A survey of thresholding techniques
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Motion segmentation and qualitative dynamic scene analysis from an image sequence
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A performance measure for boundary detection algorithms
Computer Vision and Image Understanding
An improved seeded region growing algorithm
Pattern Recognition Letters
Spatiotemporal Segmentation Based on Region Merging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Unifying Region and Boundary Information
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Region growing: a new approach
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Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Thresholding using two-dimensional histogram and fuzzy entropy principle
IEEE Transactions on Image Processing
Regions adjacency graph applied to color image segmentation
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
A multiscale random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Representing moving images with layers
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Automatic segmentation of moving objects for video object plane generation
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Semiautomatic segmentation and tracking of semantic video objects
IEEE Transactions on Circuits and Systems for Video Technology
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
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ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
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VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Hypergraph Cuts & Unsupervised Representation for Image Segmentation
Fundamenta Informaticae
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CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Image segmentation using automatic seeded region growing and instance-based learning
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Unsupervised colour image segmentation using dual-tree complex wavelet transform
Computer Vision and Image Understanding
Multi-seed segmentation of tomographic volumes based on fuzzy connectedness
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
ICICA'10 Proceedings of the First international conference on Information computing and applications
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Image data field for homogeneous region based segmentation
Computers and Electrical Engineering
An embedded software-reconfigurable color segmentation architecture for image processing systems
Microprocessors & Microsystems
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Applied Soft Computing
Automatic recognition vision system guided for apple harvesting robot
Computers and Electrical Engineering
A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces
Computer Methods and Programs in Biomedicine
GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images
Knowledge-Based Systems
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
International Journal of Bioinformatics Research and Applications
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Seeded region growing (SRG) algorithm is very attractive for semantic image segmentation by involving high-level knowledge of image components in the seed selection procedure. However, the SRG algorithm also suffers from the problems of pixel sorting orders for labeling and automatic seed selection. An obvious way to improve the SRG algorithm is to provide more effective pixel labeling technique and automate the process of seed selection. To provide such a framework, we design an automatic SRG algorithm, along with a boundary-oriented parallel pixel labeling technique and an automatic seed selection method. Moreover, a seed tracking algorithm is proposed for automatic moving object extraction. The region seeds, which are located inside the temporal change mask, are selected for generating the regions of moving objects. Experimental evaluation shows good performances of our technique on a relatively large variety of images without the need of adjusting parameters.