Automating Snakes for Multiple Objects Detection
Active contour or snake has emerged as an indispensable interactive image segmentation tool in many applications. However, snake fails to serve many significant image segmentation applications that require complete automation. Here, we present a novel technique to automate snake/active contour for multiple object detection. We first apply a probabilistic quad tree based approximate segmentation technique to find the regions of interest (ROI) in an image, evolve modifed GVF snakes within ROIs and finally classify the snakes into object and non-object classes using boosting. We propose a novel loss function for boosting that is more robust to outliers concerning snake classification and we derive a modified Adaboost algorithm by minimizing the proposed loss function to achieve better classification results. Extensive experiments have been carried out on two datasets: one has importance in oil sand mining industry and the other one is significant in bio-medical engineering. Performances of proposed snake validation have been compared with competitive methods. Results show that proposed algorithm is computationally less expensive and can delineate objects up to 30% more accurately as well as precisely.
KeywordsLoss Function Active Contour Model Adaboost Algorithm Gradient Vector Flow Decision Stump
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