Segmentation of images by color features: A survey

被引：136

作者：

Garcia-Lamont, Farid ^{[1
]}

Cervantes, Jair ^{[1
]}

Lopez, Asdrubal ^{[2
]}

Rodriguez, Lisbeth ^{[3
]}

机构：

[1] Univ Autonoma Estado Mexico, Ctr Univ UAEM Texcoco, Av Jardin Zumpango S-N, Texcoco 56259, Estado De Mexic, Mexico

[2] Univ Autonoma Estado Mexico, Ctr Univ UAEM Zumpango, Camino Viejo Jilotzingo Continuac Calle Rayon, Zumpango 55600, Estado De Mexic, Mexico

[3] Inst Tecnol Orizaba, Div Invest & Estudios Posgrad, Av Oriente 9,852 Col Emiliano Zapata, Orizaba Veracruz 94320, Mexico

来源：

NEUROCOMPUTING | 2018年 / 292卷

关键词：

Color spaces; Image segmentation; Quantitative evaluation; FUZZY C-MEANS; MINIMUM CROSS-ENTROPY; NEURAL-NETWORKS; UNSUPERVISED SEGMENTATION; AUTOMATIC SEGMENTATION; PALMPRINT RECOGNITION; LIVER SEGMENTATION; EFFICIENT METHOD; AERIAL IMAGES; ALGORITHM;

D O I：

10.1016/j.neucom.2018.01.091

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2018 Elsevier B.V. All rights reserved.

引用

页码：1 / 27

页数：27

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