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Data mining techniques were applied for the reduction of false positives in aviation explosives detection CT (computed tomography) imaging systems. An inductive post-detection classifier (PDC) was trained, implemented, and fielded. The PDC can only eliminate alarms generated from the existing detection system - it does not detect new alarms.
It is well known that the color of many natural and man-made objects is often very similar to that of human skin, such as sand, brick, to name a few. For the task of skin detection, it is often a very challenge task to identify the right skin locations while being robust against the distraction of these objects. In this paper, we present an on-line learning approach to model human skin by utilizing...
Graph-based semi-supervised learning approaches have been proven effective and efficient in solving the problem of the inefficiency of training samples in many real-world application areas, such as video annotation. As a significant factor of these algorithms, however, pairwise similarity metric has not been fully investigated. On the one hand, for general video annotation methods, the estimation...
In this work a learning algorithm for visual object tracking is presented. As object representation a fast computable set of Haar-like features is used and a weighted correlation is applied for the matching process. The object tracker utilizes the same set of features that is already calculated for object detection and thus it is possible to reuse features for detection and tracking. The feature's...
Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection...
This paper presents a novel an adaptive background subtraction method to segment the moving regions and locate the positions of human bodies. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the...
Feature selection is very important for road detection. Generally, optimal feature set is very hard to be determined manually by prior-knowledge. In this paper, a feature selection algorithm based on boosting is proposed. To fully utilize potential feature correlations, the features are combined. The feature vector is enlarged by the combined features, and the new feature vector is called raw feature...
Higher order local autocorrelation (HLAC) proposed by Otsu [5] is often used in the recent computer vision application such as gate recognition, object tracking, or video surveillance. The feature value of HLAC is the integral of the product of local pixels' value, and usually the integrals are calculated in entire images. However, in the image recognition, feature selection is often effective for...
For a large class of applications, there is time to train the system. In this paper, we propose a learning-based approach to patch perspective rectification, and show that it is both faster and more reliable than state-of-the-art ad hoc affine region detection methods. Our method performs in three steps. First, a classifier provides for every keypoint not only its identity, but also a first estimate...
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