Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing Algorithms and Applications
Digital Image Processing Algorithms and Applications
An efficient watershed segmentation algorithm suitable for parallel implementation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Geodesic Active Contour Based Fusion of Visible and Infrared Video for Persistent Object Tracking
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
A Fast, Semi-automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Objective evaluation of video segmentation quality
IEEE Transactions on Image Processing
A downstream algorithm based on extended gradient vector flow field for object segmentation
IEEE Transactions on Image Processing
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
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This work proposes a robust fully automatic segmentation scheme based on the modified edge-following technique. The entire scheme consists of four stages. In the first stage, global threshold is computed. This is followed by the second stage in which positions and directions of the initial points are determined. Local threshold is derived based on the histogram of gradients from the third stage. Finally, in the fourth stage, searching procedure is started from each initial point to obtain closed-loop contours. The whole process is fully automatic. This avoids the disadvantages of semi-automatic schemes such as manually selecting the initial contours and points. Additionally, the sensitivity to the selection of the threshold value from the watershed schemes can be dramatically improved. The proposed automatic scheme can reduce human errors and operating time tremendously, it is also more robust than the conventional segmentation schemes and applicable on various image and video applications.