The main goal of the present paper is to propose new schemes for the overall evaluation of a speech translation system. These schemes are expected to support and improve the design of the target application system, and precisely determine its performance. Experiments are conducted on the Japanese-to-English speech translation system ATR-MATRIX, which was developed at ATR Interpreting Telecommun...

We present a method for estimating classi cation performance of a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system. Target classi cation is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are matched using a Bayes likelihood metric t...

ARTIFICIAL neural network (NN) architectures have been recognized for a number of years as a powerful technology for solving real-world image processing problems. The primary purpose of this special issue is to demonstrate some recent success in solving image processing problems and hopefully to motivate other image processing researchers to utilize this technology to solve their real-world pro...

This paper presents a novel framework for evaluating Target Detection and Classification algorithms and concepts of operations based on Augmented Reality (AR). Real sonar images and synthetic target models are used to generate a ground-truthed AR theatre of operation. The detection/classification results of the human operator or Automatic Target Recognition (ATR) algorithm to be evaluated are t...

Training deep learning-based synthetic aperture radar automatic target recognition (SAR-ATR) systems for use in an “open-world” operating environment has, thus far proven difficult. Most SAR-ATR are designed to achieve maximum accuracy a limited set of classes, yet ignore the implications encountering novel classes during deployment. Even worse, standard learning training objectives fundamental...

Traditionally, Inverse Synthetic Aperture Radar (ISAR) image frames are classified individually in an automatic target recognition system. When information from different image frames is combined, it is usually in the context of timeaveraging to remove statistically independent noise fluctuations between images. The sea state induced variability of the ship target projections between frames, ho...

Automatic target recognition (ATR) systems generally consists of three stages, as shown in Fig. 1 [1]: (1) a preprocessing stage (target detection stage) that operates on the entire image and extracts regions containing potential targets, (2) a clutter rejection stage that uses some sophisticated classi"cation technique to identify true targets by discarding the clutter images (false alarms) fr...

Among the current methods of synthetic aperture radar (SAR) automatic target recognition (ATR), unlabeled measured data and labeled simulated are widely used to elevate performance SAR ATR. In view this, setting semi-supervised few-shot vehicle is proposed use these two forms cope with problem that few available, which a pioneering work in this field. allusion sensitivity poses vehicles, especi...

We present an approach for vehicle classification in IR video sequences by integrating detection, tracking and recognition. The method has two steps. First, the moving target is automatically detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using an appearance-model based particle filter. The tracking result is evaluated at each frame. Low confidence ...