Image Processing of Manganese Nodules Based on Background Gray Value Calculation

To troubleshoot two problems arising from the segmentation of manganese nodule images-uneven illumination and morphological defects caused by white sand coverage, we propose, with reference to features of manganese nodules, a method called“background gray value calculation”. As the result of the image procession with the aidthis method, the two problems above are solved eventually, together with acquisition of a segmentable image of manganese nodules. As a result, its comparison with other segmentation methods justifies its feasibility and stability. Judging from simulation results,it is indicated that this method is applicable to repair the target shape in the image, and segment the manganese nodule image in a short time. Also, it could be used tosynchronously process a large number of manganese nodules on different conditions in an image, laying a good foundation for automatic underwater manganese nodule survey. Even if the target in the image is slightly distorted, the statistical data of manganese nodules are still accurate. Moreover, other methods cannot be fully applied to the segmentation of manganese nodule images; in another word, the effectiveness and stability of this method are proved.     

A GLCM-Feature-Based Approach for Reversible Image Transformation

Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but also can recover the secret image without any loss. It also has been proved to be very useful in image content protection and reversible data hiding in encrypted images. However, the standard deviation (SD) is selected as the only feature during the matching of the secret and target image blocks in RIT methods, the matching result is not so good and needs to be further improved since the distributions of SDs of the two images may be not very similar. Therefore, this paper proposes a Gray level co-occurrence matrix (GLCM) based approach for reversible image transformation, in which, an effective feature extraction algorithm is utilized to increase the accuracy of blocks matching for improving the visual quality of transformed image, while the auxiliary information, which is utilized to record the transformation parameters, is not increased. Thus, the visual quality of the stego-image should be improved. Experimental results also show that the root mean square of stego-image can be reduced by 4.24% compared with the previous method.     

基于影像融合技术的合成孔径雷达(SAR)图像目标识别方法的探讨

对美国密歇根大学电子工程系的研究人员提出的一种多源数据融合算法进行了介绍，对SAR图像与可见光图像融合的一系列相关技术及其主要步骤进行了探讨，简要概括了评价融合后图像效果的标准和方法，并介绍了目标的检测与识别。     

点特征提取在锂电池丝印图像配准的应用

目的 为了提高锂电池丝印图像配准精度,从而解决产品质量检测中的漏检和误报问题,研究点特征提取算法在锂电池丝印图像配准中的应用.方法 对基于点特征的锂电池丝印图像配准进行综述,首先概述点特征提取算法的发展历程,然后着重围绕Harris,SIFT,SURF,ORB和AKAZE等5种经典的点特征提取算法进行分析,并介绍近几年的提升算法,最后对锂电池丝印图像进行配准测试,利用几种测评技术对实验效果进行分析,总结不同点特征提取算法在锂电池丝印图像配准中的优缺点和适用性.结果 实验结果表明,AKAZE算法提取的特征点具有较高的重复率和匹配准确率,经过配准后的定位误差也都控制在1个像素以内,但是该算法的尺度不变性较差.结论 相较于前4种算法,AKAZE算法具有较高的可靠性和稳定性,能够满足锂电池丝印图像配准的实时性和高效性需求.     

Research on Multi-View Image Reconstruction Technology Based on Auto-Encoding Learning

Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability. Then, Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures, and Generative Adversarial Networks (GAN) loss function is used to replace mean square error (MSE) to better express image features, discarding useless features to obtain effective image features. Finally, an incremental structure from motion (SFM) algorithm is performed to calculate rotation matrix and translation vector of the camera, and the feature points are triangulated to obtain a sparse spatial point cloud, and meshlab software is used to display the results. Simulation experiments show that compared with the traditional method, the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud, with an accuracy rate of 92.5% and a reconstruction complete rate of 83.6%.     

