基于纹理-模糊连接度的遥感影像道路自动提取

提出一种基于纹理-模糊连接度的遥感影像道路信息全自动提取算法。利用Canny算子对影像进行边缘检测,通过道路阈值和分类算法的联合优化,提取出道路种子点。定义了包含纹理能量的模糊连接度,对影像中各像素相对于多种子点的连接度进行遍历计算,寻址最优路径,从而识别出完整的道路。实验结果表明,该道路提取算法相对于模糊连接度算法精度更高,具有较强的通用性。     

融合全局显著性信息的轮廓编组模型

基于感知编组的轮廓提取算法容易受背景上边缘的影响,导致轮廓提取的准确率低,为此提出一种结合感知编组与全局显著性信息的轮廓提取算法.首先在Canny算子框架下增加显著性信息的约束,提取显著边缘,减少了背景上的边缘;然后在Ratio-contour算法的基础上提出了新的目标函数,使得文中算法能够收敛于显著性高的区域,得到的轮廓更准确地标识前景物体.实验结果表明,该算法有效地提高了轮廓提取的准确性,同时大幅减少了轮廓提取的计算时间.     

改进的多尺度图谱和局部谱的目标提取算法

当目标对象与背景的纹理较多或两者纹理较接近时,基于多尺度图谱和局部谱的目标提取算法不能很好地提取目标,主要由于在计算相似度度量时,金字塔多尺度图谱算法特征选取较简单。针对算法不足,提出基于改进的金字塔多尺度图谱和局部谱相结合的目标提取算法,主要通过改进多尺度图谱中干涉轮廓权重构造方法,原算法中是基于拉普拉斯边缘图和梯度图,改进后是基于多尺度边缘概率检测算子和方向分水岭算法产生的边缘强度图。多尺度边缘概率检测算子可以有效地解决纹理较复杂图像分割不佳问题,方向分水岭算法可以有效解决由于目标和背景部分边界信息较接近导致的分割不佳问题。实验结果表明改进算法有效地弥补了原算法的不足,并且具有良好的目标提取效果。     

基于全局运动对比度的轮廓编组元提取算法

在轮廓编组计算模型中,编组元的提取对于轮廓编组结果具有重要的影响。针对复杂场景中目标轮廓易与非目标边缘混淆的问题,提出了一种基于全局运动对比度的编组元提取算法。提出了基于边缘片段的运动相似度度量方法,并通过相似度定义了场景中的全局运动对比度,以此对非目标边缘片段进行抑制,从而提取出更为有效的目标轮廓边缘片段构成编组元集合。在Moseg_dataset数据集上的实验结果证明,提出的全局运动对比度对于非目标边缘片段具有良好的鉴别能力,相比较目前轮廓编组计算模型中基于边缘检测和轮廓检测的编组元提取算法,该算法显著降低了编组元集合的规模,提高了编组元集合的有效性。在相同的轮廓编组算法中,该算法提取的编组元集合能取得更优的编组结果。     

基于视觉感知的双层次阈值边缘连接方法

边缘是图像目标的重要特征,但通常边缘检测得到的边缘存在不连续现象,为此在分析传统边缘连接方法的基础上,利用人类视觉系统对边缘连接的多层次感知机理,提出了一种基于视觉感知的双层次边缘连接方法。该方法首先利用大、小阈值产生相应的大、小阈值图像计算其差值以确定模糊边缘点;然后利用人类视觉系统对边缘连接的感知特性系数判别模糊边缘点中真正的边缘点,并将真正的边缘点添加到大阈值图像,使大部分重要的区域边缘能够形成完整的封闭轮廓。仿真实验结果表明,该方法能够有效地改善边缘检测后的边缘不连续现象,相比一些传统的边缘连接方法,运算速度较快,连接效果较好,能满足边缘检测的轮廓封闭性要求。     

基于角点检测的建筑物轮廓矢量化方法

针对高分辨率航空影像的特点,提出了基于角点检测的建筑物轮廓矢量化方法:首先,对航空影像进行多尺度面积形态学分割,获取建筑物的二值图像;其次,利用边缘跟踪技术,记录边缘点,在边界曲线上利用高斯函数计算其曲率,提取候选角点集,并由自适应支持区域确定的角点角度和一个动态曲率阔值代替固定的阈值筛选出正确角点;最后,以直角作为约...     

