Image matching algorithm based on SIFT using color and exposure information

Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.

Integrating Color and Spatial Feature for Content-Based Image Retrieval

In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.

A Skeletal Camera Network for Close-range Images with a Data Driven Approach in Analyzing Stereo Configuration

Structure-from-Motion(SfM)techniques have been widely used for 3D geometry reconstruction from multi-view images.Nevertheless,the efficiency and quality of the reconstructed geometry depends on multiple factors,i.e.,the base-height ratio,intersection angle,overlap,and ground control points,etc.,which are rarely quantified in real-world applications.To answer this question,in this paper,we take a data-driven approach by analyzing hundreds of terrestrial stereo image configurations through a typical SfM algorithm.Two main meta-parameters with respect to base-height ratio and intersection angle are analyzed.Following the results,we propose a Skeletal Camera Network(SCN)and embed it into the SfM to lead to a novel SfM scheme called SCN-SfM,which limits tie-point matching to the remaining connected image pairs in SCN.The proposed method was applied in three terrestrial datasets.Experimental results have demonstrated the effectiveness of the proposed SCN-SfM to achieve 3D geometry with higher accuracy and fast time efficiency compared to the typical SfM method,whereas the completeness of the geometry is comparable.

A color image tracking method based on target characteristic hue decision

在无效区域 search.This 纸的常规关联匹配算法浪费伟人时间建议在目标特征色彩决定上基于关联搜索区域优化追踪方法的一种颜色。由确定并且减少 HSV (色彩浸透值) 的尺寸渲染空间，一个一个维的色彩空格被构造。在空间，目标特征色彩小粒集合被构造，它包含象在象素和瞄准中心之间的价值，区域和平均距离那样的属性。由使用小粒的计算方法，目标和搜索块的类似被获得，无效搜索区域能被移开。追踪实验的颜色证明了没有失去的精确，算法能为常规匹配的算法改进实时性能。

Multi-toning Image Adjustment

Due to the different lighting environments or other reasons, the pixel colors may be quite different in one image which causes distinct visual discontinuities. It makes the analysis and processing of such an image more difficult and sometime impossible. In this paper, a unified multi-toning image adjustment method is proposed to solve this problem. First, a novel unsupervised clustering method was proposed to partition the source and the target image into a certain number of subsets with similar color statistics. By matching the texture characteristics and luminance distribution between the blocks, it can create optimized correspondence. Then, the color information was transferred from the matched pixels in the source blocks to the target ones. Graph cut method was used to optimize the seams between different subsets in the final step. This method can automatically perform color adjustment of a multi-toning image. It is simple and efficient. Various results show the validity of this method.

Efficient Stereo Matching Scheme Based on Graph Cuts

A new stereo matching scheme from image pairs based on graph cuts is given,which can solve the problem of large color differences as the result of fusing matching results of graph cuts from different color spaces.This scheme builds normalized histogram and reference histogram from matching results,and uses clustering algorithm to process the two histograms.Region histogram statistical method is adopted to retrieve depth data to achieve final matching results.Regular stereo matching library is used to verify this scheme,and experiments reported in this paper support availability of this method for automatic image processing.This scheme renounces the step of manual selection for adaptive color space and can obtain stable matching results.The whole procedure can be executed automatically and improve the integration level of image analysis process.

Regular Stereo Matching Improvement System Based on Kinect-supporting Mechanism

In this paper, we built a stereoscopic video associated experimental model, which is referenced as Kinect-supporting improved stereo matching scheme. As the depth maps offered by the Kinect IR-projector are resolution-inadequate, noisy, distance-limited, unstable, and material-sensitive, the appropriated de-noising, stabilization and filtering are first performed for retrieving useful IR-projector depths. The disparities are linearly computed from the refined IR-projector depths to provide specifically referable disparity resources. By exploiting these resources with sufficiency, the proposed mechanism can lead to great enhancement on both speed and accuracy of stereo matching processing to offer better extra virtual view generation and the possibility of price-popularized IR-projector embedded stereoscopic camera.

