Functional Asplund metrics for pattern matching, robust to variable lighting conditions

Noyel, Guillaume; Jourlin, Michel

doi:10.5566/ias.2292

Cited by 3 publications

(5 citation statements)

References 58 publications

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“…However, the enhanced image still has color distortion and halo phenomena, which reduce the accuracy of feature matching. Al-Aminen [12] enhanced low-illumination images by improving the LIP algorithm [13], thus highlighting the local features of the images and improving the brightness of the images. However, the local brightness of the enhanced image is excessively enhanced, which reduces the accuracy of feature matching.…”

Section: Image Enhancementmentioning

confidence: 99%

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Target Localization Method Based on Image Degradation Suppression and Multi-Similarity Fusion in Low-Illumination Environments

Tang

Qin

Yang

et al. 2023

IJGI

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Frame buildings as important nodes of urban space. The include high-speed railway stations, airports, residences, and office buildings, which carry various activities and functions. Due to illumination irrationality and mutual occlusion between complex objects, low illumination situations frequently develop in these architectural environments. In this case, the location information of the target is difficult to determine. At the same time, the change in the indoor electromagnetic environment also affects the location information of the target. Therefore, this paper adopts the vision method to achieve target localization in low-illumination environments by feature matching of images collected in the offline state. However, the acquired images have serious quality degradation problems in low-illumination conditions, such as low brightness, low contrast, color distortion, and noise interference. These problems mean that the local features in the collected images are missing, meaning that they fail to achieve a match with the offline database images; as a result, the location information of the target cannot be determined. Therefore, a Visual Localization with Multiple-Similarity Fusions (VLMSF) is proposed based on the Nonlinear Enhancement And Local Mean Filtering (NEALMF) preprocessing enhancement. The NEALMF method solves the problem of missing local features by improving the quality of the acquired images, thus improving the robustness of the visual positioning system. The VLMSF method solves the problem of low matching accuracy in similarity retrieval methods by effectively extracting and matching feature information. Experiments show that the average localization error of the VLMSF method is only 8 cm, which is 33.33% lower than that of the Kears-based VGG-16 similarity retrieval method. Meanwhile, the localization error is reduced by 75.76% compared with the Perceptual hash (Phash) retrieval method. The results show that the method proposed in this paper greatly alleviates the influence of low illumination on visual methods, thus helping city managers accurately grasp the location information of targets under complex illumination conditions.

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Section: Image Enhancementmentioning

confidence: 99%

“…Equation ( 9) is substituted into Equations ( 10) and (11), respectively, thereby obtaining Equations ( 12) and (13).…”

Section: Low-illumination Image Enhancement Based On the Eaindamentioning

confidence: 99%

Target Localization Method Based on Image Degradation Suppression and Multi-Similarity Fusion in Low-Illumination Environments

Tang

Qin

Yang

et al. 2023

IJGI

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“…The map of Asplund distances is related to Mathematical Morphology (MM) [23,24]. Noyel as shown in [22] that it is specifically related to LMM as follows.…”

Section: Link With Logarithmic Mathematical Morphologymentioning

confidence: 99%

“…However, morphological neural networks are not intrinsically robust to real lighting variations. The analysis of images presenting such variations is a challenging task that can occur in many settings [11,30,24]: industry, traffic control, underwater vision, face recognition, large public health databases, etc. In this paper, we propose a morphological neural network which is robust to such lighting variations due to a change of light intensity or of camera exposure-time.…”

Section: Introductionmentioning

confidence: 99%

“…As the LIP model is based on a famous optical law, namely the Transmittance Law, we shall introduce in this way Physics in those neural nets. In addition, the maps of Asplund distances between an image and a reference template, the probe, are related to Mathematical Morphology [24]. We shall see that they are especially related to the newly introduced framework of Logarithmic Mathematical Morphology [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Logarithmic Morphological Neural Nets robust to lighting variations

Noyel,

Barbier--Renard,

Jourlin

et al. 2022

Preprint

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Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of Logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic Image Processing (LIP) model. This model has a LIP additive law which simulates in images a variation of the light intensity. We especially learn the structuring function of a LMM operator robust to those variations, namely : the map of LIP-additive Asplund distances. Results in images show that our neural network verifies the required property.

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