A REVIEW ON IMAGE SEGMENTATION TECHNIQUES

By Pothiraj Sivakumar, D. Meenakshi. 2016

1,155 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jianfeng Gao
2. Zhe Gan
3. Luc Van Gool
4. Chunyuan Li
5. Jianwei Yang
6. Dacheng Tao
7. Yong Jae Lee
8. Lijuan Wang

In Evaluation & Benchmarks

1. Jianwei Yang
2. Jianfeng Gao
3. Zi-Yi Dou
4. Wilfried Philips
5. Bart Goossens
6. Zhe Gan
7. Yong Jae Lee
8. Lu Yuan

Citations in the corpus

• Generalized Decoding for Pixel, Image, and Language (2022, background): “Pixel-level image understanding, also known as image segmentation, has been a long-standing problem [23, 62].”
• Various Image Segmentation Techniques: A Review (2014, background): “COMPARISON Table I shows a comparison between various segmentation techniques by specifying a brief description of every method each with its advantages and disadvantages[27].”
• Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer (2014, methodology): “Image thresholding can be difficult in low SNR and nonstationary images but may be alleviated through the application of more complicated thresholding techniques 45 or via the gray-level distance transform.”
• MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, methodology): “The diversity of image processing applications has led to development of various techniques for image segmentation [45–54].”
• A comprehensive survey of recent trends in deep learning for digital images augmentation (2021, background): “DL is the secret to computer vision (Ponce and Forsyth 2012), image classifications, object recognition (Papageorgiou et al. 1998), image segmentation (Pal and Pal 1993), and more.”
• A Survey of Digital Map Processing Techniques (2014, methodology): “Edge detection techniques work on the principle of detecting contrasts in color values between regions and thus identifying discontinuities, i.e., abrupt changes in color values using local filtering operators (Fu and Mui, 1981; Pal and Pal, 1993).”