基于FPGA的二值图像连通域快速标记

针对连通域标记算法运算量大、速度慢、硬件实现困难的缺点,提出一种适于现场可编程逻辑门阵列（FPGA）实现的二值图像连通域快速标记的算法,并用VHDL硬件开发语言在XILINX公司的FPGA上实现。实验结果表明了该算法能对二值图像复杂的连通关系正确标记,易于硬件实现,大大节约了硬件资源,电路结构简单,满足实时性要求。     

基于二值图像的卫星激光测距数据处理

为了改善激光测距数据处理中信号提取困难以及自动化程度还不高的现状,考虑激光测距数据的特征,结合二值图像处理和分析的技术提出一种新的提取测距信号的方法。先将原始数据映射为一幅二值图像,根据激光测距数据密度分布特点作第一次粗略的去噪,再根据信号形状特征作一次细去噪,将去噪后的图像矩阵逆映射回来。实验结果显示用于各种激光测距中的自动数据处理有很好的效果,可替代人工选点的激光测距数据提取方式。     

一种基于区域分割的SIFT图像特征提取算法

提出一种基于区域分割的SIFT图像特征提取算法.首先采用改进后的SIFT算法对图像进行计算,同时采用快速分割算法对原始图像进行分割,对照分割后的区域分别取每个区域最显著的几个SIFT特征,最后采用局部降维算法,将高维特征降低到可以接受的低维度特征.实验表明,该方法的运行时间短,对特征提取的位置界定准.     

基于区域分割的序列红外图像融合算法

针对传统的基于像素点和窗口策略的融合算法对图像特征表征的失真,提出了一种基于区域分割的序列图像融合算法.首先将序列红外图像分割为3个不同的特征区域,目标区域、背景区域以及灰度区域,并将分割结果映射到可见光图像中.随后,利用多尺度几何分析工具非下采样Contourlet变换(NSCT)有效提取图像特征的特点,根据不同区域的特性在NSCT域设计不同的融合规则.对试验结果进行主观和客观的对比,结果表明:基于区域分割的序列图像融合算法不仅能够为融合图像保留更全面、丰富的背景信息,还能够更加有效、准确地提取图像中的目标特征.该算法优于传统的基于像素点和窗口规则的融合算法,是一种有效可行的图像融合算法.     

基于Shearlet变换与区域分割的遥感图像融合

结合区域分割理论,提出了一种基于Shearlet变换 的遥感图像融合算法。首先通过色度H-亮度I-饱和度S(HI S) 变换对多光谱图像进行分解,将I分量与全色图像进行Shearlet变换,得到低 频、高频信息图;然后对 低频信息图进行基于灰度的区域分割,两图像的低频部分使用改进的加权融合算法改善融 合图像轮廓模 糊问题,以区域匹配度作为融合规则,高频分量采用区域清晰比作为融合规则,得到更多 的细节信息; 最后通过HIS逆变换得到融合图像。实验结果表明,本文算法 所得融合图像在有效地保持了多光谱图像光谱信息的同时,提高了空间细节信息。     

基于区域分割与相关反馈的高效图像检索算法

为了提高图像标注与检索的性能,提出了一种基于区域分割与相关反馈的图像标注与检索算法.该算法利用视觉特征与标注信息的相关性,采用基于区域的视觉特征对每幅图像采用聚类方法获得其一组视觉相似图像.通过计算与其距离最近的前三个分类的相似度,然后对这些关键字概率向量进行整合,获得最适合该图像的关键字概率向量,对图像进行标注.利用用户的反馈信息,修正查询关键词与每个分类之间的关系,进一步提高图像检索的准确性.实验结果表明,提出的算法具有更高的查准率与查全率.     

综合灰度形态学与模糊熵的红外图像区域分割

针对目标与背景有交叠的红外图像分割这一难题,首先对原始红外图像进行灰度形态学校正处理,然后利用模糊熵测度方法对校正后的红外图像进行分割,从而提出了一种综合利用灰度形态学与模糊熵的区域分割方法.与传统的阈值分割方法进行对比实验,结果表明,运用该方法得到的分割图像质量更好.     

二值图像连通域的分段标记算法及实现

在基于图像的精确制导中对标记算法的时间性能要求很高,常规的标记算法无法满足高速实时图像处理的需要.在分析现有算法的基础上,引入段的概念,以段为标记单位记录连通区域,在对图像一次扫描的过程中完成连通性传递闭包的计算.在标记的同时记录标记目标的边沿像素坐标值,计算目标的形心、面积、轨迹、灰度等特征.给出了算法的思路和实现步骤.使用该算法标记128×128的二值图像,用TMS320C6414 600 MHz simulator,可以在0.4 ms内完成.将文中算法与其他4种连通区域标记算法进行时间性能对比,明显优于其他算法.软件仿真结果也显示分段标记算法的正确性与稳定性.适用于高速运动目标的实时截获与跟踪.     

基于改进Mask R-CNN的绝缘子目标识别方法

为了提高绝缘子目标检测精度,缩短网络训练时间,提出基于卷积神经网络的InsuNet绝缘子目标识别方法.InsuNet网络采用56层卷积层作为主干网络,在特征提取网络的每个池化层后附加一层开运算,以过滤掉目标特征周围的干扰,增强算法鲁棒性.图像经主干网络传向区域提议网络提取感兴趣区域和主干网络前向传播的特征图共享给感兴趣区域对齐层固定特征尺寸,而后掩膜分支和目标分类、边界框回归分支并行输出ROI目标的掩膜、类别和回归框.结果表明,与Mask R-CNN模型对比,本方法在识别各种类型绝缘子时精确率达到94.7%,单张图像处理时间约为0.18 s,缩短了约40 ms.     

