, B. Vision UFormer: Long-Range Monocular Absolute Depth Estimation. COMPUTERS & GRAPHICS-UK, 2023, vol. 111, no. 4, p. 180-189. ISSN: 0097-8493. Detail

2022

  • RAJASEKARAN, S.; KANG, H.; ČADÍK, M.; GALIN, E.; GUÉRIN, E.; PEYTAVIE, A.; SLAVÍK, P.; BENEŠ, B. PTRM: Perceived Terrain Realism Metric. ACM Transactions on Applied Perception, 2022, vol. 19, no. 2, p. 1-22. ISSN: 1544-3558. Detail
  • TOMEŠEK, J.; ČADÍK, M.; BREJCHA, J. CrossLocate: Cross-Modal Large-Scale Visual Geo-Localization in Natural Environments using Rendered Modalities. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa: Institute of Electrical and Electronics Engineers, 2022. p. 2193-2202. ISBN: 978-1-6654-0477-8. Detail

2021

  • AHMAD, T.; EMAMI, E.; ČADÍK, M.; BEBIS, G. Resource Efficient Mountainous Skyline Extraction using Shallow Learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN). Hoffman Estates: Institute of Electrical and Electronics Engineers, 2021. p. 1-9. ISBN: 978-1-6654-3900-8. Detail
  • POLÁŠEK, T.; HRŮŠA, D.; BENEŠ, B.; ČADÍK, M. ICTree: Automatic Perceptual Metrics for Tree Models. ACM TRANSACTIONS ON GRAPHICS, 2021, vol. 40, no. 6, p. 1-15. ISSN: 0730-0301. Detail

2020

  • BOBÁK, P.; ČMOLÍK, L.; ČADÍK, M. Temporally Stable Boundary Labeling for Interactive and Non-Interactive Dynamic Scenes. COMPUTERS & GRAPHICS-UK, 2020, vol. 91, no. 10, p. 265-278. ISSN: 0097-8493. Detail
  • BREJCHA, J.; LUKÁČ, M.; HOLD-GEOFFROY, Y.; WANG, O.; ČADÍK, M. LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors. In Computer Vision - ECCV 2020. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland AG, 2020. p. 295-312. ISBN: 978-3-030-58525-9. Detail
Patents

2023

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