Logarithmic Mathematical Morphology: A New Framework Adaptive to Illumination Changes

Noyel, Guillaume

doi:10.1007/978-3-030-13469-3_53

Cited by 4 publications

(5 citation statements)

References 53 publications

(91 reference statements)

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“…In the future, these metrics will be extended to the analysis of colour and multivariate images starting from the preliminary ideas developed by Noyel and Jourlin (2015;2017c). They will also be related to Logarithmic Mathematical Morphology (Noyel, 2019b). Proof of proposition 9, p. 63.…”

Section: Resultsmentioning

confidence: 99%

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

Noyel

Jourlin

2020

Image Anal Stereol

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In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund metric which is robust to object absorption variations and ii) the LIP-additive Asplund metric which is robust to variations of source intensity or camera exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movement. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting.

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Section: Resultsmentioning

confidence: 99%

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

Noyel

Jourlin

2020

Image Anal Stereol

Self Cite

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“…Logarithmic Mathematical Morphology (LMM) was introduced by Noyel in [21,22]. LMM consists of defining morphological operations [27,8] b and + b are named logarithmic-dilation and logarithmic-erosion, respectively.…”

Section: Logarithmic Mathematical Morphologymentioning

confidence: 99%

“…Such relations are based on the isomorphism ξ : F M → R D and its inverse [12]. The relations between the logarithmic-operations δ + b , ε + b and the usual functional erosion are the following ones [21,22]:…”

Section: Logarithmic Mathematical Morphologymentioning

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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“…In detail, our contribution is two-fold. (1) Firstly, we extend the preliminary works defining the functional metrics and their corresponding maps of distances between a template and an image [33,25,27,34,35,36] 2 . Beyond the prior work, we add theoretical work at two stages.…”

Section: Introductionmentioning

confidence: 99%

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

Noyel,

Jourlin

2019

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund's metric which is robust to object absorption variations and ii) the LIP-additive Asplund's metric which is robust to variations of source intensity and exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movements. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting. 1

show abstract

Logarithmic Mathematical Morphology: A New Framework Adaptive to Illumination Changes

Cited by 4 publications

References 53 publications

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

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

Logarithmic Morphological Neural Nets robust to lighting variations

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

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