定量金相分析算法及实现

对定量金相学与体视学中所规定的最常用的几个守形参数的实验方法及计算机模拟实验方法的算法分别加以描述,实际应用表明,该算法具有速度快,精度高的特点。     

基于三角形几何相似性的图像配准与拼接

介绍了一种基于三角形几何相似性的图像配准方法.提取两幅待拼接图像的特征点,将每幅图像各自的重叠区域内或图像内容复杂情况下的选定区域内的特征点任意组合为三角形,得到分别对应于每一幅图像的三角形集合.然后根据定义的新的三角形表示方法,包括最大角方向和最小角方向,在两组三角形集合内层层筛选任意组合的三角形对,最终找到其中的匹配三角形对,即相似三角形对,从而找到匹配的点对.最后计算图像间变换矩阵,对图像进行拼接,得到了一张具有更宽视野的无缝拼接图.该方法只与特征点间相互几何位置有关,所以对两幅图像间的灰度差异、任意的旋转、缩放等都表现了很强的鲁棒性.     

无参考图像质量评价研究现状与前景分析

目的 为了进一步掌握无参考图像质量评价的发展过程与研究热点,为后续的相关研究提供参考。方法 使用citespace文献可视化软件对2000—2021年在Web of Science检索到的1 712条文献数据进行基本分析、共被引分析和     

Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification

For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy‐efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device‐to‐system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction.     

一种基于小波变换的遥感图像压缩算法

为验证图像压缩算法122.0-B-0对遥感图像的有效性,在对该算法进行了较为详细的研究后,对该算法进行了软件实现,然后将该算法与JPEG2000、SPIHT算法在压缩效率及压缩速度上进行了比较.实验结果表明:该算法在较低码率下压缩性能与JPEG2000、SPIHT算法相当,在较高码率下压缩性能略微下降,但在相同码率下它的编码速度比JPEG2000快2倍左右,比SPIHT算法约快1.5倍左右,且编解码速度与码率成正比.该算法采用的编码方式相对简单,无反馈操作,可适应于不同内存大小的压缩系统,并采用分段编码有效地防止误码扩散,因此在空间飞行器上具有巨大的应用价值.     

一种新的印刷图像检测系统的设计与实现

为了满足实际印刷生产中对大面积印品图像进行高速度、高精度配准检测的需要,设计出了一种新的印刷图像检测系统。该系统使用多个CCD同步获取图像信息,并采用CPLD配合PCI总线的方式实现图像数据的采集和传输控制,并采用了一种新的图像配准算法。系统相关程序和算法由DDK结合VC语言编程实现。实验表明,该系统基本满足了实时性要求,其图像配准的速度更快、精度更高、适应性更强,具有一定的实用价值。     

基于电子地图的遥感影像自动粗定位研究

在GIS的基础上,提出了一种新的遥感图像自动定位方法．将GIS技术和遥感技术相结合．利用GIS软件从电子地图中提取出线状图层一定范围内各点的经纬度坐标,将这些信息作为参考源．同时对遥感图像进行二值化和边沿提取,将提取出来的遥感图像的线状特征作为目标源,然后在分辨率匹配和投影变换的基础上对二者进行匹配,引入Hausdorff距离作为相似性测度,最后获取遥感图像的定位信息．     

像素级图像融合效果的评价方法研究

研究像素级图像融合效果的各种评价指标。对现有的指标总结分析。提出一些新的指标。并提出了根据图像融合目的选取效果评价的方法。     

多义图像合成算法研究

提出了一种基于小波变换的多义图像合成算法。该算法首先通过视角来选择小波分解级对两幅目标图像进行小波分解,然后根据可选的小波融合规则和权值变量融合并重构出在两个视角下的多义图像;对小波算法与现有两种算法进行了分析比较,通过试验证明该算法合成的结果多义特性更好,合成图视觉效果醒目并充分保留原目标图像的色彩和细节特征。合成结果采取了均值降噪。     

