Abstract Mathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net
{"title":"Image Restoration by Learning Morphological Opening-Closing Network","authors":"Ranjan Mondal, M. Dey, B. Chanda","doi":"10.1515/mathm-2020-0103","DOIUrl":"https://doi.org/10.1515/mathm-2020-0103","url":null,"abstract":"Abstract Mathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114737606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sylvain Lempereur, Arnim Jenett, Elodie Machado, Ignacio Arganda-Carreras, Matthieu Simion, P. Affaticati, J. Joly, Hugues Talbot
Abstract Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible. Here, we propose a sample-dependent method for contrast correction. It relies on segmenting the sample, and estimating sample depth isosurfaces that serve as reference for the correction. We segment the brain white matter of zebrafish larvae. We show that this correction allows a better stitching of opposite sides of each larva, in order to image the entire larva with a high signal-to-noise ratio throughout. We also show that our proposed contrast correction method makes it possible to better recognize the deep structures of the brain by comparing manual vs. automated segmentations. This is expected to improve image observations and analyses in high-content methods where signal loss in the samples is significant.
{"title":"Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains","authors":"Sylvain Lempereur, Arnim Jenett, Elodie Machado, Ignacio Arganda-Carreras, Matthieu Simion, P. Affaticati, J. Joly, Hugues Talbot","doi":"10.1515/mathm-2020-0100","DOIUrl":"https://doi.org/10.1515/mathm-2020-0100","url":null,"abstract":"Abstract Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible. Here, we propose a sample-dependent method for contrast correction. It relies on segmenting the sample, and estimating sample depth isosurfaces that serve as reference for the correction. We segment the brain white matter of zebrafish larvae. We show that this correction allows a better stitching of opposite sides of each larva, in order to image the entire larva with a high signal-to-noise ratio throughout. We also show that our proposed contrast correction method makes it possible to better recognize the deep structures of the brain by comparing manual vs. automated segmentations. This is expected to improve image observations and analyses in high-content methods where signal loss in the samples is significant.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124948654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, where labels are given to pixels sharing the same features, distinguishing the present materials of the scene from one another. Naturally a HSI acquires spectral features of pixels, but spatial features based on neighborhood information are also important, which results in the problem of spectral-spatial classification. There are various ways to account to spatial information, one of which is through Mathematical Morphology, which is explored in this work. A HSI is a third-order data block, and building new spatial diversities may increase this order. In many cases, since pixel-wise classification requires a matrix of pixels and features, HSI data are reshaped as matrices which causes high dimensionality and ignores the multi-modal structure of the features. This work deals with HSI classification by modeling the data as tensors of high order. More precisely, multi-modal hyperspectral data is built and dealt with using tensor Canonical Polyadic (CP) decomposition. Experiments on real HSI show the effectiveness of the CP decomposition as a candidate for classification thanks to its properties of representing the pixel data in a matrix compact form with a low dimensional feature space while maintaining the multi-modality of the data.
{"title":"Hyperspectral Image Classification Based on Mathematical Morphology and Tensor Decomposition","authors":"Mohamad Jouni, M. Mura, P. Comon","doi":"10.1515/MATHM-2020-0001","DOIUrl":"https://doi.org/10.1515/MATHM-2020-0001","url":null,"abstract":"Abstract Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, where labels are given to pixels sharing the same features, distinguishing the present materials of the scene from one another. Naturally a HSI acquires spectral features of pixels, but spatial features based on neighborhood information are also important, which results in the problem of spectral-spatial classification. There are various ways to account to spatial information, one of which is through Mathematical Morphology, which is explored in this work. A HSI is a third-order data block, and building new spatial diversities may increase this order. In many cases, since pixel-wise classification requires a matrix of pixels and features, HSI data are reshaped as matrices which causes high dimensionality and ignores the multi-modal structure of the features. This work deals with HSI classification by modeling the data as tensors of high order. More precisely, multi-modal hyperspectral data is built and dealt with using tensor Canonical Polyadic (CP) decomposition. Experiments on real HSI show the effectiveness of the CP decomposition as a candidate for classification thanks to its properties of representing the pixel data in a matrix compact form with a low dimensional feature space while maintaining the multi-modality of the data.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134072056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Burgeth, A. Kleefeld, Benoît Naegel, Benjamin Perret
Abstract This editorial presents the Special Issue dedicated to the conference ISMM 2019 and summarizes the articles published in this Special Issue.