基于视觉统计概率模型的目标定位

复杂场景中的目标定位是目标检测和识别的重要过程,为了更好地对复杂场景中的目标进行定位,基于视觉的概率模型,提出了一种目标定位的新方法。区别于一般的区域分割和边缘检测方法,该方法首先通过建立平滑、纹理、阴影和杂乱等4种不同类型区域特性的概率模型,对场景中的前景和背景进行了概率分析;然后结合不同的尺度大小,标记出图像中显著度较高的目标区域;最后经过边缘轮廓的概率建模和连通性分析来提取完整目标区域。实验结果表明,该方法具有较好的鲁棒性和通用性,不仅符合人的视觉注意特性,而且具有一定的抗背景干扰能力。     

基于感知编组的高层建筑物立面提取方法

将感知编组理论引入到遥感影像上高层建筑物立面的提取中,依据感知编组的邻近性、连接性和封闭性等规则,提出了一种基于感知编组的遥感影像上高层建筑物立面的提取算法,该算法包括边缘提取、边缘数据分块、块内感知编组提取建筑物立面、建筑物立面提取结果融合等步骤.实验验证了该方法的有效性.     

一种基于物体的视觉注意计算模型

为解决基于空间的视觉注意计算模型存在的注意目标不完整、容易转移到无意义区域等问题,提出一种结合空间显著性的基于物体的视觉注意计算模型。检测图像的边缘信息,根据空间视觉显著性度量结果,提取显著值高的封闭边缘,得到感知物体的轮廓。根据各感知物体的大小、位置和显著程度计算其注意度。注意焦点按照注意度递减的顺序在各感知物体之间进行转移。在多幅自然图像上进行实验验证,实验结果表明该模型具有和人类视觉特性相符合的注意效果。     

自然图像中目标轮廓上显著边缘检测的计算模型

如何模拟人类视觉感知系统的感知过程,建立一个鲁棒性较好、无监督的自然图像中目标轮廓上显著边缘检测的计算模型是文中要讨论的问题。首先确定自然图像中目标所在的子区域。然后通过分析纹理以及颜色等低级视觉特征得到一组潜在的轮廓边缘,对这些潜在的轮廓边缘进行闭合性分析,建立各条潜在边缘之间闭合关系的图模型。最后通过最短路径找出最优的轮廓上的显著边缘。将该模型用于多幅自然图像,实验效果较好。该模型在生物学上的合理性也得到验证。     

Unsupervised segmentation of unknown objects in complex environments

This paper presents a novel object segmentation approach for highly complex indoor scenes. Our approach starts with a novel algorithm which partitions the scene into distinct regions whose boundaries accurately conform to the physical object boundaries in the scene. Next, we propose a novel perceptual grouping algorithm based on local cues (e.g., 3D proximity, co-planarity, and shape convexity) to merge these regions into object hypotheses. Our extensive experimental evaluations demonstrate that our object segmentation results are superior compared to the state-of-the-art methods.     

基于支持向量机的鲁棒盲水印算法

提出了一种基于支持向量机的鲁棒盲水印算法.该算法首先用多尺度Harris-Laplace检测算子从载体图像中提取出稳定的特征点,然后根据特征自适应确定局部特征区域,在特征区域选择一些点作为嵌入水印的点,结合图像的邻域相关性,根据灰度图像特点,选取特征向量作为SVR训练模型,进而利用SVR进行预测,调节嵌入点的像素值进行水印的嵌入和提取.实验结果表明,用该技术嵌入水印后的图像具有很好的图像感知质量,时常规信号处理乖去同步攻击特别是JPEG压缩具有较强的鲁棒性.     

城乡一体化地籍管理信息系统数据组织研究

将城乡一体化地籍实体对象分为行政境界、所有权权属界、建设用地使用权权属界、图斑、线状地物、点状地类等地籍实体后,研究了实体间的关系,讨论了这些实体与现有城镇、农村地籍调查实体对象的衔接,指出以乡镇地理单元进行数据的分块存储和管理符合自上而下的行政隶属关系,也符合多尺度地理思想的表达.论文最后研究了多尺度地籍空间数据的组织方式.     

