Color image segmentation using histogram multithresholding and fusion

Kurugöllü, Fatih; Sankur, B.; Harmanci, A. Emre

doi:10.1016/s0262-8856(01)00052-x

Cited by 115 publications

(67 citation statements)

References 26 publications

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“…To avoid the partitioning of the 3D histogram in rectangular regions [32], here a direct look-up in the 3D histogram cubes instead of (multiple) one-dimensional color component thresholds is used. For this approach two 3D histogram cubes for foreground and background are accumulated using the provided training data.…”

Section: Ipk Gatersleben: Segmentation Via 3d Histogramsmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

241

181

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Image-based plant phenotyping is a growing application domain of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first of its kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge (LSC) of the Computer Vision Problems in Plant Phenotyping (CVPPP) workshop in 2014. Four methods are presented: three segment leaves via processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be achieved with satisfactory accuracy (>90% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Besides, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (http://www.plantphenotyping.org/CVPPP2014-dataset) to support future challenges beyond segmentation within this application domain.

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Section: Ipk Gatersleben: Segmentation Via 3d Histogramsmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

241

181

View full text Add to dashboard Cite

show abstract

“…The former group includes the adoption of both the SRM and an alternative approach, proposed in [17] and investigated for dermoscopic images in [18], named Multi-Thresholding and based on the Principal Component Analysis (PCA, also known as the discrete Karhunen-Loeve Transform or Hotelling Transform [19]). Structural techniques designed to search for primitive structures such as points, lines and circles, have been extensively adopted for automatically detecting texture and/or local networks in dermoscopic images.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

Leo¹,

Liguori²,

Paciello³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…Along that line, one approach to color image segmentation consists in blending the information carried by the segmentation maps of the different channels . The resulting map is often referred to as the so-called label concordance map (Richards and Jia, 1999;Kurugollu et al, 2001). The label concordance map (Fig.…”

Section: Concordance Of the Marginal Labelsmentioning

confidence: 99%

Supervised Automatic Histogram Clustering and Watershed Segmentation. Application to Microscopic Medical Color Images

Lézoray¹

2011

Image Anal Stereol

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In this paper, an approach to the segmentation of microscopic color images is addressed, and applied to medical images. The approach combines a clustering method and a region growing method. Each color plane is segmented independently relying on a watershed based clustering of the plane histogram. The marginal segmentation maps intersect in a label concordance map. The latter map is simplified based on the assumption that the color planes are correlated. This produces a simplified label concordance map containing labeled and unlabeled pixels. The formers are used as an image of seeds for a color watershed. This fast and robust segmentation scheme is applied to several types of medical images.

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Color image segmentation using histogram multithresholding and fusion

Cited by 115 publications

References 26 publications

Leaf segmentation in plant phenotyping: a collation study

Leaf segmentation in plant phenotyping: a collation study

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

Supervised Automatic Histogram Clustering and Watershed Segmentation. Application to Microscopic Medical Color Images

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