基于SIFT相似性度量的图像缩放

针对传统图像缩放算法存在的图像内容破坏与失真等问题,提出一种结合接缝雕刻和标准缩放的图像缩放算法。利用接缝雕刻算法对原始图像进行尺寸调整,分别从原始图像和缩放图像上提取尺度不变特征变换(SIFT)特征,并对2幅图像的SIFT特征进行匹配计算,得到两者的相似性距离。当原始图像与缩放图像之间的相似性距离达到某阈值时,即在图像重要目标或内容将要被破坏之前,停止使用接缝雕刻算法,采用标准缩放算法进行整体尺寸的调整操作。实验结果表明,该算法不仅可以有效避免图像内容的破坏与失真,而且可以较好地保护图像局部结构和全局视觉效果。     

Interactive image segmentation by maximal similarity based region merging

Efficient and effective image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. This paper presents a new region merging based interactive image segmentation method. The users only need to roughly indicate the location and region of the object and background by using strokes, which are called markers. A novel maximal-similarity based region merging mechanism is proposed to guide the merging process with the help of markers. A region R is merged with its adjacent region Q if Q has the highest similarity with Q among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by labeling all the non-marker regions as either background or object. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. Extensive experiments are performed and the results show that the proposed scheme can reliably extract the object contour from the complex background.     

基于质量评价的无人机航拍图像拼接方法

针对大视差无人机航拍图像拼接中出现的错位、伪影问题,提出一种基于质量评价寻找最佳拼接缝的方法QEB-U。首先通过常规拼接缝估计得到初始拼接缝,然后根据无人机航拍图像的特点,综合考虑结构相似性、色差、纹理复杂度,提出一种新的质量评价函数对拼接缝上的每个像素进行评价,进而根据评价结果更新差异代价,之后再重新估计拼接缝。估计和评价过程重复执行,直到拼接缝趋于稳定则停止迭代,最后通过梯度融合生成最终结果。实验结果表明,所提方法可以避免大视差无人机航拍图像拼接中出现错位、伪影,优于目前几种无人机航拍图像拼接方法,且得到的拼接缝优先穿过道路、林地等区域,更加符合人类视觉感知,在常见的图像清晰度评价指标上表现良好。     

Automatic building height extraction by volumetric shadow analysis of monoscopic imagery

This article presents a new approach to automatic extraction of building heights from monoscopic urban scenes. A volumetric shadow analysis (VSA) method was proposed previously for extraction of 3D building information (height, shape, and footprint location) and for handling occluded building footprints or shadows. It determined building heights by adjusting building height manually until the projected shadows generated for an assumed height and actual shadows in the image matched. In this article, we propose an intelligent scheme based on the VSA for automatic building height extraction. We achieve this by checking the location change of projected shadow lines with respect to the actual shadow regions while building heights are increased incrementally. In this article, the performance of the proposed automatic height extraction was compared to that of manual extraction. The method was first applied to IKONOS, KOMPSAT-2, QuickBird, and Worldview-1 images with manually extracted building roofs. The root mean square error (RMSE) of building heights was under 3 m by automatic height extraction and 2 m by manual extraction. The RMSE of building footprint location was close to twice that of image ground sample distance (GSD) by automatic height extraction and under twice that of image GSD by manual extraction. These results support the capability of the proposed method in automatic height extraction from a single image efficiently and accurately, and in handling occluded building footprints and shadows. Second, the method was combined with an existing roof extraction method and tested for automated building roof extraction. The results showed that the proposed method can also provide a powerful cue for automatic building roof extraction from a single image.     

