Abstract
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.
Highlights
Image segmentation is one of the most important object recognition stages for artificial vision systems
We analyzed the characteristics, advantages and disadvantages of the usual color spaces; the classic and current segmentation techniques; the methods for quantitative evaluation of algorithms performance using metrics to compute the quality of the segmented images; the areas where color image segmentation has been applied
Despite the RGB is usually employed for color image processing, it is not an adequate space for color processing because it is sensitive to illumination and the color changes within the space are not linear, the Euclidean distance cannot be used
Summary
Image segmentation is one of the most important object recognition stages for artificial vision systems. The algorithms for color image segmentation have been developed because color features may provide relevant data about the objects within the image. These algorithms have been applied in different areas such as medicine [9,55,127,160,193] and food analysis [47,108,111], among others [7,14,33,74,137,169,174].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
