Ship detection and classification plays a significant role in naval warfare. Inverse Synthetic Aperture Radar (ISAR) images are being used extensively for feature extraction in ship detection and classification. The classification problem is solved in two steps. The first step is the extraction of features that characterize the target. The second step is to feed the computed feature values to a...

In this paper, several one-class classification methods are investigated in pixel space and PCA (Principal component Analysis) subspace having in mind the need of finding suitable learning and classification methods to support natural language grounding in the context of Human-Robot Interaction. Face and non-face classification is used as an example to demonstrate effectiveness of these one-cla...

A sequential Monte Carlo algorithm is suggested for joint maneuvering target tracking and classification, based on kinematic measurements. A mixture Kalman filter is designed for two-class identification of air targets: commercial and military aircraft. Speed and acceleration constraints are imposed on the target behaviour models in order to improve the classification process. The class is mode...

Multi-label text classification deals with problems in which each document is associated with a subset of categories. These documents often consist of a large number of words, which can hinder the performance of learning algorithms. Feature selection is a popular task to find representative words and remove unimportant ones, which could speed up learning and even improve learning performance. T...

This paper describes the classification function of naval targets performed by an infrared camera (IR) and an electrooptical camera (EO) that operate in a more complex multisensor system for the surveillance of a coastal region. The following naval targets are considered: high speed dinghy, motor boat, fishing boat, oil tanker. Target classification is automatically performed by exploiting the ...

Deep learning can be an effective and efficient means to automatically detect and classify targets in synthetic aperture radar (SAR) images, but it is critical for trained neural networks to be robust to variations that exist between training and test environments. The layers in a neural network can be understood to be successive transformations of an input image into embedded feature represent...

SHORT ABSTRACT: An algorithm is proposed for the fusion of multiple views of an object from each of two information sources – a synthetic aperture sonar (SAS) image and a bathymetric map. A parameter tied to the success of interferometric processing, and hence the reliability of the bathymetric estimates, automatically weights the relative contribution of each information source. The variation ...

The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound-target interactions has increased from 7000 to 665,000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from ...