地球物理位场图象特征信息自动提取

提出了一种自动提取地球物理位场水平一阶导数图象中局部极大（极小）值线的有效方法。该方法的基本原理是，首先用方向剖面法识别出地球物理位场水平一阶导数图象中的局部极大（极小）值点；然后，用旋转变换方法搜索出各个方向展布的局部极大（极小）值线的端点；最后，根据极值线共线和钝角相交的合并规则将极值线合并成折线，并删除过短的极值线，从面时球物理位场水平一阶导数局部极值线的矢量图。用山东半岛航磁图做试验，自动提取结果与目视解译结果基本吻合，从而证明了方法的有效性。     

WALRUS: a similarity retrieval algorithm for image databases

Approaches for content-based image querying typically extract a single signature from each image based on color, texture, or shape features. The images returned as the query result are then the ones whose signatures are closest to the signature of the query image. While efficient for simple images, such methods do not work well for complex scenes since they fail to retrieve images that match the query only partially, that is, only certain regions of the image match. This inefficiency leads to the discarding of images that may be semantically very similar to the query image since they may contain the same objects. The problem becomes even more apparent when we consider scaled or translated versions of the similar objects. We propose WALRUS (wavelet-based retrieval of user-specified scenes), a novel similarity retrieval algorithm that is robust to scaling and translation of objects within an image. WALRUS employs a novel similarity model in which each image is first decomposed into its regions and the similarity measure between a pair of images is then defined to be the fraction of the area of the two images covered by matching regions from the images. In order to extract regions for an image, WALRUS considers sliding windows of varying sizes and then clusters them based on the proximity of their signatures. An efficient dynamic programming algorithm is used to compute wavelet-based signatures for the sliding windows. Experimental results on real-life data sets corroborate the effectiveness of WALRUS'S similarity model.     

融合事件相机的视觉场景识别

目的 传统视觉场景识别（visual place recognition,VPR）算法的性能依赖光学图像的成像质量,因此高速和高动态范围场景导致的图像质量下降会进一步影响视觉场景识别算法的性能。针对此问题,提出一种融合事件相机的视觉场景识别算法,利用事件相机的低延时和高动态范围的特性,提升视觉场景识别算法在高速和高动态范围等极端场景下的识别性能。方法 本文提出的方法首先使用图像特征提取模块提取质量良好的参考图像的特征,然后使用多模态特征融合模块提取查询图像及其曝光区间事件信息的多模态融合特征,最后通过特征匹配查找与查询图像最相似的参考图像。结果 在MVSEC（multi-vehicle stereo event camera dataset）和RobotCar两个数据集上的实验表明,本文方法对比现有视觉场景识别算法在高速和高动态范围场景下具有明显优势。在高速高动态范围场景下,本文方法在MVSEC数据集上相较对比算法最优值在召回率与精度上分别提升5.39%和8.55%,在Robot‐Car数据集上相较对比算法最优值在召回率与精度上分别提升3.36%与4.41%。结论 本文提出了融合事件相机的视觉场景识别算法,利用了事件相机在高速和高动态范围场景的成像优势,有效提升了视觉场景识别算法在高速和高动态范围场景下的场景识别性能。     

图像主特征直线的检测算法

直线特征的检测是计算机视觉与图像处理的基本任务之一。主特征直线是图像中最显著而且被人们最为关心的一些直线。本文提出了从图像中自动检测主特征直线的算法。首先利用图像分割算法分析得到图像的特征边缘,然后用短线段逼近这些边缘像素,接着用动态聚类算法对这些线段进行聚类,并用直线拟合聚类中的线段得到所有可能存在的直线,最后根据直线有效性度量找到其中有效性最高的直线就是主特征直线。最后本文给出了主特征直线在图像自动修复和自动美学构图中的应用实例。     

Color image retrieval using multispectral random field texture model and color content features

This paper describes a color-texture-based image retrieval system for query of an image database to find similar images to a target image. The color-texture information is obtained via modeling with the multispectral simultaneous autoregressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The retrieval process involves segmenting the image into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The color-texture content, location, area and shape of the segmented regions are used to develop similarity measures describing the closeness of a query image to database images. These attributes are derived from the maximum fitting square and best fitting ellipse to each of the segmented regions. The proposed similarity measure combines all these attributes to rank the closeness of the images. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectively.     