这篇社论介绍了ISMM 2019会议特刊，并总结了该特刊上发表的文章。
{"title":"Editorial — Special Issue: ISMM 2019","authors":"B. Burgeth, A. Kleefeld, Benoît Naegel, Benjamin Perret","doi":"10.1515/mathm-2020-0200","DOIUrl":"https://doi.org/10.1515/mathm-2020-0200","url":null,"abstract":"Abstract This editorial presents the Special Issue dedicated to the conference ISMM 2019 and summarizes the articles published in this Special Issue.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123388235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Teo Asplund, C. L. Hendriks, M. Thurley, R. Strand
Abstract This paper proposes a way of better approximating continuous, two-dimensional morphology in the discrete domain, by allowing for irregularly sampled input and output signals. We generalize previous work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentally we show improved results over regular, discrete morphology with respect to the approximation of continuous morphology. It is also worth noting that the number of output samples can often be reduced without sacrificing the quality of the approximation, since the morphological operators usually generate output signals with many plateaus, which, intuitively do not need a large number of samples to be correctly represented. Finally, the paper presents some results showing adaptive morphology on irregularly sampled signals.
{"title":"Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions","authors":"Teo Asplund, C. L. Hendriks, M. Thurley, R. Strand","doi":"10.1515/MATHM-2020-0104","DOIUrl":"https://doi.org/10.1515/MATHM-2020-0104","url":null,"abstract":"Abstract This paper proposes a way of better approximating continuous, two-dimensional morphology in the discrete domain, by allowing for irregularly sampled input and output signals. We generalize previous work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentally we show improved results over regular, discrete morphology with respect to the approximation of continuous morphology. It is also worth noting that the number of output samples can often be reduced without sacrificing the quality of the approximation, since the morphological operators usually generate output signals with many plateaus, which, intuitively do not need a large number of samples to be correctly represented. Finally, the paper presents some results showing adaptive morphology on irregularly sampled signals.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":" 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113948349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Guimarães, Y. Kenmochi, J. Cousty, Zenilton K. G. Patrocínio, Laurent Najman
Abstract The original version of the article was published in Mathematical Morphology - Theory and Applications 2 (2017) 55–75. Unfortunately, the original version contains a mistake: in the definition of Dif (C1, C2) in Section 3.6, max should be replaced by min. In this erratum we correct the formula defining Dif (C1, C2).
{"title":"Erratum to “Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity: the case of the Felzenszwalb-Huttenlocher method”","authors":"S. Guimarães, Y. Kenmochi, J. Cousty, Zenilton K. G. Patrocínio, Laurent Najman","doi":"10.1515/mathm-2019-0010","DOIUrl":"https://doi.org/10.1515/mathm-2019-0010","url":null,"abstract":"Abstract The original version of the article was published in Mathematical Morphology - Theory and Applications 2 (2017) 55–75. Unfortunately, the original version contains a mistake: in the definition of Dif (C1, C2) in Section 3.6, max should be replaced by min. In this erratum we correct the formula defining Dif (C1, C2).","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133241721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We consider Hausdorff discretization from a metric space E to a discrete subspace D, which associates to a closed subset F of E any subset S of D minimizing the Hausdorff distance between F and S; this minimum distance, called the Hausdorff radius of F and written rH(F), is bounded by the resolution of D. We call a closed set F separated if it can be partitioned into two non-empty closed subsets F1 and F2 whose mutual distances have a strictly positive lower bound. Assuming some minimal topological properties of E and D (satisfied in ℝn and ℤn), we show that given a non-separated closed subset F of E, for any r > rH(F), every Hausdorff discretization of F is connected for the graph with edges linking pairs of points of D at distance at most 2r. When F is connected, this holds for r = rH(F), and its greatest Hausdorff discretization belongs to the partial connection generated by the traces on D of the balls of radius rH(F). However, when the closed set F is separated, the Hausdorff discretizations are disconnected whenever the resolution of D is small enough. In the particular case where E = ℝn and D = ℤn with norm-based distances, we generalize our previous results for n = 2. For a norm invariant under changes of signs of coordinates, the greatest Hausdorff discretization of a connected closed set is axially connected. For the so-called coordinate-homogeneous norms, which include the Lp norms, we give an adjacency graph for which all Hausdorff discretizations of a connected closed set are connected.
