Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Efficient greedy learning of Gaussian mixture models
Neural Computation
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Methods and Programs in Biomedicine
SMEM Algorithm for Mixture Models
Neural Computation
Bridging the Gap Between Simulation and Reality in Urban Search and Rescue
RoboCup 2006: Robot Soccer World Cup X
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
| Hi-index | 0.00 |
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.