基于核竞争学习算法的图像特征提取

利用核函数学习可有效解决图像特征线性不可分的特性,结合稀疏表示算法的优势,提出了一种新的图像特征提取方法。采用基于竞争学习规则的独立分量分析法对图像进行稀疏表示,该算法可提取数据的高维特征,且不需要优化高阶的非线性函数和进行稀疏密度估计,因而有较快的收敛速度。与仅使用基于竞争学习的独立分量分析法相比,在PolyU数据库上的实验结果表明,采用基于核函数学习和稀疏表示相结合的方法所提取的数据特征有利于提高特征分类精度。     

基于智能像卡输入的手绘电气符号特征提取方法研究

提出了一种特定输入方式下脱机手绘电气符号的特征提取方法。采用一种新的书写载体和图像采样装置。在分析了以智能像卡输入的手绘电气符号的结构特点的基础上,给出了扩展网格构造方法,定义了扩展网格的比较特征,将其应用于手绘电气符号的特征提取。所提取的特征具备对不同书写笔画不规则变形的适应能力,有效地反映了手绘电气符号的局部和全局特征。识别实验表明,该特征提取方法是有效的。     

一种新的彩色图像碎片全自动拼接算法

目的为了实现横向和纵向分切的彩色图像碎片的拼接,提出针对同一张经过横向和纵向分切的彩色图像碎片建立关于相关系数的拼接复原模型和算法。方法通过获取彩色图像碎片的各单色图像,并提取单色图像边缘的灰度值,根据图像碎片边缘灰度值之间的相似程度自动拼接碎片。结果文中算法对彩色图像碎片的拼接效果优于常规算法,实验中采用100张彩色图,对每张彩图分切成64张300×300像素的彩图碎片进行顺序复原,综合拼接成功率达到100%,拼接平均耗时1.59 s。此外,文中算法实验性强,不仅能拼接仅纵切的图像,还能拼接横向和纵向分切的彩色图像。结论实验结果表明该算法对彩色图像碎片的拼接具有很好的适应性和可重复性,对图像碎片的大小和颜色无严格要求,是一套完整有效的针对规则彩色图像碎片的全自动拼接方案。     

基于量子核聚类算法的图像边缘特征提取研究

为了提高图像边缘特征提取质量,采取了量子核聚类算法。首先把像素映射量子编码,在码元建立域内对像素块进行随机采样;然后通过聚类距离计算数据点和每一个聚类核心的距离,把数据向量分配到距离最小的核心向量中,核函数确定有效影响范围;最后对像素聚类相异性分析,给出了算法流程。实验仿真显示这种算法对图像边缘特征提取轮廓清晰,连贯性好,评价指标MS和聚类准确率较好,算法收敛快。     

一种图像的显著区域提取方法

本文提出一种新的图像显著区域提取方法,分别提取原始图像的亮度、颜色、方向三个特征,并将三个特征的多尺度图像特征合并成一幅总的显著图;其中,在图像颜色特征提取中融入图像的频域特征,简化了算法的复杂度及实现难度,在图像方向特征提取中应用新的特征函数,使得方向特征图更加完善.实验结果显示本文方法相比较Itti算法显著图更为明显且易于实现,在图像目标重定位应用中图像形变少,效果更好.     

可见光和红外反相图像的SURF特征双向匹配

特征匹配的准确率影响图像配准的精度,是基于特征配准方法的重点和难点之一。为了解决单向最近邻/次近邻法所导致特征点一对多的误匹配问题,提出了一种红外和可见光图像的特征双向匹配方法。首先,对红外图像进行反相和直方图均衡化处理,增强两类图像的相似性,提取数量更多重复率高的共有特征;其次,对提取的SURF(Speed-up Robust Feature)特征进行双向最近邻/次近邻粗匹配,确保特征匹配的一致性,降低误匹配率,并利用RANSAC(Random Sample Consensus)算法对特征点进行二次匹配,实现特征点精确匹配。实验结果表明,该算法在正确匹配率和配准精度方面都优于传统SURF的单向最近邻/次近邻匹配方法,具有有效性。