{"title":"Correspondence between Topological and Discrete Connectivities in Hausdorff Discretization","authors":"C. Ronse, L. Mazo, M. Tajine","doi":"10.1515/mathm-2019-0001","DOIUrl":"https://doi.org/10.1515/mathm-2019-0001","url":null,"abstract":"Abstract We consider Hausdorff discretization from a metric space E to a discrete subspace D, which associates to a closed subset F of E any subset S of D minimizing the Hausdorff distance between F and S; this minimum distance, called the Hausdorff radius of F and written rH(F), is bounded by the resolution of D. We call a closed set F separated if it can be partitioned into two non-empty closed subsets F1 and F2 whose mutual distances have a strictly positive lower bound. Assuming some minimal topological properties of E and D (satisfied in ℝn and ℤn), we show that given a non-separated closed subset F of E, for any r > rH(F), every Hausdorff discretization of F is connected for the graph with edges linking pairs of points of D at distance at most 2r. When F is connected, this holds for r = rH(F), and its greatest Hausdorff discretization belongs to the partial connection generated by the traces on D of the balls of radius rH(F). However, when the closed set F is separated, the Hausdorff discretizations are disconnected whenever the resolution of D is small enough. In the particular case where E = ℝn and D = ℤn with norm-based distances, we generalize our previous results for n = 2. For a norm invariant under changes of signs of coordinates, the greatest Hausdorff discretization of a connected closed set is axially connected. For the so-called coordinate-homogeneous norms, which include the Lp norms, we give an adjacency graph for which all Hausdorff discretizations of a connected closed set are connected.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115975603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The morphological discretization is most commonly used for curve and surface discretization, which has been well studied and known to have some important properties, such as preservation of topological properties (e.g., connectivity) of an original curve or surface. To reduce its high computational cost, on the other hand, an approximation of the morphological discretization, called the analytical approximation, was introduced. In this paper, we study the properties of the analytical approximation focusing on discretization of 2D curves and 3D surfaces in the form of y = f (x) (x, y Є R) and z = f (x, y) (x, y, z Є R). We employ as a structuring element for the morphological discretization, the adjacency norm ball and use only its vertices for the analytical approximation.We show that the discretization of any curve/surface by the analytical approximation can be seen as the morphological discretization of a piecewise linear approximation of the curve/surface. The analytical approximation therefore inherits the properties of the morphological discretization even when it is not equal to the morphological discretization.
形态离散化是曲线和曲面离散化中最常用的一种方法，它具有一些重要的性质，如保持原始曲线或曲面的拓扑性质(如连通性)。另一方面，为了降低其高昂的计算成本，引入了一种形态离散化的近似，称为解析近似。本文以y = f (x) (x, y Є R)和z = f (x, y, z Є R)的形式研究了二维曲线和三维曲面离散化的解析逼近的性质。我们采用邻接范数球作为形态学离散化的结构元素，并仅使用其顶点进行解析逼近。我们表明，任何曲线/曲面的解析近似离散化可以看作是曲线/曲面的分段线性近似的形态离散化。因此，解析近似即使在不等于形态离散化的情况下也继承了形态离散化的性质。
{"title":"On properties of analytical approximation for discretizing 2D curves and 3D surfaces","authors":"Fumiki Sekiya, A. Sugimoto","doi":"10.1515/mathm-2017-0002","DOIUrl":"https://doi.org/10.1515/mathm-2017-0002","url":null,"abstract":"Abstract The morphological discretization is most commonly used for curve and surface discretization, which has been well studied and known to have some important properties, such as preservation of topological properties (e.g., connectivity) of an original curve or surface. To reduce its high computational cost, on the other hand, an approximation of the morphological discretization, called the analytical approximation, was introduced. In this paper, we study the properties of the analytical approximation focusing on discretization of 2D curves and 3D surfaces in the form of y = f (x) (x, y Є R) and z = f (x, y) (x, y, z Є R). We employ as a structuring element for the morphological discretization, the adjacency norm ball and use only its vertices for the analytical approximation.We show that the discretization of any curve/surface by the analytical approximation can be seen as the morphological discretization of a piecewise linear approximation of the curve/surface. The analytical approximation therefore inherits the properties of the morphological discretization even when it is not equal to the morphological discretization.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133254766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Guimarães, Y. Kenmochi, J. Cousty, Zenilton K. G. Patrocínio, Laurent Najman
Abstract This article is a first attempt towards a general theory for hierarchizing non-hierarchical image segmentation method depending on a region-dissimilarity parameter which controls the desired level of simpli fication: each level of the hierarchy is “as close as possible” to the result that one would obtain with the non-hierarchical method using the corresponding scale as simplification parameter. The introduction of this hierarchization problem in the form of an optimization problem, as well as the proposed tools to tackle it, is an important contribution of the present article. Indeed, with the hierarchized version of a segmentation method, the user can just select the level in the hierarchy, controlling the desired number of regions or can leverage on any of the tools introduced in hierarchical analysis. The main example investigated in this study is the criterion proposed by Felzenszwalb and Huttenlocher for which we show that the results of the hierarchized version of the segmentation method are better than those of the original one with the added property that it satisfies the strong causality and location principles from scale-sets image analysis. An interesting perspective of thiswork, considering the current trend in computer vision, is obviously, on a specific application, to use learning techniques and train a criterion to choose the correct region.
