On active contour models and balloons
CVGIP: Image Understanding
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Graphical Models and Image Processing
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
IFT-Watershed from Gray-Scale Marker
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
Fuzzy-connected 3D image segmentation at interactive speeds
Graphical Models
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Image Segmentation by Tree Pruning
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
An efficient method of license plate location
Pattern Recognition Letters
Holonic based approach to machine vision
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Links Between Image Segmentation Based on Optimum-Path Forest and Minimum Cut in Graph
Journal of Mathematical Imaging and Vision
Synergistic arc-weight estimation for interactive image segmentation using graphs
Computer Vision and Image Understanding
Fast interactive segmentation of natural images using the image foresting transform
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Engineering Applications of Artificial Intelligence
Spectral Image Segmentation Using Image Decomposition and Inner Product-Based Metric
Journal of Mathematical Imaging and Vision
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The Image Foresting Transform (IFT) is a tool for the design of image processing operators based on connectivity, which reduces image processing problems into an optimum-path forest problem in a graph derived from the image. A new image operator is presented, which solves segmentation by pruning trees of the forest. An IFT is applied to create an optimum-path forest whose roots are seed pixels, selected inside a desired object. In this forest, object and background are connected by optimum paths (leaking paths), which cross the object's boundary through its "most weakly connected" parts (leaking pixels). These leaking pixels are automatically identified and their subtrees are eliminated, such that the remaining forest defines the object. Tree pruning runs in linear time, is extensible to multidimensional images, is free of ad hoc parameters, and requires only internal seeds, with little interference from the heterogeneity of the background. These aspects favor solutions for automatic segmentation. We present a formal definition of the obtained objects, algorithms, sufficient conditions for tree pruning, and two applications involving automatic segmentation: 3D MR-image segmentation of the human brain and image segmentation of license plates. Given that its most competitive approach is the watershed transform by markers, we also include a comparative analysis between them.