This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects...

This paper investigates the use of a hybrid neurocomputing approach to detect and then recognise images. The first technique creates and trains intelligent agents capable of detecting face images by using a Generalised Regression Neural Network (GRNN). The second technique further refines the search by recognising images from the detected data set using a feed forward backpropagation neural net...

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) and compares the result with conventional classifiers. The SVMs are tested for classification both in closed and o...

Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a...

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from degradation when it encountered a small sample set. In this paper, an integrated counterfactual generation and filtering approach is proposed to alleviate negative influence of The method consists...

This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which...

1 Introduction Corpus-based, quantitative approaches are important to the study of terminology , because terms are, unlike words, elements which can only be recognised at the level of language fact (Kageura 1995). Despite this, the only work which takes this approach is automatic term recognition Most of the simple and straightforward quantitative characterisations of terms have already been pu...

This paper presents an Automatic Target Recognition (ATR) system for laser radar (LADAR) imagery, designed to classify objects at multiple levels of discrimination (target detection, classification, and recognition) from single LADAR images. Segmentation is performed in both the range and non-range LADAR channels and results combined to increase object detection rate or decrease false positive ...

We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one ...

Due to the advantages of Synthetic Aperture Radar (SAR), study Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in case Convolutional Neural Network (CNN), works an end-to-end way and powerful feature-extracting abilities. Thus, researchers SAR ATR also seek solutions from deep learning. We review related algorithms with regard this paper. firstly introduce c...