Target recognition in SAR images using radial Chebyshev moments

In this study, a new algorithm for classification of ground vehicles from standard synthetic aperture radar (SAR) images is proposed. Radial Chebyshev moment (RCM) is a discrete orthogonal moment that has distinctive advantages over other moments for feature extraction. Unlike invariant moments, its orthogonal basis leads to having minimum information redundancy, and its discrete characteristics explore some benefits over Zernike moments (ZM) due to having no numerical errors and no computational complexity owing to normalization. In this context, we propose to use RCM as the feature extraction mechanism on the segmented image and to compare results of the fused images with both Zernike and radial Chebyshev moments. Firstly, by applying different threshold target and shadow parts of each SAR images are extracted separately. Then, segmented images are fused based on the combination of the extracted segmented region, segmented boundary and segmented texture. Experimental results will verify that accuracy of RCM, which improves significantly over the ZM. Ten percent improvement in the accuracy is obtained by using RCM and fusion of segmented target and shadow parts. Furthermore, feature fusion improves the total accuracy of the classification as high as 6%.     

Hiding color watermarks in halftone images using maximum-similarity binary patterns

This paper presents a halftoning-based watermarking method that enables the embedding of a color image into binary black-and-white images. To maintain the quality of halftone images, the method maps watermarks to halftone channels using homogeneous dot patterns. These patterns use a different binary texture arrangement to embed the watermark. To prevent a degradation of the host image, a maximization problem is solved to reduce the associated noise. The objective function of this maximization problem is the binary similarity measure between the original binary halftone and a set of randomly generated patterns. This optimization problem needs to be solved for each dot pattern, resulting in processing overhead and a long running time. To overcome this restriction, parallel computing techniques are used to decrease the processing time. More specifically, the method is tested using a CUDA-based parallel implementation, running on GPUs. The proposed technique produces results with high visual quality and acceptable processing time.     

Inpainting of binary images using the Cahn-Hilliard equation.

Image inpainting is the filling in of missing or damaged regions of images using information from surrounding areas. We outline here the use of a model for binary inpainting based on the Cahn-Hilliard equation, which allows for fast, efficient inpainting of degraded text, as well as super-resolution of high contrast images.     

Salt and pepper noise removal in binary images using image block prior probabilities

During scanning and transmission, images can be corrupted by salt and pepper noise, which negatively affects the quality of subsequent graphic vectorization or text recognition. In this paper, we present a new algorithm for salt and pepper noise suppression in binary images. The algorithm consists of the computation of block prior probabilities from training noise-free images; noise level estimation; and the maximum a posteriori probability estimation of each image block. Our experiments show that the proposed method performs significantly better than the state of the art techniques.     

Delineation of ECT images using global constraints and dynamic programming

A model-based delineation algorithm is presented. It is a flexible model fitting algorithm, approaching contour detection as an optimization problem. An objective function is introduced, which depends not only on local contour features, but also on a global shape constraint. The latter is implemented as the similarity to the instance of a parametric shape model. The algorithm optimizes both the contour points and the parameters of the model. As a result, both global and local characteristics of the contour are determined as a compromise between photometric data and prior knowledge. The method was applied to myocardial perfusion SPECT images, to delineate the entire left ventricle (endocardium and epicardium), including possible regions of reduced perfusion. By adapting the balance between the image data and the shape model, images with different characteristics can be processed, including Thallium-201 and MIBI scans.     

基于虚拟曝光图像的立体高动态范围图像合成算法

本文提出基于多视点多曝光图像的立体高动态范围图像合成算法。首 先,考虑多视点多曝光 图像以及相机响应函数曲线的特性,提出一种虚拟曝光图像绘制算法,将不同曝光的图像绘 制到同一视点;然后, 为了使绘制曝光图像保留更多细节和结构,需要对绘制虚拟曝光图像进行空洞填补及边缘修 复,故引入了边缘差值 掩膜图,对图像边缘信息进行校正平滑处理;最后利用绘制的虚拟曝光图像合成立体高动态 范围图像。实验结果表 明,获得的绘制曝光图像与参考曝光视点图像之间的结构相似性高达0.99以上,且合成的 高动态范围图像质量高。     

Detection of binary images in film grain noise

Binary detection of photographic images in the presence of signal-dependent film grain noise has been investigated. The probability of error has been evaluated in terms of the SNR for three values of the signal-dependency parameter and a different number of repetitive samples of binary information.     

城市街区星载光学遥感图像车辆目标自动检测方法

针对星载光学遥感图像城市街区复杂背景问题,提出一种车辆目标自动检测方法.首先,利用场景中植被背景的物理属性,通过多光谱波段抑制场景中的植被背景,然后,在分析城市街区地物形态反射率特性的基础上,利用全色波段并结合二值形态学方法抑制场景中的建筑物,最后,引入著名的RX算法对抑制后的图像进行车辆目标检测.将文中提出的方法应用于实际Quickbird影像的车辆目标检测,结果表明所提出的方法具有鲁棒性强,执行效率高,不需要人工辅助等方面的特点,可用于城市街区车辆目标的自动检测.