Length estimators for digitized contours
Computer Vision, Graphics, and Image Processing
Algorithm 813: SPG—Software for Convex-Constrained Optimization
ACM Transactions on Mathematical Software (TOMS)
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
Deterministic Defuzzification Based on Spectral Projected Gradient Optimization
Proceedings of the 30th DAGM symposium on Pattern Recognition
High-Precision Boundary Length Estimation by Utilizing Gray-Level Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pixel Coverage Segmentation for Improved Feature Estimation
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Measurements of digitized objects with fuzzy borders in 2D and 3D
Image and Vision Computing
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
IEEE Transactions on Signal Processing
Estimation of moments of digitized objects with fuzzy borders
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Feature based defuzzification at increased spatial resolution
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
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We present a method for coverage segmentation, where the, possibly partial, coverage of each image element by each of the image components is estimated. The method combines intensity information with spatial smoothness criteria. A model for linear unmixing of image intensities is enhanced by introducing two additional conditions: (i) minimization of object perimeter, leading to smooth object boundaries, and (ii) minimization of the thickness of the fuzzy object boundary, and to some extent overall image fuzziness, to respond to a natural assumption that imaged objects are crisp, and that fuzziness is mainly due to the imaging and digitization process. The segmentation is formulated as an optimization problem and solved by the Spectral Projected Gradient method. This fast, deterministic optimization method enables practical applicability of the proposed segmentation method. Evaluation on both synthetic and real images confirms very good performance of the algorithm.