Probe-based automatic target recognition in infrared imagery

A probe-based approach combined with image modeling is used to recognize targets in spatially resolved, single frame, forward looking infrared (FLIR) imagery. A probe is a simple mathematical function that operates locally on pixel values and produces an output that is directly usable by an algorithm. An empirical probability density function of the probe values is obtained from a local region of the image and used to estimate the probability that a target of known shape is present. Target shape information is obtained from three-dimensional (3-D) computer-aided design (CAD) models. Knowledge of the probe values along with probe probability density functions and target shape information allows the likelihood ratio between a target hypothesis and background hypothesis to be written. The generalized likelihood ratio test is then used to accept one of the target poses or to choose the background hypothesis. We present an image model for infrared images, the resulting recognition algorithm, and experimental results on three sets of real and synthetic FLIR imagery.     

基于自旋图的三维自动目标识别

将三维表面匹配领域的自旋图(spin-images)方法运用于扫描激光雷达自动目标识别中,基于自旋图特征,提出了平均最大相似性度量、候选对应关系数目、几何一致性筛选后对应关系数目、分组筛选后最大组的对应关系数目这4个识别指标,并将其组合构成组合识别准则,从而进行三维目标识别。同时针对实际应用需求,进一步研究了点云空间分辨率、激光雷达测距误差以及遮挡对目标识别率的影响,为激光雷达三维目标识别系统的设计提供了参考。     

图像目标的自动识别和快速筛选技术

针对战场环境和打击目标类型的不同,研究了图像目标自动识别和快速筛选技术,提出了大气云层背景下低信噪比弱信号小目标的检测截获、多目标边跟踪、边识别技术方案;然后针对海天图像的特点,分析了海杂波条件下两种海天背景的自适应分割算法(基于统计直方图的分割算法和基于梯度均值的分割算法);最后讨论了典型地面目标(阵列目标、港口目标等)的自动识别方法.仿真试验和外场测试试验验证了算法的有效性和实用性.     

混合因子模型在雷达目标识别中的应用

高分辨一维距离像是雷达自动目标识别的重要特征之一,它对目标姿态变化很敏感,只有通过进一步处理才能够实现有效的目标识别。针对距离像的这种姿态敏感性,本文提出了一种基于混合因子建模的雷达目标识别框架,它通过对从各个姿态角下获得的目标一维距离像出发构建目标的距离像概率生成模型,然后利用该模型通过比较条件概率大小的方法判别目标类属。对5类飞机数据的实验结果表明该框架对任意姿态角距离像的目标识别有很好性能。     

Foveal automatic target recognition using a multiresolution neuralnetwork

This paper presents a method for detecting and classifying a target from its foveal (graded resolution) imagery using a multiresolution neural network. Target identification decisions are based on minimizing an energy function. This energy function is evaluated by comparing a candidate blob with a library of target models at several levels of resolution simultaneously available in the current foveal image. For this purpose, a concurrent (top-down-and-bottom-up) matching procedure is implemented via a novel multilayer Hopfield (1985) neural network. The associated energy function supports not only interactions between cells at the same resolution level, but also between sets of nodes at distinct resolution levels. This permits features at different resolution levels to corroborate or refute one another contributing to an efficient evaluation of potential matches. Gaze control, refoveation to more salient regions of the available image space, is implemented as a search for high resolution features which will disambiguate the candidate blob. Tests using real two-dimensional (2-D) objects and their simulated foveal imagery are provided.     

自动目标识别(ATR)可靠性研究

对同类目标畸变不变的正确识别率与不同类目标分类误识别率是衡量一个自动目标识别 (ATR)系统的两个最重要性能指标。但在实际应用中 ,ATR系统所获取的外场的目标与背景总是处于随时间不断变化的条件下 ,与系统所存储的参考目标通常都不会一致 ,从而导致相关识别SNR劣化。特别对于多目标识别与不同类目标的区分 ,常规的相关门限判决方法会造成很大的误识别 ,大大影响了ATR系统的识别可靠性。本文采用人工神经网络 (ANN)与模糊逻辑技术 ,对相关信号与噪声进行实时数字后处理 ,通过对信号与噪声强度分布等高线而不仅仅是强度的识别 ,大大提高了ATR系统的识别可靠性 ,改善了识别效率。     

全套中间总线体系结构的电源方案

德州仪器(TI)最近以突破性的低价格推出两款新型高性能数字信号处理器(DSP)-TMS320C6410与TMS320C6413.这两款新型DSP具有TMS320C64x性能以及增加的内存、外设与低成本器件选项,而性能与TI在两年前推出的高端C64x器件相比提高了6倍多,而价格仅为17.95美元.     

