An object segmentation method for the color slow-motion videos based on adjacent frames gradual change

Liu, Bin; Li, Haojie; Jia, Xianyong; Zhang, Hui; Huang, Rui; Liu, Zhaoliang; Xu, Zhao‐Dong

doi:10.1007/s11042-014-1981-7

Search citation statements

Order By: Relevance

Paper Sections

Select...

Color Space References1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RGB [8,22,30,51,68,72,94,106,108,119,136,142,146,157,165,170] HSV [30,67,69,100,105,118,137,150,189] HSI [30,67,69,100,105,118,137,150,189] L * a * b * [22,30,66,81,94,139,185] L * u * v * [30,125,155,160,165,191,193] YUV [23,26,27,80,…”

Section: Color Space Referencesmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

View full text Add to dashboard Cite

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.

show abstract