Color image segmentation using edge and adaptive threshold value based on the image characteristics

Kwak, Nae-Joung; Kwon, Dongjin; Kim, Young-Gil; Ahn, Jae-Seong

doi:10.1109/ispacs.2004.1439118

Cited by 4 publications

(1 citation statement)

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“…An interesting feature of this trade-off parameter was its dependence on the level saturation. Other color segmentation edge-based techniques can be found in [37,50,89,176] . In [108] a system for quality control in citrus fruits was presented.…”

Section: Edge Detectionmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

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Section: Edge Detectionmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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A similarity measure between fuzzy regions to obtain a hierarchy of fuzzy image segmentations

Prados-Suárez

Sánchez

Martínez

2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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Abstract-In image segmentation it is well known that a given image can be analyzed with different detail levels, this is why some hierarchical approaches have been proposed to give a different segmentation for each detail level. Most of these proposals are specially designed for precise and well defined regions. However regions usually have blurred contours, soft color shades, and brightness that give rise to the problem of the imprecision in the regions. In this paper we face both problems considering the imprecision of the regions at the definition of the criteria to obtain a hierarchy detail levels. Concretely, we propose to calculate a similarity relation between fuzzy regions, based on two measures that take into account the imprecision in the transition between the regions, as well as the likeness of their characteristics. Then we use this fuzzy similarity relation to obtain a nested hierarchy of fuzzy segmentations by means of its α-cuts. In this way we obtain a tool to easily change the detail level and obtain a new fuzzy segmentation of the image, just changing the value of α.

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