IR image databases generation under target intrinsic thermal variability constraints

• URL: http://arxiv.org/abs/2411.07577v1
• Date: Tue, 12 Nov 2024 06:29:27 GMT
• Title: IR image databases generation under target intrinsic thermal variability constraints
• Authors: Jerome Gilles, Stephane Landeau, Tristan Dagobert, Philippe Chevalier, Christian Bolut,
• Abstract summary: We propose a method which superimpose targets and occultants on background under image quality metrics constraints to generate realistic images. We also propose a method to generate target signatures with intrinsic thermal variability based on 3D models plated with real infrared textures.
• Score: 0.0
• License:
• Abstract: This paper deals with the problem of infrared image database generation for ATR assessment purposes. Huge databases are required to have quantitative and objective performance evaluations. We propose a method which superimpose targets and occultants on background under image quality metrics constraints to generate realistic images. We also propose a method to generate target signatures with intrinsic thermal variability based on 3D models plated with real infrared textures.

Related papers

• Towards Degradation-Robust Reconstruction in Generalizable NeRF [58.33351079982745]
  Generalizable Radiance Field (GNeRF) across scenes has been proven to be an effective way to avoid per-scene optimization. There has been limited research on the robustness of GNeRFs to different types of degradation present in the source images.
  arXiv  Detail & Related papers  (2024-11-18T16:13:47Z)
• Génération de bases de données images IR sous contraintes avec variabilité thermique intrinsèque des cibles [0.0]
  We develop a principle which authorizes us to generate different thermal configurations of target signatures. This method enables us to easily generate huge datasets for ATR algorithms performance evaluation.
  arXiv  Detail & Related papers  (2024-11-12T06:29:18Z)
• Thermal3D-GS: Physics-induced 3D Gaussians for Thermal Infrared Novel-view Synthesis [11.793425521298488]
  This paper introduces a physics-induced 3D Gaussian splatting method named Thermal3D-GS. The first large-scale benchmark dataset for this field named Thermal Infrared Novel-view Synthesis dataset (TI-NSD) is created. The results indicate that our method outperforms the baseline method with a 3.03 dB improvement in PSNR.
  arXiv  Detail & Related papers  (2024-09-12T13:46:53Z)
• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. We present a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs. We also present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• Thermal-NeRF: Neural Radiance Fields from an Infrared Camera [29.58060552299745]
  We introduce Thermal-NeRF, the first method that estimates a volumetric scene representation in the form of a NeRF solely from IR imaging. We conduct extensive experiments to demonstrate that Thermal-NeRF can achieve superior quality compared to existing methods.
  arXiv  Detail & Related papers  (2024-03-15T14:27:15Z)
• CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components [77.33782775860028]
  We introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-07-24T11:59:07Z)
• Does Thermal Really Always Matter for RGB-T Salient Object Detection? [153.17156598262656]
  This paper proposes a network named TNet to solve the RGB-T salient object detection (SOD) task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality.
  arXiv  Detail & Related papers  (2022-10-09T13:50:12Z)
• 3D Human Texture Estimation from a Single Image with Transformers [106.6320286821364]
  We propose a Transformer-based framework for 3D human texture estimation from a single image. We also propose a mask-fusion strategy to combine the advantages of the RGB-based and texture-flow-based models.
  arXiv  Detail & Related papers  (2021-09-06T16:00:20Z)
• Self-training Guided Adversarial Domain Adaptation For Thermal Imagery [0.12891210250935145]
  We propose an unsupervised domain adaptation method which does not require RGB-to-thermal image pairs. We employ large-scale RGB dataset MS-COCO as source domain and thermal dataset FLIR ADAS as target domain. To perform self-training, pseudo labels are assigned to the samples on the target thermal domain to learn more generalized representations for the target domain.
  arXiv  Detail & Related papers  (2021-06-14T05:17:21Z)
• Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving [67.69430435482127]
  Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving. The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals. This work proposes a domain adaptation framework which employs a style transfer technique for transfer learning from visible spectrum images to thermal images.
  arXiv  Detail & Related papers  (2020-06-01T09:59:09Z)
• Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images [4.810743887667828]
  This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images. Experiments demonstrate the effectiveness and superiority of the proposed method.
  arXiv  Detail & Related papers  (2020-02-17T04:53:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.