Abstract
Image segmentation is a critical low-level visual routine for robot perception. However, most image segmentation approaches are still too slow to allow real-time robot operation. In this paper we explore a new method for image segmentation based on the expectation maximization algorithm applied to Gaussian Mixtures. Our approach is fully automatic in the choice of the number of mixture components, the initialization parameters and the stopping criterion. The rationale is to start with a single Gaussian in the mixture, covering the whole data set, and split it incrementally during expectation maximization steps until a good data likelihood is reached. Singe the method starts with a single Gaussian, it is more computationally efficient that others, especially in the initial steps. We show the effectiveness of the method in a series of simulated experiments both with synthetic and real images, including experiments with the iCub humanoid robot.
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Greggio, N., Bernardino, A., Santos-Victor, J. (2010). Image Segmentation for Robots: Fast Self-adapting Gaussian Mixture Model. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_12
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DOI: https://doi.org/10.1007/978-3-642-13772-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13771-6
Online ISBN: 978-3-642-13772-3
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