On active contour models and balloons
CVGIP: Image Understanding
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Genetic Snakes for Medical Images Segmentation
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
A method for interactive shape detection in cattle images using genetic algorithms
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
LevelSet versus AGSnakes as mouth's shape extraction algorithm
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Automatic hippocampus localization in histological images using PSO-based deformable models
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Image space colonization algorithm
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Particle Swarm Optimization and Differential Evolution for model-based object detection
Applied Soft Computing
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The world of meat faces a permanent need for new methods of meat quality evaluation. Recent advances in the area of computer and video processing have created new ways to monitor quality in the food industry. In this paper we propose a segmentation method to separate connective tissue from meat. We propose the use of Genetic Snakes, that are active contour models, also known as snakes, with an energy minimization procedure based on Genetic Algorithms (GA). Genetic Snakes have been proposed to overcome some limits of the classical snakes, as initialization, existence of multiple minima, and the selection of elasticity parameters, and have both successfully applied to medical and radar images. We extend the formulation of Genetic Snakes in two ways, by exploring additional internal and external energy terms and by applying them to color images. We employ a modified version of the image energy which considers the gradient of the three color RGB (red, green and blue) components. Experimental results on synthetic images as well as on meat images are reported. Images used in this work are color camera photographs of beef meat.