IR-Guided Energy Optimization Framework for Depth Enhancement in Time of Flight Imaging
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/159451
comunitat-uji-handle4:
INVESTIGACIONThis resource is restricted
https://doi.org/10.1007/978-3-031-49018-7_46 |
Metadata
Title
IR-Guided Energy Optimization Framework for Depth Enhancement in Time of Flight ImagingDate
2023-11-27Publisher
SpringerISBN
978-3-031-49017-0; 978-3-031-49018-7ISSN
0302-9743Bibliographic citation
Achaibou, A., Pla, F., Calpe, J. (2024). IR-Guided Energy Optimization Framework for Depth Enhancement in Time of Flight Imaging. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_46Type
info:eu-repo/semantics/conferenceObjectVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
This paper introduces an optimization energy framework based on infrared guidance to improve depth consistency in Time of Flight image systems. The primary objective is to formulate the problem as an image energy ... [+]
This paper introduces an optimization energy framework based on infrared guidance to improve depth consistency in Time of Flight image systems. The primary objective is to formulate the problem as an image energy optimization task, aimed at maximizing the coherence between the depth map and the corresponding infrared image, both captured simultaneously from the same Time of Flight sensor. The concept of depth consistency relies on the underlying hypothesis concerning the correlation between depth maps and their corresponding infrared images. The proposed optimization framework adopts a weighted approach, leveraging an iterative estimator. The image energy is characterized by introducing spatial conditional entropy as a correlation measure and spatial error as image regularization. To address the issue of missing depth values, a preprocessing step is initially applied, by using a depth completion method based on infrared guided belief propagation, which was proposed in a previous work. Subsequently, the proposed framework is employed to regularize and enhance the inpainted depth. The experimental results demonstrate a range of qualitative improvements in depth map reconstruction, with a particular emphasis on the sharpness and continuity of edges. © 2024, Springer Nature Switzerland AG. [-]
Description
Ponència presentada al: 26th Iberoamerican Congress on Pattern Recognition, CIARP 2023. Coimbra, 27 November 2023 through 30 November 2023
Is part of
V. Vasconcelos et al. (Eds.): CIARP 2023, LNCS 14469, pp. 646–660, 2024. https://doi.org/10.1007/978-3-031-49018-7_46Funder Name
Analog Devices, Inc. | Agencia Valenciana de la Innovación | Generalitat Valenciana
Project title or grant
“Plan GEnT. Doctorados Industriales. Innodocto”
Rights
© Springer Nature Switzerland AG 2024.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess