采用热核特征的SAR图像目标识别

为了解决SAR图像受相干斑噪声干扰和震后发生形变而识别率偏低的问题,提出了一种新的仿射、形变不变特征-热核特征,并将该特征用于SAR图像目标识别.首先采用推广的核模糊C-均值方法分割SAR图像,提取SAR图像目标形状;接着对目标形状进行Delaunay三角剖分,采用余切权重法对Laplace-Beltrami Operator离散化,通过离散化Laplace-Beltrami Operator特征值、特征向量求每一点热核特征;然后采用谱距离公式对点点间热核距离计算,转化为距离分布表示目标形状的热核特征;最后采用L1相似性准则对图像进行相似性度量,得到识别结果.实验表明:与经典的Hu不变矩方法相比,对于仿射变换和发生形变的SAR图像,该方法都具有更高的识别率.因此,基于热核特征的SAR图像识别方法是一种更加有效的识别方法.     

新闻视频中标题条检测及文字内容提取算法

提出了一种电视新闻节目中标题条自动检测及文字内容提取的算法.算法分为两步:第一步,根据标题条的形状特征对其进行快速检测,并根据文字在标题条中的位置等先验信息提取出文字区域图像;第二步,对文字图像进行预处理,并利用常用的OCR软件对所得到文字图像进行识别,得到标题条的文字信息可作为该新闻场景的内容描述.实验结果表明,该方法有较高的准确率和适应性,可用于新闻节目内容的自动分析和检索.     

基于BP神经网络的玻璃缺陷识别技术研究

为了准确、快速地对玻璃质量进行分类,提出一种基于BP神经网络的玻璃缺陷识别方法。由于不变矩与灰度共生矩阵分别可以描述图像的形状与纹理,在分析了缺陷灰度图像特点的基础上,将图像的纹理特征和不变矩特征融合,综合提取出一个分类能力更强的特征向量,再使用一个拟牛顿改进算法的三层前向BP网络。作为分类器,对常见的玻璃缺陷进行了识别。通过实验对比该方法和传统的单一特征识别法,证明该方法不仅具有更高的识别率,并且实时性较好,为玻璃缺陷的自动识别提供了一种新的途径。     

基于不变矩特征的图像识别

针对目标二维形状发生平移、旋转和尺度变换的情况,采用不变矩作为图像识别的特征量.图像灰度变化时,对应的直方图形状基本保持不变.将直方图的统计特征与不变矩相结合,提出了不变矩理论的图像识别方法.通过实验验证,这种方法对图像的亮度、对比度变化不敏感,可以有效地降低光照的影响,能够更准确地对图像进行识别.     

基于随机森林的雷达信号脉内调制识别

针对雷达信号脉内调制识别算法存在着准确率低的问题,提出一种新的雷达脉内调制类型自动识别方法,该方法首先提取雷达信号时频图像的形状特征和纹理特征构成融合特征,然后将融合特征输入随机森林分类器,实现信号的分类识别.仿真实验中对8种常见的不同调制类型的雷达信号进行识别,提出的算法在信噪比为-2 dB时识别准确率可以达到90％以上,验证了该方法的有效性.     

基于深度学习方法的单张零件图像重建网格模型

重建物体的三维形状是计算机图形学领域的一个研究热点,网格模型是一种常用的三维模型。文中基于深度学习方法提出一种从单幅机械零件图像重建其网格模型的方法。首先经过一个图像预处理过程将前景零件从背景中分离出来;其次基于ResNet和BSP-Net两个骨干网络建立一个新的网络结构,将前景零件图像重构为网格模型。该网络将零件的多个视图图像作为输入,并融合它们的重要特征。此外,加入形状先验损失引导模型的训练过程以优化重建结果。对螺母、螺栓和垫圈进行重建实验,验证了该方法的有效性。通过训练过程的损失函数曲线说明添加多视图特征融合和形状先验损失可以让损失收敛到更低的值。在三个评价指标上的测试表明,文中方法的重建结果优于ResNet+BSP-Net方法和IM-Net方法。     

地面红外目标图像识别方法研究

红外成像自动目标识别是精确制导武器的重要研究内容.针对地面复杂背景条件下,目标难以检测识别的问题,在分析地面目标红外图像场景的基础上,首先利用形态学技术对图像进行去噪和抑制背景,采用Otsu法对图像进行阈值分割并用形态学技术消除虚警目标点,用扩展像素标记法对图像进行区域标记,然后提取目标形状、矩和统计分布三类特征作为识别特征,并采用模糊综合评判方法识别目标.对实地拍摄的100幅坦克目标红外图像进行了识别实验,结果表明,该方法在坦克的典型作战环境下具有较强的抗噪性能和抑制背景结构干扰的能力,能有效识别出目标,并且易于用并行处理和硬件实现.     

