Abstract Sentiment analysis techniques have a long history in natural language processing and become standard tool the of political texts, promising conceptually straightforward automated method extracting meaning from textual data by scoring documents on scale positive to negative. However, while these kinds sentiment scores can capture overall tone document, underlying concept interest for is...

The main issues with machine learning-based feature extraction techniques are the requirement of extensive domain-level knowledge, experience, and need to be supported by large amounts data that sometimes not available. Moreover, it is often difficult apply knowledge extract necessary features for building a machine-learning classifier. Therefore, significantly important find develop depend mai...

It is known that a synthetic aperture radar (SAR) image obtained by matched filtering (MF)-based algorithms always suffers from serious noise, sidelobes, and clutters. However, the improvement of quality means complexity SAR system will increase, which affects application image. The introduction sparse signal processing technique into imaging proposes new way to solve this problem. Sparse recov...

Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces problems serious noise, sidelobes, clutters, which will bring down accuracy target classification. Different from MF-based result, sparse shows better quality with less noise higher signal-to-noise ratio (SNR...

This paper describes an approach to areas of FLIR target recognition; 1) target isolation 2) target classification. The method utilized for the isolation of potential target regions is based on localized texture information. The modality of the local gradient histogram is used to define both target regions and to segment these regions into subcomponents corresponding to the vehicle morphology (...