自动目标识别算法性能评估中的图像度量

作为评估自动目标识别(Automatic Target Recognition,ATR)算法性能,图像度量是ATR性能评估中的非常重要的部分,并且与ATR算法的相关系与否对系统后的后续评价工作有很大的影响。文章先介绍了传统的图像质量,并分析了传统目标局部背景对比度度量(TBC)的使用条件及作为ATR算法性能评估的不足。基于此,引用了基于灰度共生矩阵的图像杂波度量(TIC)的方法来进行图像度量。最后设计了实验来论证TIC图像度量算法具有良好的单调性,从而使ATR算法性能获得更好的评估。     

自动目标识别中的图像序列质量评价方法

图像质量评价是自动目标识别(ATR)性能评估中的重要组成部分。传统的评价指标如目标信噪比(SNR)等针对的皆是单幅图像,而对于图像序列质量评价的研究尚属空白。针对该问题,该文首次提出了帧间目标变化程度的概念,利用其定量描述图像序列的质量。该指标融合了3部分信息:图像序列中帧间目标区纹理变化的信息,帧间目标大小变化的信息以及帧间目标位置不规律的信息。为验证所提指标的有效性,设计了用于分析帧间目标变化程度和ATR算法实际效果关系的实验,实验用样本为真实的目标图像序列。实验结果表明,帧间目标变化程度与ATR算法性能具有很强的相关性,基本呈现单调关系,是一种有效的图像序列质量评价标准。     

红外目标识别图像复杂度度量方法综述

红外图像复杂度度量方法不仅可以用于描述目标识别面临的复杂场景变化,而且在红外成像系统性能预测与评估、目标识别算法性能对比、建立和改进目标获取性能模型等方面也有广泛而重要的应用。给定了红外目标识别图像复杂度的定义,对该领域近年来最新出现的和部分经典的度量方法进行系统的归纳总结和对比分析,提出了度量方法选择的依据,指出了现有度量方法的缺点和不足,并指出红外目标识别图像复杂度度量未来将向着融合多种特征或者综合多种度量方法的趋势发展。     

基于Mean Shift的目标跟踪算法性能比较研究

针对目前尚未有对基于Mean Shift的各类目标跟踪算法在同一数据集下进行性能比较这一问题,选取了七种具有代表性的基于Mean Shift的目标跟踪算法,分别从算法时效性、跟踪成功率和跟踪精确度三个方面对算法在25段包括多种复杂场景的图像序列上的性能进行了仿真实验比较。实验结果给出了算法在不同评价指标以及不同图像场景下的性能表现。由实验得出的结论可以为基于Mean Shift的目标跟踪算法的进一步优化改进提供参考。     

利用改进特征金字塔模型的SAR图像多目标船舶检测

深度学习模型中的特征金字塔网络（Feature Pyramid Network,FPN）常被用作合成孔径雷达（Synthetic Aperture Radar,SAR）图像中多目标船舶的检测。针对复杂场景下多目标船舶检测问题,提出了一种基于改进锚点框的FPN模型。首先将特征金字塔模型嵌入传统的RPN（Region Proposal Network）并映射成新的特征空间用于目标检测,然后利用基于形状相似度距离（Shape Similar Distance,SSD）度量的Kmeans聚类算法优化FPN的初始锚点框,并使用SAR船舶数据集测试。实验结果表明,所提算法目标检测精确率达到98.62%,在复杂场景下与YOLO、Faster RCNN、FPN based on VGG/ResNet等模型进行对比,模型准确率提高,整体性能更好。     

红外目标自动识别(ATR)算法性能评估的方法研究

本文对红外自动目标识别(ATR)算法性能评估的发展进行了系统的梳理,介绍了ATR算法性能评估过程中必不可少的工作条件限制以及性能指标的选取,并总结了目前ATR算法性能评估中经常采用的几种方法。     

复杂场景下单通道SAR目标检测及鉴别研究进展综述

SAR作为一种主动式微波成像传感器,以其全天时、全天候、作用距离远等独特的技术优势,成为当前对地观测的主要手段之一,在军事和民用领域发挥着十分重要的作用。随着SAR遥感技术的发展,高分辨率、高质量的SAR图像不断产生,仅依靠人工手段对感兴趣的目标进行检测、识别费时费力,因此亟需发展SAR自动目标识别(ATR)技术。典型的SAR ATR系统主要包括检测、鉴别、分类/识别3个阶段,其中,检测和鉴别阶段是整个SAR ATR系统的基础,是国内外雷达界一直开展的SAR应用基础研究之一。针对单通道SAR图像,简单场景下目标检测与鉴别已经取得了不错的结果;而在复杂场景下,杂波散射强度相对高、杂波背景非均匀和目标散射强度相对弱、分布密集等情况,使得SAR目标检测和鉴别依然是一个难点。该文对近十年左右复杂场景下单通道SAR目标检测及鉴别方法的研究进展进行了归纳总结,并分析了各类方法的特点及存在的问题,展望了未来复杂场景下单通道SAR目标检测与鉴别方法的发展趋势。      