基于Nonsubsampled Contourlet变换的SAR图像形状特征检索

Nonsubsampled Contourlet变换是一种非抽取得具有平移不变性的多尺度多方向的变换。将Canny算子和Nonsubsampled Contourlet变换(NSCT)[1]相结合,对图像运用Canny算子[2]提取边缘特征,再进行Nonsubsampled Contourlet变换,引入了三阶中心矩作为特征向量提取形状特征的算法。实现了基于Nonsubsampled Contourlet变换的图像形状特征检索,并将结果与基于2-D小波变换和基于Contourlet变换的图像形状特征检索作了比较,实验结果证明该方法的图像形状特征检索效率有较大的提高。     

基于轮廓分层描述的目标识别算法研究

有效的目标轮廓分段是描述目标局部特征的关键环节.针对现有轮廓描述算法存在轮廓分段不合理的问题,本文基于认知心理学,提出了分层描述的轮廓描述算法.算法思想是首先根据角点特征将整个轮廓划分成一些轮廓分段,接着对轮廓分段的分布特点提出价值尺度,然后将多级轮廓分段按照价值尺度原则合并得到有限个能够完整描述目标轮廓的特征分段,最后将特征分段综合考虑长度尺度应用到Shape Context相似度检测模型中进行目标识别.通过对MPEG-7图像数据库中的图像进行实验分析表明该算法能够完整描述目标图像的形状特征,提高了目标识别率和形状检索率,并对部分遮挡的目标也具有良好的鲁棒性.基本满足目标识别识别和形状检索对准确率、稳定性、抗遮挡能力等方面的要求.     

基于红外热像和权值直接确定神经网络的零值绝缘子识别方法

提出一种利用红外图像和神经网络权值直接确定法进行现场零值绝缘子识别的方法。识别的基本过程为:对红外图像进行图像增强、去噪、分割,在绝缘子串内截取矩形作为目标图像;鉴于红外热像的灰度值与物体表面温度的关系,直接提取了灰度标准偏差值、绝对偏差、四分差以及极差4个特征参数;将这4个参数作为权值直接确定法神经网络的输入来训练模型,并用于现场零值绝缘子的识别。该方法有效剔除了现场识别时输电线路的干扰,且能满足现场识别实时性要求。实验结果验证了该方法的可行性与有效性。     

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%.     

Preferential Image Segmentation Using Trees of Shapes

A novel preferential image segmentation method is proposed that performs image segmentation and object recognition using mathematical morphologies. The method preferentially segments objects that have intensities and boundaries similar to those of objects in a database of prior images. A tree of shapes is utilized to represent the content distributions in images, and curve matching is applied to compare the boundaries. The algorithm is invariant to contrast change and similarity transformations of translation, rotation and scale. A performance evaluation of the proposed method using a large image dataset is provided. Experimental results show that the proposed approach is promising for applications such as object segmentation and video tracking with cluttered backgrounds.      

基于多层运动历史图像的飞行时间相机人体运动识别

该文利用飞行时间(Time-Of-Fligh, TOF)相机提供的距离图像,在运动历史图像的基础上提出一种基于多层运动历史图像的人体运动识别方法。计算距离轮廓序列的运动能量图作为整体运动信息,同时根据距离变化量,计算前向、后向的多层运动历史图像作为局部运动信息,共同组成多层运动历史图像。为了解决Hu矩对不连续或具有噪声的形状较为敏感的问题,引入R变换对每层运动历史图像进行特征提取,串联形成特征向量送入SVM进行分类识别。实验结果表明,该识别方法可以有效识别人体运动。     

深度学习的MPCANet火灾图像识别模型设计

针对火灾发生时,火灾图像背景复杂、人工特征提取过程繁琐、对火灾图像的识别泛化能力不强、容易出现精度不高、误报和漏报等问题,提出了张量对象特征提取的多线性主成分分析（Multilinear Principal Component Analysis,MPCA）深度学习算法的火灾图像识别新方法。利用MPCANet建立火灾图像识别模型,通过MPCA算法学习滤波器作为深度学习网络卷积层卷积核,对张量对象的高维图像进行特征提取,并把蜡烛图像和烟花图像作为干扰。通过仿真实验并与其他火灾图像识别方法对比得到提出的图像识别方法识别精度达到了97.5%、误报率1.5%、漏报率1%。实验表明:该方法可以有效解决火灾图像识别存在的问题。     

