Background: Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false nega...
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
We propose an unsupervised method for evaluating image segmentation. Common methods are typically based on evaluating smoothness within segments and contrast between them, and the...
Outliers due to occlusions and contrast and offset signal deviations notably hinder recognition and retrieval of facial images. We propose a new maximum likelihood matching score ...
Georgy L. Gimel'farb, Patrice Delmas, John Morris,...
In this paper, we present a Gaussian mixture model based approach to capture the spatial characteristics of any target signal in a sensor network, and further propose a temporally...