A simple expression for the map of Asplund's distances with the multiplicative Logarithmic Image Processing (LIP) law

International Prevention Research Institute（国际预防研究所）

Guillaume Noyel, Michel Jourlin

备注：: Accepted to the 12th European Congress for Stereology and Image Analysis 2017, Kaiserslautern, Germany, September 11-14, 2017
发表信息：: 12th European Congress for Stereology and Image Analysis 2017, Sep 2017, Kaiserslautern, Germany. Proceedings of the 12th European Congress for Stereology and Image Analysis 2017. http://www.mathematik.uni-kl.de/events/ecsia-2017/12th-ecsia-2017

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

We introduce a simple expression for the map of Asplund's distances with the multiplicative Logarithmic Image Processing (LIP) law. It is a difference between a morphological dilation and a morphological erosion with an additive structuring function which corresponds to a morphological gradient.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Spatio-Colour Asplünd 's Metric and Logarithmic Image Processing for Colour Images (LIPC)

International Prevention Research Institute（国际预防研究所）；Lab. H. Curien, UMR CNRS 5516（H. 居里实验室，法国国家科学研究中心混合研究单位5516）

Asplünd 's metric, which is useful for pattern matching, consists in a double-sided probing, i.e. the over-graph and the sub-graph of a function are probed jointly. This paper extends the Asplünd 's metric we previously defined for colour and multivariate images using a marginal approach (i.e. component by component) to the first spatio-colour Asplünd 's metric based on the vectorial colour LIP model (LIPC). LIPC is a non-linear model with operations between colour images which are consistent with the human visual system. The defined colour metric is insensitive to lighting variations and a va…

2017-02-27cs.CV

A Link Between the Multiplicative and Additive Functional Asplund's Metrics

University of Strathclyde（思克莱德大学）；International Prevention Research Institute（国际预防研究所）

Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to double-sided probing in the Logarithmic Image Processing (LIP) framework. Two metrics were defined, namely the LIP-multiplicative Asplund's metric which is robust to variations of object thickness (or opacity) and the LIP-additive Asplund's metric which is robust to variations of camera exposure-time (or light intensity). Maps of distances-i.e. maps of these metric values-were also computed between a reference template and an image. Recently, it was proven that the map of LIP-…

2019-07-17cs.CVmath.FA

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

University of Strathclyde（思克莱德大学）；International Prevention Research Institute（国际预防研究所）

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 referenc…

2020-07-24cs.CVcs.NAeess.SP

Region homogeneity in the Logarithmic Image Processing framework: application to region growing algorithms

International Prevention Research Institute（国际预防研究所）；University of Strathclyde Institute of Global Public Health（思克莱德大学全球公共卫生学院）

In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to…

2019-04-17cs.CV

Logarithmic Morphological Neural Nets robust to lighting variations

Laboratoire Hubert Curien（于贝尔·居里安实验室）；Université Jean Monnet（让·莫内大学）

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 …

2022-11-29cs.CVcs.NAeess.SP