基于双目视觉的柔性塑胶套壁厚检测系统研究

为了完成锥形波纹管形状的柔性塑胶防尘套壁厚的检测,建立一个基于双目立体视觉模型的测量系统。该系统中的两个工业数字摄像机通过PLC给予的外触发对工件同时进行图像采集,对采集的多组图像进行相应的预处理后,采用特征点提取方法实现图像拼接,运用特征匹配算法提取出相应待测点三维坐标信息,完成柔性塑胶防尘套壁厚检测。该测量系统,使用气动柔性夹爪夹取工件,减少柔性工件的夹取变形影响,使用高精度移动滑台带动工件运动到拍照位置,通过控制滑台特定的移动距离和对图形特征提取结合方法对多次拍照后的工件图像进行特征点筛选和图像拼接,完成工件壁厚的高精度检测。实验数据表明,该测量系统绝对检测精度达±0.05mm以内,简单快捷,缩短了工件生产工作中的设备调整时间。

基于轻量化PASMnet神经网络的立体匹配方法

针对传统方法用于立体匹配、三维地形重建效果差,而深度学习的方法模型大、速度慢的问题,提出了一种轻量化PASMnet神经网络的立体匹配方法。首先,对PASMnet进行结构化剪枝,精化参数配置,减少模型参数数量,提升模型运行速度。然后对归一化后的图像进行极线校正,得到成像平面平行的立体影像,进行立体匹配任务。在KITTI2015数据集上进行验证,实验结果表明改进的模型参数量减少为原模型的1/6,运行时间为0.56 s。剪枝量化后的模型运行速度更快,推理时间缩短,并且可以用于月面影像的立体匹配,拓展了模型应用。

一种基于Integral Imaging和多基线立体匹配算法的深度测量方法

Integral Imaging(II)是一种能够记录和显示全真三维场景的图像技术.该技术采用微透镜阵列记录空间场景,空间任意一点的深度信息只需通过一次成像即可直接获得.本文研究结合多基线立体匹配算法采用II直接获取物体空间信息的方法.其结果既可为下一代基于II的三维电视图像的数据处理提供应用基础,同时也可望应用于开发新型的深度测量工具.

面向海岛海岸带区域的高分遥感影像智能化色彩增强方法

原始高空间分辨率海岛海岸带遥感影像往往存在影像灰暗、偏色、地物信息较难辨识的现象。为及时获取清晰、信息丰富、反差适中、亮度均匀的海岛礁遥感影像,满足日益强烈的海岛海岸带地理信息保障需求,针对海岛海岸带高空间分辨率遥感影像,该文提出一种深度学习结合改进直方图匹配的智能化调色方法。首先,进行数据重采样与自适应分块获取抽稀影像;其次,应用MBLLEN网络对抽稀影像进行真彩色增强;最后,采用改进直方图匹配的方法对原始影像进行色彩映射,最终得到符合人眼视觉、色彩一致、细节丰富的遥感影像。采用主客观相结合的方式综合评价调色效果,结果表明:相较于Retinex,HE和MASK等常用调色方法,该文算法结果更符合人眼视觉、色彩一致、细节丰富,可有效改善海岛海岸带高空间分辨率遥感影像视觉效果,较好地保留原始地物的细节信息,大幅提升调色效率。

结合注意力机制的卫星遥感影像立体匹配

采用立体匹配技术对多视卫星遥感影像进行三维场景重建一直是摄影测量与遥感领域的核心问题。基于卷积神经网络的深度学习方法极大地促进了立体匹配技术的发展,然而其中涉及匹配困难和误匹配问题的相关研究仍然不足。为了提升卫星遥感影像不适定区域中视差估计的精度,本研究提出了一种结合注意力机制的立体匹配深度学习网络,在特征提取模块中加入注意力机制,分别从通道和空间两个维度捕获全局信息,对特征进行优化;在代价体的构建模块中构建新的代价体积,并重新设置视差的回归范围。为了验证本文方法的有效性,在US3D、WHU-Stereo两个数据集上分别与已有方法Stereo-Net、PSM-Net进行了比较分析。结果表明,本文方法在EPE(endpointerror)和D_1两个指标上均能达到最优,取得了较好的性能,提高了立体匹配的精度,尤其在无纹理、重复纹理、遮挡及视差不连续区域表现出良好的鲁棒性。

基于双目识别技术的铝合金轮毂尺寸测量算法研究

针对双目识别技术以铝合金轮毂工件作为研究对象,利用MATLAB设计测量程序,并完成对铝合金轮毂工件的尺寸测量。首先,利用张正友标定法快速准确得出标定系数;然后,对轮毂图像进行直方图均衡化、中值滤波、自动阈值分割、Canny边缘检测、开运算等图像预处理,并对图像进行畸变校正和极线校正;最后,完成立体匹配得到其三维坐标,进行拟合计算处理得到轮毂尺寸,精度满足需求。

