Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Image Preprocessing
Pattern Recognition and Image Preprocessing
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Learning-based algorithm selection for image segmentation
Pattern Recognition Letters
A Learning Approach for Adaptive Image Segmentation
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2 (5th Edition) (OpenGL)
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Image Segmentation - A State-Of-Art Survey for Prediction
ICACC '09 Proceedings of the 2009 International Conference on Advanced Computer Control
Learning an interactive segmentation system
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
The Watershed Transform: Definitions, Algorithms and Parallelization Strategies
Fundamenta Informaticae
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The majority of vision research focusses on advancing technical methods for image analysis, with a coupled increase in complexity and sophistication. The problem of providing access to these sophisticated techniques is largely ignored, leading to a lack of application by mainstream applications. We present a feature-based clustering segmentation algorithm with novel modifications to fit a developer-centred abstraction. This abstraction acts as an interface which accepts a description of segmentation in terms of properties (colour, intensity, texture, etc.), constraints (size, quantity) and priorities (biasing a segmentation). This paper discusses the modifications needed to fit the algorithm into the abstraction, which conditions of the abstraction it supports, and results of the various conditions demonstrating the coverage of the segmentation problem space. The algorithm modification process is discussed generally to help other researchers mould their algorithms to similar abstractions.