高斯过程模型在SAR图像目标识别中的应用

将高斯过程模型应用于合成孔径雷达（SAR）图像目标识别。高斯过程模型是基于贝叶斯框架的统计学习算法,通过结合核函数和和概率判别构建分类模型。与传统分类模型相比,高斯过程模型可以获得更高的分类效率和精度。方法实施过程中,采用SAR图像的特征矢量作为输入,以目标类别标签作为输出训练高斯过程模型。对于待识别样本,通过计算其在高斯过程模型下属于各个类别的后验概率判定其目标类别。实验中,依托MSTAR数据集在典型条件下开展测试。根据实验结果,所提方法在标准操作条件下对10类目标识别精度达到99.28%;在30°和45°俯仰角下的平均识别率分别为98.04%和73.13%;在噪声干扰各个信噪比条件下均保持最高性能。实验结果验证了所提方法的有效性和稳健性。     

S2-aware network for visual recognition

Capturing the comprehensive information of various sizes and shapes of images in the same convolution layer is typically a challenging task in computer vision. There are two main kinds of methods for capturing those features. The first uses the inception structure and its variants. The second utilizes larger convolution kernels on specific layers or stacks with more convolution blocks. However, these methods can result in computationally intensive or vanishing gradients. In this paper, to accommodate feature distributions with different sizes, shapes and reduce computational cost, we propose a width- and depth-aware module named the WD-module to match feature distributions. Moreover, the proposed WD-module consumes less computational cost and parameters compared with traditional residual convolution layers. To verify the effectiveness of our proposed method, a size- and shape-aware backbone network named S²A-Net was built, which was obtained by stacking the WD-modules. By visualizing heat maps and features, the proposed S²A-Net can adapt to objects with different sizes and shapes in visual recognition tasks and learn more comprehensive characteristics. Experimental results show that the proposed method has higher accuracy in image recognition and outperforms other state-of-the-art networks with the same numbers of layers.     

多源红外弱小目标灰色关联融合识别方法

针对从复杂地物背景下识别红外弱小目标的需求,提出了一种针对中/长波红外图像的多源信息融合识别方法。首先在对低信噪比红外图像的预处理中改进了去相关和分块阈值分割等算法以抑制温度场的非线性影响,有效提取出目标及其特征,再利用基于灰色理论的目标关联度计算得到基本概率赋值,进一步运用D-S证据理论组合规则对多个观测样本的信任度进行决策融合,得到同一时刻多传感器探测样本数据的最终识别结果。实验表明,该方法对复杂地物背景下的红外弱小目标具有较高的识别效率。     

基于对称Gabor小波滤波的舰船目标识别方法

文中构建一种对称Gabor小波滤波器(SGWF),能够对逆合成孔径雷达(ISAR)像中舰船进行识别。由Gabor小波滤波器构建的SGWF 滤波器具有上下对称结构,对镜像翻转的图像滤波后仍具有镜像对称性。使用SGWF 与奇异值分解(SVD)相结合来提取特征,使得该方法可以避免因多普勒变化导致的ISAR正像和倒像的检测问题。SGWF可在不同尺度和方向对图像进行滤波,充分反映图像的纹理特征和强度特征。在实验中,使用九艘民船的实测ISAR 像进行识别,通过在成像过程加入高斯白噪声来检测算法对噪声的鲁棒性,并检验图像分块数量对算法性能的影响,以及检验训练样本数量对算法性能的影响,识别结果证明了算法的有效性。     

Feature-guided shape-based image interpolation

A feature-guided image interpolation scheme is presented. It is an effective and improved, shape-based interpolation method used for interpolating image slices in medical applications. The proposed method integrates feature line-segments to guide the shape-based method for better shape interpolation. An automatic method for finding these line segments is given. The proposed feature-guided shape-based method can manage translation, rotation and scaling situations when the slices have similar shapes. It can also interpolate intermediate shapes when the successive slices do not have similar shapes. This method is experimentally evaluated using artificial and real two-dimensional and three-dimensional data. The proposed method generated satisfactory interpolated results in these experiments. We demonstrate the practicality, effectiveness and reproducibility of the proposed method for interpolating medical images.     

Modeling Interaction for Segmentation of Neighboring Structures

This paper presents a new method for segmenting medical images by modeling interaction between neighboring structures. Compared to previously reported methods, the proposed approach enables simultaneous segmentation of multiple neighboring structures for improved robustness. During the segmentation process, the object contour evolution and shape prior estimates are influenced by the interactions between neighboring shapes consisting of attraction, repulsion, and competition. Instead of estimating the a priori shape of each structure independently, an interactive maximum a posteriori shape estimation method is used for estimating the shape priors by considering shape prior distribution, neighboring shapes, and image features. Energy functionals are then formulated to model the interaction and segmentation. With the proposed method, neighboring structures with similar intensities and/or textures, and blurred boundaries can be extracted simultaneously. Experimental results obtained on both synthetic data and medical images demonstrate that the introduced interaction between neighboring structures improves segmentation performance compared with other existing approaches.