基于深度滤波的高精度视差图研究

针对由于激光雷达扫描特性所造成的点云拖尾及遮挡区域的歧义投影问题,提出了一种基于深度滤波的视差图像滤波算法,对非结构化的点云数据进行处理分析。首先,利用迭代几何编码代价体(IGEV)计算出双目深度估计视差图;然后将点云投影到对应的彩色图像得到投影视差图,以双目深度估计视差图作为引导图像,在对应的像素位置上对点云投影图进行一致性比较。由于激光雷达在近距离表现不佳,本文还创造性地提出了区分前景和背景的阈值判断方法。与德国卡尔斯鲁厄理工学院和美国芝加哥丰田理工学院(KITTI)计算机视觉算法评测数据集进行了对比实验,其实验结果表明,与KITTI算法相比,该算法在检测精度上提升了0.72%,验证了本文方法的有效性。

多基高比定位精度影响因素仿真与分析

文章针对遥感立体测绘卫星体制选择与设计方案,结合目前测绘能力的现状,通过分析传统大基高比、小基高比以及双基高比测绘模式的优势和瓶颈,提出多基高比航天测绘模式,来适应山地建筑等不同地形条件的测绘任务。根据立体成像模型分析定位精度影响因素,构建数值模拟场景,结合解析空中三角测量方法,得到不同匹配误差与平面精度和高程精度的关系。仿真结果表明:对于不同程度的匹配误差,0.15至0.6左右的较小基高比参数适用于对平面精度要求高的地形特点,0.8左右至1.2的较大基高比适用于对高程精度要求高的地形特点,根据相应地形准确选取基高比参数能够提升定位精度。文章研究成果为指导设计提升卫星测绘能力的方案提供参考依据。

RGB模式快速图像比色法测定高锰酸钾

基于高锰酸钾光吸收曲线特性,构建了高锰酸钾溶液浓度与颜色RGB值之间的数学模型,建立了快速图像比色法测定高锰酸钾含量的分析方法。对快速图像比色法进行优化:在室内光源条件下,固定比色皿位置和拍摄距离,优化读取RGB值的像素位置及像素大小[固定像素点坐标从(665,376)至(694,390)的矩形区域、固定像素大小为(30×15)],扣除模型参数的背景值,获得了最佳条件下快速图像比色法测定高锰酸钾浓度的分析方法。在最佳条件下快速图像比色法的拟合曲线线性关系较好(r=0.9997)。该方法成功应用于高锰酸钾实际样品的测定,测定结果及误差范围与分光光度法保持一致。在不具备分光光度计仪器情况下,不需要配备专业拍摄装置,就可以实现对高锰酸钾这类有色溶液的准确快速测定。在智能手机普及的情况下,快速图像比色法的应用范围较为广泛。

基于色彩信息校准权重的融合代价算法

由于融合函数中的Census变换部分的代价计算取决于中心像素点的灰度值、易受光照和噪声影响,绝对误差和SAD代价函数不能利用图像色彩信息导致精度低等缺点,提出一种使用图像色彩信息来校正像素点代价值的Census变换和有权重的SAD代价函数相结合的局部立体匹配算法。在代价计算部分,使用改进SAD和Census变换的代价值结合,形成该像素点的融合代价值;在代价聚合部分,使用十字交叉域聚合的方法,得出结果后再进行迭代,可以得到精度更高的视差图。该算法通过引入邻域像素点的色彩信息,计算出权重以及色彩校正参数,然后对中心像素点的融合代价值进行优化,从而降低SAD-Census算法的误匹配率。实验结果表明,使用了色彩校正参数的改良SAD-Census算法,有效降低了图像的误匹配率,适用于色彩信息重复度不高的图像。

基于卫星立体影像的DSM数据自动提取试验

先简单介绍了DSM自动提取所用到的局部匹配算法、全局匹配算法和半全局匹配算法;其次进行了基于Inpho软件半全局匹配算法的卫星立体影像的DSM自动提取试验,探讨了从传感器类型和卫星影像分辨率2个方面对卫星立体影像DSM自动提取效果的影响,试验表明,更高分辨率的卫星立体影像、稳定且更高适配度的卫星传感器的卫星立体影像对DSM自动提取能取得更好的效果。

HSI色系下的国画颜色再现技术研究

为实现国画作品的数字化重建,提出一套HSI色系下的国画颜色再现方案。基于国画材质反射特性对颜色信息特性加以分析,发现在实际操作中可以采用亮度匹配的方式实现国画作品的数字化再现,最后针对该颜色再现方法进行对比实验。经实验研究发现,所提出的国画颜色再现技术能够实现较高精度的色彩重建,重建效果十分接近拍摄效果。