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Since a vehicle logo is the clearest indicator of a vehicle manufacturer, most vehicle manufacturer recognition (VMR) methods are based on vehicle logo recognition. Logo recognition can be still a challenge due to difficulties in precisely segmenting the vehicle logo in an image and the requirement for robustness against various imaging situations simultaneously. In this paper, a convolutional neural...
We present an intensity neighborhood-based system for segmenting arbitrary biomedical image datasets using supervised learning. Because neighborhood methods are often associated with high-dimensional feature vectors, we explore a Principal Component Analysis (PCA) based method to reduce the dimensionality (and provide computational savings) of each neighborhood. Our results show that the system can...
This paper presents an algorithm using discriminative sparse representations to segment tissues in optical images of the uterine cervix. Because of the large variations in the image appearance caused by the changing of illumination and specular reflection, the different classes of color and texture features in optical images are often overlapped with each other. Using sparse representations they can...
In this paper, we introduce a new classifier ensemble approach, applied to tissue segmentation in optical images of the uterine cervix. Ensemble methods combine the predictions of a set of diverse classifiers. The main contribution of our approach is an effective way of combination based on each classifier's performance level-namely, the sensitivity p and specificity q, which also produces an optimal...
We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers...
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