Global Detection of Salient Convex Boundaries

As an important geometric property of many structures or structural components, convexity plays an important role in computer vision and image understanding. In this paper, we describe a general approach that can force various edge-grouping algorithms to detect only convex structures from a set of boundary fragments. The basic idea is to remove some fragments and fragment connections so that, on the remaining ones, a prototype edge-grouping algorithm that detects closed boundaries without the convexity constraint can only produce convex closed boundaries. We show that this approach takes polynomial time and preserves the grouping optimality by not excluding any valid convex boundary from the search space. Choosing the recently developed ratio-contour algorithm as the prototype grouping algorithm, we develop a new convex-grouping algorithm, which can detect convex salient boundaries with good continuity and proximity in a globally optimal fashion. To facilitate the application of this convex-grouping algorithm, we develop a new fragment-connection method based on four-point Bezier curves. We demonstrate the performance of this convex-grouping algorithm by conducting experiments on both synthetic and real images. In addition, we provide a comparison with some prior edge-grouping algorithms. Finally, we show that the proposed convex-grouping algorithm can be further extended to detect convex open boundaries, derive region-based image hierarchies, and incorporate some simple human-computer interactions.     

A Unified Approach to Detecting Spatial Outliers

Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. In this paper, we first provide a general definition of S-outliers for spatial outliers. This definition subsumes the traditional definitions of spatial outliers. Second, we characterize the computation structure of spatial outlier detection methods and present scalable algorithms. Third, we provide a cost model of the proposed algorithms. Finally, we experimentally evaluate our algorithms using a Minneapolis-St. Paul (Twin Cities) traffic data set.     

一种JPEG图像边缘检测算法

提出了一种针对JPEG压缩图像的边缘检测算法。该算法首次推导出了DCT域中8×8DCT块到2×2DCT块的直接变换矩阵,并以2×2DCT块为单位进行边缘检测。实验结果表明,与已有的方法相比,该算法简单易行,且提取出的边缘特征具有较高的分辨率。     

空间数据库的数据模型和查询语言

介绍了空间数据库的几种数据模型,分析各种模型的特点,在此基础上讨论了基于空间数据模型的空间数据管理、查询及查询语言。最后概括了将来的研究方向。     

高分辨率遥感图像中机场跑道快速检测方法

在分析机场跑道结构特征的基础上，提出了一种从高分辨率遥感图像中检测机场跑道的方法．具体介绍了嵌入置信度的边缘检测算法和基于线基元的直线提取方法，前者增加了检测结果的可靠性，后者加快了跑道检测速度．最后的实验结果表明本文算法是快速、稳健的．     

基于卡尔曼滤波的SAR图像边缘检测方法

针对传统 Canny 边缘检测算法对合成孔径雷达(SAR)图像的相干斑噪声抑制程度 太高,导致大量边缘的真实信息丢失问题,提出一种新型 Canny 算子边缘检测算法。首先建立 合适的非对称半平面区域(NSHP)图像模型,将空间模型转换成卡尔曼滤波可适用的系统状态方 程;然后用“预测+反馈”的方式对图像去噪;最后通过双阈值算法提取图像的边缘。仿真实验表 明,该方法可以有效地抑制 SAR 图像中的相干斑噪声,同时能较好地保留图像的边缘信息,相 对于传统的 Canny 算法有较好的检测效果。     

SAR图像边缘检测方法综述

边缘检测是SAR图像解译的基本问题之一。固有的乘性噪声特性使得经典的梯度边缘检测方法并不适用于SAR图像,因此研究针对SAR图像的边缘检测方法具有重要的意义。本文对SAR图像边缘检测问题进行了系统的研究,简单描述了SAR图像边缘检测问题,讨论了现有的SAR图像边缘检测方法,总结了SAR图像边缘检测中存在的两个问题——边缘细化和边缘定位,对检测结果的性能评估方法也进行了概括。最后在总结现有研究成果的基础上展望了SAR图像边缘检测的发展方向。     