采用光流估计的数字相机自动对焦算法

自动对焦技术对于数字相机至关重要，它是获取清晰图像的重要手段。针对复杂环境下多目标场景图像，提出了一种基于光流场估计的自动对焦算法。通过计算输入图像序列的光流场，对场景中的运动目标进行检测，根据目标运动属性准确判断出感兴趣目标。改进了Brenner清晰度评价方法，利用目标的二维边缘梯度信息建立评价函数，并且通过非线性增益提高评价函数的灵敏度，减小了噪声对评价值的影响。实验证明，该算法能够在主辅目标景深比达50倍的情况下分辨出感兴趣主目标，并在方差为0.02的随机噪声干扰下能有效地评价图像的清晰度；此算法将Brenner等评价函数的峰值稳定余量提高了1至4倍，对于不同图像具有良好的鲁棒性，易于硬件实现。     

复杂目标场景合成孔径雷达图像异质性 分析与测量方法研究

异质性是复杂目标场景合成孔径雷达(SAR)图像纹理信息变化程度的度量,本文针对基于变差系数、基于算术-几何均值比和基于信息论三种异质性分析方法的原理进行综述,给出了具体的测量实现方法与步骤,并对比了三种测量方法的测量结果.最后本文给出了异质性分析与测量应用于具有复杂目标场景SAR图像分割上的实验仿真,证明了异质性分析与测量能够描述SAR图像场景的复杂程度,为SAR图像分割提供有效的辅助手段.     

动态图像跟踪优化算法的研究与仿真

传统图像跟踪算法中,跟踪的图像搜索过程需要历遍所有特质,在图像场景较为复杂的情况下,在"无用"匹配点上耗费大量计算时间,跟踪过程误差较大。提出一种适用于复杂场景下动态图像跟踪优化算法,选择在复杂场景下鲁棒性较强的参数,以增加复杂场景下目标描述的信息量和稳定性;引入一种MAD(平均绝对差)匹配算法:在进行动态图像跟踪过程中采用MAD算法和鲁棒性参数相结合,通过动态帧图像和静态帧对比量即MCD(最多临近点),设定跟踪阈值,通过选取后的图像实现动态图像的运动目标跟踪。仿真实验结果表明,提出方法的跟踪精度对比传统方法有明显提高。     

客观的图象质量测度及其在分割评价中的应用

图象分割是图象分析中的关健步骤。对图象分割的评价是研究分割技术性能的有效手段,其中分割质量测度起着重要作用。本文介绍一类新的客观质量测度并对其性能进行了比较研究。另外为了验证新测度的有效性,本文提出了一种比较合理全面的分割技术分类法。在此基础上选取各类技术中有代表性的算法借助新的质量测度进行了实际评价比较并取得了有意义的结果。     

Adaptive integrated image segmentation and object recognition

The paper presents a general approach to image segmentation and object recognition that can adapt the image segmentation algorithm parameters to the changing environmental conditions. Segmentation parameters are represented by a team of generalized stochastic learning automata and learned using connectionist reinforcement learning techniques. The edge-border coincidence measure is first used as reinforcement for segmentation evaluation to reduce computational expenses associated with model matching during the early stage of adaptation. This measure alone, however, cannot reliably predict the outcome of object recognition. Therefore, it is used in conjunction with model matching where the matching confidence is used as a reinforcement signal to provide optimal segmentation evaluation in a closed-loop object recognition system. The adaptation alternates between global and local segmentation processes in order to achieve optimal recognition performance. Results are presented for both indoor and outdoor color images where the performance improvement over time is shown for both image segmentation and object recognition     

OBJECTIVE IMAGE QUALITY MEASURES AND THEIR APPLICATIONS IN SEGMENTATION EVALUATION

Image segmentation is a critical step of image analysis. Segmentation evaluation is an effective procedure for studying the performance of segmentation techniques, in which quality measure plays an important role. This paper presents a group of new objective quality measures for segmentation evaluation and compares their performances. In addition, to verify the effectiveness of these new measures, an appropriate classification of segmentation is proposed. According to this classification, several representative algorithms from different categories are selected for comparison testing. Some valuable results are obtained and presented.     

Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.     

