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An interesting target detection framework with transferred deep convolutional neural network (CNN) is proposed. For CNN, many labeled samples are needed to train the multi-layer network. However, for target detection tasks, only few target spectral signatures are available, or they are unknown in anomaly detection. In this work, we employ a reference data and further generate pixel-pairs to enlarge...
In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage...
Pedestrian detection systems are receiving increasing attention in both industry and academia with the rapid development of autonomous automobiles which employ artificial intelligence. These systems must detect specific classes of objects such as pedestrians rather than generic objects. In this paper, we present a faster RCNN based pedestrian detection system which improves upon previous solutions...
This paper proposes a Fully Affine Invariant Feature (FAIF) detector which is based on affine Gaussian scale-space. The covariance matrix of Maximally Stable Extremal Region is interpreted as an isotropy measure of an image patch. A local anisotropic image patch can be supposed as an affine transformed isotropic image patch. Therefore, the affine deformation of a MSER can be estimated by its covariance...
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