{"title":"Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity","authors":"S. Guimarães, Y. Kenmochi, J. Cousty, Zenilton K. G. Patrocínio, Laurent Najman","doi":"10.1515/mathm-2017-0004","DOIUrl":"https://doi.org/10.1515/mathm-2017-0004","url":null,"abstract":"Abstract This article is a first attempt towards a general theory for hierarchizing non-hierarchical image segmentation method depending on a region-dissimilarity parameter which controls the desired level of simpli fication: each level of the hierarchy is “as close as possible” to the result that one would obtain with the non-hierarchical method using the corresponding scale as simplification parameter. The introduction of this hierarchization problem in the form of an optimization problem, as well as the proposed tools to tackle it, is an important contribution of the present article. Indeed, with the hierarchized version of a segmentation method, the user can just select the level in the hierarchy, controlling the desired number of regions or can leverage on any of the tools introduced in hierarchical analysis. The main example investigated in this study is the criterion proposed by Felzenszwalb and Huttenlocher for which we show that the results of the hierarchized version of the segmentation method are better than those of the original one with the added property that it satisfies the strong causality and location principles from scale-sets image analysis. An interesting perspective of thiswork, considering the current trend in computer vision, is obviously, on a specific application, to use learning techniques and train a criterion to choose the correct region.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121550389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Aubry, Bertrand Kerautret, P. Even, Isabelle Debled-Rennesson
Abstract The segmentation or the geometric analysis of specular objects is known as a difficult problem in the computer vision domain. It is also true for the problem of line detection where the specular reflection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In order to overcome this limitation, a new strategy to detect thick segment is proposed. It is not based on the image gradient as usually, but rather exploits the image intensity profile defined inside a parallel strip primitive. Associated to a digital straight segment recognition algorithmwhich is robust to noise, this strategy was implemented to track metallic tubular objects in gray-level images. The efficiency of the proposed method is demonstrated through extensive tests using an actual industrial application. An alternate release intended to overcome the possible impact of the digitization process on the achieved performance is also introduced. Both strategies are discussed at the end of the article.
{"title":"Photometric Intensity Profiles Analysis for Thick Segment Recognition and Geometric Measures","authors":"N. Aubry, Bertrand Kerautret, P. Even, Isabelle Debled-Rennesson","doi":"10.1515/mathm-2017-0003","DOIUrl":"https://doi.org/10.1515/mathm-2017-0003","url":null,"abstract":"Abstract The segmentation or the geometric analysis of specular objects is known as a difficult problem in the computer vision domain. It is also true for the problem of line detection where the specular reflection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In order to overcome this limitation, a new strategy to detect thick segment is proposed. It is not based on the image gradient as usually, but rather exploits the image intensity profile defined inside a parallel strip primitive. Associated to a digital straight segment recognition algorithmwhich is robust to noise, this strategy was implemented to track metallic tubular objects in gray-level images. The efficiency of the proposed method is demonstrated through extensive tests using an actual industrial application. An alternate release intended to overcome the possible impact of the digitization process on the achieved performance is also introduced. Both strategies are discussed at the end of the article.","PeriodicalId":244328,"journal":{"name":"Mathematical Morphology - Theory and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123742629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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