A Spatial Thresholding Method for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Split-and-merge image segmentation based on localized feature analysis and statistical tests
CVGIP: Graphical Models and Image Processing
The image processing handbook (3rd ed.)
The image processing handbook (3rd ed.)
Automatic Extraction of Man-Made Objects from Aerial and Space Images
Automatic Extraction of Man-Made Objects from Aerial and Space Images
Markov random field modeled range image segmentation
Pattern Recognition Letters
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
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Multi-resolution segmentation, as one of the most popular approaches in object-oriented image segmentation, has been greatly enabled by the advent of the commercial software, eCognition. However, the application of multi-resolution segmentation still poses problems, especially in its operational aspects. This paper addresses the issue of optimization of the algorithm-associated parameters in multi-resolution segmentation. A framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type. The proposed framework was tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi-spectral images, based on a sampling scheme of all the parameters required by the algorithm. Results show that the feature-type-oriented segmentation evaluation provides an insight to the decision-making process in choosing appropriate parameters towards a high-quality segmentation. By adopting these feature-type-based optimal parameters, multi-resolution segmentation is able to produce objects of desired form to represent artificial features.