Gaze-contingent automatic speech recognition

There has been progress in improving speech recognition using a tightly-coupled modality such as lip movement; and using additional input interfaces to improve recognition of commands in multimodal human? computer interfaces such as speech and pen-based systems. However, there has been little work that attempts to improve the recognition of spontaneous, conversational speech by adding information from a loosely?coupled modality. The study investigated this idea by integrating information from gaze into an automatic speech recognition (ASR) system. A probabilistic framework for multimodal recognition was formalised and applied to the specific case of integrating gaze and speech. Gaze-contingent ASR systems were developed from a baseline ASR system by redistributing language model probability mass according to the visual attention. These systems were tested on a corpus of matched eye movement and related spontaneous conversational British English speech segments (n = 1355) for a visual-based, goal-driven task. The best performing systems had similar word error rates to the baseline ASR system and showed an increase in keyword spotting accuracy. The core values of this work may be useful for developing robust speech-centric multimodal decoding system functions.     

采用DSP与小波变换实现声信号单目标识别

采用TMS320F2812数字信号处理器作为系统核心处理器件,辅以必要的外围电路实现声信号的采集、处理、特征提取及目标识别。根据典型目标声信号的特性,运用小波变换理论对其进行阈值滤波处理;利用小波分析能够反映信号时域和频域局部特性的优点,采用小波变换实现子空间能量特征提取;实现了声信号的快速处理与识别。将此系统应用于典型的车辆目标进行识别,取得了满意的识别效果。     

Recent advances in automatic speech recognition

The paper reviews the work done in speech recognition and understanding, mostly in the years 1976–1977. The attention is focussed on problems of system organization, use and representation of syntax and semantics, control strategies, lexical classification, extraction and emission of hypotheses about acoustic, phonetic and phonemic features.     

通信信号自动识别方法

通信信号的自动识别是通信信号处理的一个重要研究课题,近年来随着数字信号处理技术的发展,通信信号的调制方式增加了,对通信信号的自动识别提出了更高的要求.许多新的方法应用于该领域,本文对近年来这个领域的研究作了综合评述,讨论了其中存在的问题,并指出了今后的发展方向.     

SJ／T 11380—2008《自动声纹识别（说话人识别）技术规范》概述

本标准的某些内容有可能涉及专利,本标准的发布机构不应承担识别这些专利的责任. 本标准的附录A是规范性附录.     

Automatic target recognition organized via jump-diffusionalgorithms

Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the concatenation of simple rigid templates. The variability of the infinity of pose is accommodated via the actions of matrix Lie groups extending the templates to individual instances. The variability of target number and target identity is accommodated via the representation of scenes as unions of templates of varying types, with the associated group transformations of varying dimension. We focus on recognition in the air-to-ground and ground-to-air scenarios. The remote sensing data is organized around both the coarse scale associated with detection as provided by tracking and range radars, along with the fine scale associated with pose and identity supported by high-resolution optical, forward looking infrared and delay-Doppler radar imagers. A Bayesian approach is adopted in which prior distributions on target scenarios are constructed via dynamical models of the targets of interest. These are combined with physics-based sensor models which define conditional likelihoods for the coarse/fine scale sensor data given the underlying scene. Inference via the Bayes posterior is organized around a random sampling algorithm based on jump-diffusion processes. New objects are detected and object identities are recognized through discrete jump moves through parameter space, the algorithm exploring scenes of varying complexity as it proceeds. Between jumps, the scale and rotation group transformations are generated via continuous diffusions in order to smoothly deform templates into individual instances of objects.     

Algorithms for automatic modulation recognition of communicationsignals

This paper introduces two algorithms for analog and digital modulations recognition. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm it is found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB     

车牌自动识别技术探讨

汽车牌照识别技术是一个以特定目标为对象的专用计算机视觉系统，能从一幅图像中自动提取车牌图像，自动分割字符，进而对字符进行识别。该技术对采集到的汽车图像进行处理，能够实时准确地自动识别出车牌的数字、字母及汉字字符，并以计算机可直接运行的数据形式给出识别结果。     

基于微多普勒特征的空间锥体目标识别

空间锥体目标在大气层外飞行过程中存在不同的微动,目标的微动为进动,诱饵的微动为摆动或自旋.根据真假目标的微动差异,提出了低分辨雷达的目标识别方法.在多散射中心信号模型下分析了真假目标的微多普勒特性,指出目标回波信号微多普勒谱与其进动参数密切相关,而诱饵回波信号微多普勒谱与其摆动＼自旋频率密切相关.应用低分辨雷达多次回波...