Recognising text in real scenes

We present two different approaches to the location and recovery of text in images of real scenes. The techniques we describe are invariant to the scale and 3D orientation of the text, and allow recovery of text in cluttered scenes. The first approach uses page edges and other rectangular boundaries around text to locate a surface containing text, and to recover a fronto-parallel view. This is performed using line detection, perceptual grouping, and comparison of potential text regions using a confidence measure. The second approach uses low-level texture measures with a neural network classifier to locate regions of text in an image. Then we recover a fronto-parallel view of each located paragraph of text by separating the individual lines of text and determining the vanishing points of the text plane. We illustrate our results using a number of images. Received May 20, 2001 / Accepted June 19, 2001     

Evaluating structural and topological consistency of complex regions with broad boundaries in multi-resolution spatial databases

Multi-resolution or multi-scale spatial databases store and manage multiple representations of spatial objects in the same area, so consistency among multiple representations of the same objects should be evaluated and maintained. Although many approaches have been proposed to check inconsistencies in multi-resolution databases, there is still a lack of effective approaches working for complex objects, especially for regions with broad boundaries which is a general model for representing various types of uncertainties. This paper presents approaches for evaluating structural and topological consistency among multiple representations of complex regions with broad boundaries (CBBRs) based on map generalization operators: merging, dropping, and hybrid of these two. For evaluation of structural consistency, all possible multiple representations of a CBBR are generated automatically and organized into a structured neighborhood graph, and then correspondences and equivalences among the multiple representations are defined to determine whether two representations at different levels of detail are structurally consistent. For evaluation of topological consistency, the topological relations between all pairs of regions in two CBBRs are considered, and their variation with change of spatial scale is analyzed. Since the approaches in this paper are built on a hiearchical representation of CBBRs with arbitrarily complex structure, they will also work well for evaluating consistency among multiple representations of complex objects.     

几何活动轮廓模型的多尺度扩散分割算法

提出了一种对几何活动轮廓模型中的停止速度场进行多尺度扩散的算法,它通过引进2个控制参数来定义停止速度场的目标边界、同质区域和过渡区域;对于不同复杂性的图像,采取不同的控制参数对其停止速度场进行多尺度扩散;并将多尺度扩散后的停止速度场应用于几何活动轮廓模型进行图像分割.实验结果表明:对1幅合成图像和2幅自然图像,该算法大大地减少了分割时间,在一定程度上也减少了边界泄漏.     

Multi-scale object-based measurement of arid plant community structure

The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values.     

Multi-scale weighted gradient-based fusion for multi-focus images

Anisotropic blur and mis-registration frequently happen in multi-focus images due to object or camera motion. These factors severely degrade the fusion quality of multi-focus images. In this paper, we present a novel multi-scale weighted gradient-based fusion method to solve this problem. This method is based on a multi-scale structure-based focus measure that reflects the sharpness of edge and corner structures at multiple scales. This focus measure is derived based on an image structure saliency and introduced to determine the gradient weights in the proposed gradient-based fusion method for multi-focus images with a novel multi-scale approach. In particular, we focus on a two-scale scheme, i.e., a large scale and a small scale, to effectively solve the fusion problems raised by anisotropic blur and mis-registration. The large-scale structure-based focus measure is used first to attenuate the impacts of anisotropic blur and mis-registration on the focused region detection, and then the gradient weights near the boundaries of the focused regions are carefully determined by applying the small-scale focus measure. Experimental results clearly demonstrate that the proposed method outperforms the conventional fusion methods in the presence of anisotropic blur and mis-registration.     

Structure compliant local warping of images with applications to watermarking attack

Localized geometric warping of images is known to be one of the most effective attacks against image watermarking systems. However, the existing local geometrical attacks, when applied to images with regular structures, cause perceptible distortion because they are not adaptive to the content of the images. In this work, we present a multi-scale directional smoothing framework in which local displacement vectors are smoothed by locally adaptive directional kernels. Both the displacement vectors for large structures and those for fine structures are constrained by using a multi-scale pyramid. Subjective tests and objective metrics show that our proposed approach can effectively enhance the perceptual quality of the image after geometric attacks. The test of the attacking effects on two typical watermarking systems demonstrates that our approach does not degrade the attacking effects for Markov random field generated displacement field.     

First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D

Most computer vision applications require the reliable detection of boundaries. In the presence of outliers, missing data, orientation discontinuities, and occlusion, this problem is particularly challenging. We propose to address it by complementing the tensor voting framework, which was limited to second order properties, with first order representation and voting. First order voting fields and a mechanism to vote for 3D surface and volume boundaries and curve endpoints in 3D are defined. Boundary inference is also useful for a second difficult problem in grouping, namely, automatic scale selection. We propose an algorithm that automatically infers the smallest scale that can preserve the finest details. Our algorithm then proceeds with progressively larger scale to ensure continuity where it has not been achieved. Therefore, the proposed approach does not oversmooth features or delay the handling of boundaries and discontinuities until model misfit occurs. The interaction of smooth features, boundaries, and outliers is accommodated by the unified representation, making possible the perceptual organization of data in curves, surfaces, volumes, and their boundaries simultaneously. We present results on a variety of data sets to show the efficacy of the improved formalism.