Fractures in borehole images are currently handpicked by geologists, which is a tedious and expensive task.
Automatically detecting fractures in these images is not an easy task. We present a scheme for automatic fracture
detection in borehole images. First, an adaptive histogram equalization method is applied to enhance borehole images
which enhances the visibility of fractures in the images. Then, a direction filtering method is proposed to extract traces of
fractures in borehole images. Finally, the fast Hough transform is taken to detect fractures from the results of direction
filtering. Experiment results show that the scheme achieves the good results for automatic fracture detection in borehole
images.
Group actions play a key role in intelligent meetings. In this paper, we propose a probabilistic approach to online
segment meetings as a sequence of group actions, such as monologue, presentation, discussion, and break. In our
approach, we decompose group actions into three sub-actions according to three sorts of features independently: audio
features, video features and group visual features. In accordance with this assumption state spaces are decomposed into
two levels of resolution: meeting actions and meeting sub-actions. Multi-stream dynamic Bayesian network is
constructed based on three level state nodes modeling group actions, sub-actions, and three sorts of features. Particle
filters are applied to efficiently online recognize group actions, which is based on the estimate of joint posteriors over
node states of multi-stream dynamic Bayesian network. Posterior probabilities over all state spaces are represented by
temporal sets of their weighted samples. We make seven compared experiments with the different sample numbers of
50,100, 200,300,400, 500 and 600. The recognition accuracy gets higher when there are more sample numbers, but is
takes more time for event inference.
KEYWORDS: Binary data, Magnetic resonance imaging, Brain, Image segmentation, Neuroimaging, Tissues, Image classification, Visual process modeling, FDA class I medical device development, Data modeling
In this paper we propose an approach to model the posterior probability output of multi-class SVMs. The sigmoid
function is used to estimate the posterior probability output in binary classification. This approach modeling the posterior
probability output of multi-class SVMs is achieved by directly solving the equations that are based on the combination of
the probability outputs of binary classifiers using the Bayes's rule. The differences and different weights among these
two-class SVM classifiers, based on the posterior probability, are considered and given for the combination of the
probability outputs among these two-class SVM classifiers in this method. The comparative experiment results show that
our method achieves the better classification precision and the better probability distribution of the posterior probability
than the pairwise couping method and the Hastie's optimization method.
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