结合最佳缝合线和多分辨率融合的图像拼接

目的 针对图像拼接过程中,缝合线通过运动物体或配准不准确区域等情况导致融合图像出现鬼影、重影的问题,提出了一种基于差异图像加权的改进最佳缝合线算法,采用基于多分辨率和加权平均的分区图像融合算法解决了拼接线问题。方法 首先将两幅图像的重叠区域划分为缝合线区域和过渡区域;在缝合线区域内,使用差异图像加权的最佳缝合线搜索准则构建准则值图像,基于动态规划思想来搜索得到最佳缝合线;基于缝合线生成掩码图像,并对重叠区域图像进行扩展,采用多分辨率融合算法实现了非严格重叠区域的融合;在过渡区域采用加权平均算法来消除拼接线。结果 采用含有大量运动物体的图像序列对算法进行测试,实验结果表明,基于差分图像加权的最佳缝合线有效避开了大部分运动物体,当缝合线难以绕开运动物体时,能够尽量少地穿过运动物体;通过多分辨率和加权平均融合算法消除了拼缝等问题。结论 提出的最佳缝合线算法能够有效地避免缝合线通过运动物体、配准不准确的区域,将多分辨率图像融合算法应用于非严格重叠图像融合,能够合成高质量的全景图像。     

一种基于区域分级合并的彩色图像分割方法

在区域合并过程中,手工设置颜色相似性和边界距离的权重极大地影响了分割的精度和自动化.针对这一问题,提出了一种新的基于区域分级合并的彩色图像分割算法.该方法能够根据邻接区域的边界特点设置权重因子,从而自适应地融合区域的颜色相似性和边界距离.使用均值漂移算法对图像进行初始分割,将原图像分割为具有较好边界的同质区域;通过计算区域相似度对区域进行分级合并.多幅彩色图像的分割实验结果证明,所提算法优于传统的基于区域合并的方法.     

高质量快速全景场景再现技术

目的 色彩纠正和图像融合是生成高质量全景场景图像的关键技术。色彩纠正中参考图像的选择以及图像融合算法,决定着所生成全景图像的质量和速度。现有方法在确定一幅图像是否适合作为参考图像时,需要遍历所有其他图像,计算其作为参考图像进行色彩纠正的效果,复杂度高,速度慢;在图像融合时存在融合质量与融合速度之间的矛盾。因此,如何快速生成高质量的全景图像就成为全景场景再现的主要诉求。为此本文提出优化的参考图像自动选择的色彩纠正方法和基于重叠区域划分的分区融合方法。方法 针对参考图像选择算法复杂度高的问题,根据图像质量与稳定性通常呈反比关系的事实,采用贪婪策略,选择质量最差的图像在色彩纠正前后的相似度,作为是否选择当前图像作为参考图像的评价指标,在保证参考图像满足色彩纠正需求的前提下,大幅降低计算复杂度。针对融合质量与融合速度之间的矛盾,提出分区融合：将重叠区域划分为接缝区域和非接缝区域,利用泊松融合的接缝不可见性和线性融合实现速度快的特性分别对接缝区域和非接缝区域进行融合,既保证融合的质量,又加快融合速度。在此基础上,加入简单点光源,解决上述过程产生的光线一致性问题,进一步提高图像质量。结果 采用主观和客观相结合的方法对结果进行评估。主观方面,本文算法生成的全景图像色彩基本实现平滑过渡且图像原始信息保留完整。客观方面,色彩纠正前后图像的结构相似度（SSIM）控制在0.850.99之间,时间复杂度由原来的O（n²）降低到O（n）;分区融合后图像信息熵接近于泊松融合,但时间消耗降低72%。采用基于PC端的问卷调查法和OG-IQA算法将本文算法与PTGui、OpenCV、Xiong方法生成的全景图质量进行对比,在大多数情况下本文算法均优于上述算法。结论 实验表明,本文算法适用于多种场景,在保证目视效果良好的前提下,时间消耗降低,可广泛应用于医学、数字旅游、遥感等领域。     