Volume preserving image segmentation with entropy regularized optimal transport and its applications in deep learning

Image segmentation with a volume constraint is an important prior for many real applications. In this work, we present a novel volume preserving image segmentation algorithm, which is based on the entropy and Total Variation (TV) regularized optimal transport theory. The volume and classification constraints can be regarded as two measures preserving constraints in the optimal transport. By studying the dual problem, we develop a simple but efficient dual algorithm for our model. Moreover, to be different from many variational based image segmentation algorithms, the proposed algorithm can be directly unrolled to a new Volume Preserving and TV regularized softmax (VPTV-softmax) layer for semantic segmentation in the popular Deep Convolution Neural Network (DCNN). The experiment results show that our proposed model is very competitive and can improve the performance of many semantic segmentation networks such as the popular U-net and DeepLabv3+.     

基于红外运动目标分割的夜视融合系统设计

为了提高夜视系统的质量以及目标探测性能, 设计了红外视频运动目标与可见光融合夜视侦察系统。系统在硬件处理平台上实现了基于人眼视觉的红外运动目标分割算法和基于目标特性的加权融合算法, 采用红外运动目标分割电路和融合处理电路互联的结构, 首先利用FPGA为核心的红外目标分割电路提取红外视频中的运动目标, 然后将只有红外运动目标的视频输入后端DSP融合处理电路中进行融合处理, 最后从系统中输出一系列目标形态突出、背景细节清晰的融合序列。实验结果表明: 该系统可以提高夜视融合系统目标探测性能和探测概率, 融合结果的各项评价指标提高了90%以上, 有的评价指标甚至提高了7倍以上, 可以很大程度地降低系统的虚警率。     

Design and Analysis of an Efficient Evolutionary Image Segmentation Algorithm

Evolutionary image segmentation algorithms have a number of advantages such as continuous contour, non-oversegmentation, and non-thresholds. However, most of the evolutionary image segmentation algorithms suffer from long computation time because the number of encoding parameters is large. In this paper, design and analysis of an efficient evolutionary image segmentation algorithm EISA are proposed. EISA uses a K-means algorithm to split an image into many homogeneous regions, and then uses an intelligent genetic algorithm IGA associated with an effective chromosome encoding method to merge the regions automatically such that the objective of the desired segmentation can be effectively achieved, where IGA is superior to conventional genetic algorithms in solving large parameter optimization problems. High performance of EISA is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that EISA is robust and efficient using nature images with various characteristics.     

融合对称特性的混合标签传递半监督直觉模糊聚类图像分割

现有的直觉模糊聚类算法应用于图像分割时,往往只考虑图像的像素信息,忽略了图像的几何特征和区域信息,使得分割效果不太理想。为了提高直觉模糊聚类算法的分割性能,提出一种融合对称特性的混合标签传递半监督直觉模糊聚类算法。该算法首先对图像进行对称轴检测获取图像的对称特性,接着利用图像的对称特性进行对称像素的标签传递并改进像素对聚类中心的直觉模糊距离测度,然后设计一种混合标签传递半监督策略,对所有像素进行隶属度的估计并将其作为监督隶属度进行引入,随后构建融合对称特性的混合标签传递半监督直觉模糊聚类目标函数,通过聚类获得最终的分割结果。两个彩色图像库上的实验结果表明,该算法能够将目标从复杂背景中完整的分割出来,分割性能优于对比算法。      

一种柔性自适应SAR 图像目标分割算法

合成孔径雷达(SAR)图像分割是SAR 图像处理的基础,国内外研究者提出了很多行之有效的分割方法。典型的算法如基于单阈值形态学分割算法、基于马尔科夫随机场的分割算法等。然而,考虑实际需求,图像分割需要同时兼顾快速性和准确性,这是当前手段相对缺乏的。文中提出了一种柔性自适应SAR 图像目标分割算法,将峰值点的提取过程与恒虚警率检测算法相结合分割SAR 图像中的目标。该算法可以将散射中心信息融入到目标分割中,同时完成目标分割和峰值点提取,是一种快速而又精确的图像分割算法。最后,该文基于数据集对算法进行了验证,证实了该算法的合理性与可行性。     

基于超像素多特征融合的快速图像分割算法

为了提高高分辨率图像分割效率,解决复杂图案中待分割目标边缘附近前景与背景区分度小而造成的分割目标不完整问题,本文通过引入超像素HOG特征,提出了一种基于超像素多特征融合（superpixel multi-feature fusion,SMFF）的快速图像分割算法.首先采用目前最有效的超像素算法对待分割图像进行超像素预分割,然后提取基于超像素的HOG特征、Lab颜色特征和空间位置特征,设计基于超像素的多特征度量算法,最终采用图割理论实现了基于超像素多特征融合的快速图像分割.实验结果验证了本文算法的有效性,其算法性能接近于目前最经典图像分割算法,且本文算法的时间性能要明显优于其它对比算法.