A survey on image segmentation

By K. Fu, J. Mui. 1981

Subfields: Computer Vision and Pattern Recognition

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. Yong Jae Lee
7. Dacheng Tao
8. Lijuan Wang

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 [22,57].”
• Understanding Deep Learning Techniques for Image Segmentation (2019, background): “There have been many reviews and surveys regarding the traditional technologies associated with image segmentation [61, 160].”
• Estimation of timber volume at the sample plot level by means of image segmentation and Landsat TM imagery (2001, methodology): “Reviews of segmentation methods have been presented by Fu and Mui (1981), Haralick and Shapiro (1985), and Pal and Pal (1993).”
• Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery (2006, background): “There are two important categories, edge detection and region extraction (Fu and Mui, 1981).”
• Evaluation of global image thresholding for change detection (2003, methodology): “In (Sahoo et al., 1988) previous work done by Weszka (1978) and Fu and Mui (1981) was updated , providing a well-presented taxonomy of different thresholding algorithms with limited testing of results (only three images).”
• Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine (2010, methodology): “Local behaviour-based methods (e.g. edge detection and region extraction; see Fu and Mui (1981)) analyse the variation of spectral features in a small neighbourhood.”
• A review of automatic mass detection and segmentation in mammographic images (2010, methodology): “Classical approaches to solving unsupervised segmentation are divided in three major groups (Fu and Mui, 1981): Region-based methods , which divide the image into homogeneous and spatially connected regions.”