Normalized Kemeny and Snell distance: a novel metric for quantitative evaluation of rank-order similarity of images

There are needs for evaluating rank order-based similarity between images. Region importance maps from image understanding algorithms or human observer studies are ordered rankings of the pixel locations. We address three problems with Kemeny and Snell's distance (d/sub KS/), an existing measure from ordinal ranking theory, when applied to images: its high-computational cost, its bias in favor of images with sparse histograms, and its image-size dependent range of values. We present a novel computationally efficient algorithm for computing d/sub KS/ between two images and we derive a normalized form d/sub KS/ with no bias whose range is independent of image size. For evaluating similarity between images that can be considered as ordered rankings of pixels, d/sub KS/ is subjectively superior to cross correlation.     

一种基于页眉线的扭曲文档图像快速校正方法

在对文档图像进行光学字符识别时,由于书籍扭曲的存在,识别率会降低。对于 含有页眉页脚线的扭曲文档图像,提出一种快速校正方法。首先分别检测并定位图像中的页眉 线,保存页眉线的坐标信息。根据等比算法计算页眉线上各点在校正时所需向上或向下移动的 距离,然后以此距离为参数扫描图像,计算页眉页脚线之间的各个目标像素校正所需移动的距 离,同时进行像素点的移动重构图像,最终得到校正的图像。实验结果表明,该方法校正效果明显, 对于包含页眉页脚线的扭曲文档图像有较好的校正效果,校正后OCR 识别率大幅度提高。     

基于二维主成分分析的交通标志牌识别

提出了将二维主成分分析方法应用于交通标志牌识别的特征提取,并在已建立的两个标志牌的数据库上利用最近部分类器与欧氏距离度量进行了相应的实验。一个数据库是将标志牌图像二值化后经过一系列的仿真变换得到的,另外一个数据库是选取不同位置场景经过实地拍摄得到的标志牌图像。本方法对两个图像库的识别都得到了良好的效果。     

综合颜色和轮廓曲线特征的图像检索方法研究

传统的基于内容图像检索（CBIR）及跟踪算法主要利用图像的颜色、纹理等特征进行相似性比较,但大量的实验和应用也表明利用颜色和纹理进行图像相似性比较在空间结构和对象形状上难以精确控制,致使图像检索经常出现一些不可预料的结果。为了提高图像在形状、颜色及纹理上的检索精度,提出了一种综合颜色和图像轮廓曲线特征的检索方法。该方法分割图像并提取图像中感兴趣对象的轮廓,对提取的轮廓进行仿射变换及最小值化处理,经处理后的轮廓带有边缘的完整信息,具有几何不变性;利用聚类的颜色信息,提取主聚类的直方图,所提取的直方图不仅包含了主聚类的颜色信息也包含了该聚类的空间位置信息。利用检索对象与被检索对象的颜色距离直方图及轮廓曲线距离偏差的加权平均度量检索及被检索对象的相似性。实验结果表明,针对基于感兴趣对象的图像检索问题,给出了一种具有高度检索精度的算法。     

Automatic registration of laser reflectance and colour intensity images for 3D reconstruction

The objective of the work presented in this paper is to generate complete, high-resolution models of real world scenes from passive intensity images and active range sensors. In previous work, an automatic method has been developed in order to compute 3D models of real world scenes from laser range data. The aim of this project is to improve these existing models by fusing range and intensity data. The paper presents different techniques in order to find correspondences between the different sets of data. Based on these control points, a robust camera calibration is computed with a minimal user intervention in order to avoid the fastidious point and click phase that is still necessary in many systems. The intensity images are then re-projected into the laser coordinate frame to produce an image that combines the laser reflectance and the available video intensity images into a colour texture map.