mlsanityData to 7 Oct 2026

Transferability of labels between multilens cameras

arXiv 2501.11513 ↗First posted 20 Jan 2025cs.CVcited by 0

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Abstract (arXiv)

In this work, a new method for automatically extending Bounding Box (BB) and mask labels across different channels on multilens cameras is presented. For that purpose, the proposed method combines the well known phase correlation method with a refinement process. During the first step, images are aligned by localizing the peak of intensity obtained in the spatial domain after performing the cross correlation process in the frequency domain. The second step consists of obtaining the best possible transformation by using an iterative process maximising the IoU (Intersection over Union) metric. Results show that, by using this method, labels could be transferred across different lens on a camera with an accuracy over 90% in most cases and just by using 65 ms in the whole process. Once the transformations are obtained, artificial RGB images are generated, for labeling them so as to transfer this information into each of the other lens. This work will allow users to use this type of cameras in more fields rather than satellite or medical imagery, giving the chance of labeling even invisible objects in the visible spectrum.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 20 Jan 2025.

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References · 9

  1. 2023Segment Anythingcited by 8,158
  2. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  3. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  4. 2024Better Call SAL: Towards Learning to Segment Anything in Lidarcited by 47
  5. 2017Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learningcited by 37
  6. 2021MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labelscited by 12
  7. 2024A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)cited by 8
  8. 2024Vision and Tactile Robotic System to Grasp Litter in Outdoor Environmentscited by 3
  9. 2022Detection and depth estimation for domestic waste in outdoor environments by sensors fusioncited by 1

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