图像边缘检测的多尺度灰度Gap统计模型

汲取Hastie和Tibshiran i等人提出的“Gap statistic”的思想方法,利用样本灰度数据分布的差别定义多尺度的图像灰度间隙,在提出反分布函数概念的基础上,建立了图像边缘检测的多尺度灰度Gap统计模型。通过分析分布间隙和灰度间隙的一致性,优化了Gap统计模型的边缘检测算法。分析不同尺度下的检测结果,并比较了灰度Gap统计模型与Prew itt和Sobel边缘算子之间的相互关系。通过实例与分析,证明了该模型具有抗噪声、多尺度的特点。     

一种在图象中检测网格直线的新方法

设计了一个扩展Robert算子,该算子能够在有噪声的情形下对图象中各种宽度的线条进行检测。当使用Hough变换确定直线的参数时,首先只用Hough变换检测第一条最为显著的直线,随后去掉该条直线及其附近的点,然后再次对图象进行Hough变换,并重复此过程,直到找到所有直线或者Hough变换后参数平面上的值都小于某个阈值为止。使用此改进后的Hough变换能够准确地检测到图象中构成网格的直线的参数。给出了具体的检测例子。     

Extracting Grain Boundaries and Macroscopic Deformations from Images on Atomic Scale

Nowadays image acquisition in materials science allows the resolution of grains at atomic scale. Grains are material regions with different lattice orientation which are frequently in addition elastically stressed. At the same time, new microscopic simulation tools allow to study the dynamics of such grain structures. Single atoms are resolved experimentally as well as in simulation results on the data microscale, whereas lattice orientation and elastic deformation describe corresponding physical structures mesoscopically. A qualitative study of experimental images and simulation results and the comparison of simulation and experiment requires the robust and reliable extraction of mesoscopic properties from the microscopic image data. Based on a Mumford–Shah type functional, grain boundaries are described as free discontinuity sets at which the orientation parameter for the lattice jumps. The lattice structure itself is encoded in a suitable integrand depending on a local lattice orientation and one global elastic displacement. For each grain a lattice orientation and an elastic displacement function are considered as unknowns implicitly described by the image microstructure. In addition the approach incorporates solid–liquid interfaces. The resulting Mumford–Shah functional is approximated with a level set active contour model following the approach by Chan and Vese. The implementation is based on a finite element discretization in space and a step size controlled, regularized gradient descent algorithm. This work was supported by the DFG priority program 1114.     

噪声数据集上的边界点检测算法

为了有效检测聚类的边界点,提出了结合对象的密度及其Eps-邻域中数据的分布特点进行的边界点检测技术和边界点检测算法 ——BOUND。实验结果表明,BOUND能在含有不同形状、大小簇的噪声数据集上有效地检测出聚类的边界点,并且执行效率高。     

Detecting and locating landmine fields from vehicle- and air-borne measured IR Images

Air- and vehicle-borne sensor-based technique is a potentially attractive approach for fast detecting landmines and locating landmine fields towards humanitarian demining. For images measured from airborne and vehicle-borne cameras, landmines may be indicated by direct or indirect signs, e.g., spatial difference from their surroundings due to digging or, due to thermal and material signatures. The background in images usually consists of various types of noise and clutter, e.g., thermal noise, sand, gravel road and vegetation, thus making the detection even more difficult. This paper is focused on the following aspects: (1) Finding a robust detector that is suitable for detecting/locating landmine candidates and man-made landmarks by using infrared images measured from vehicle- or air-borne sensors; (2) Interpreting the detector using the 2D isotropic bandpass filter, matched filter, detection theory and thermodynamic-based landmine models; (3) Extending the detector to a multiscale version where landmine detectability is enhanced by automatically selecting a proper scale and localization is improved by inter-scale position tracing. We propose a special type of isotropic feature detector that exploits the characteristic difference between landmines and their surroundings in the spatial-frequency domain under the multiscale framework. Experiments were performed on several infrared images measured from vehicle-borne sensors as well as airborne sensors on a helicopter over the test bed scenarios. The performance of the detector was also evaluated in terms of detectability, localization, and automatic scale selection of the detector. These results and evaluations have shown the effectiveness of the method and its potential in landmine field detection.     

Detecting Irregularities in Images and in Video

We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. The term “irregular” depends on the context in which the “regular” or “valid” are defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. We pose the problem of determining the validity of visual data as a process of constructing a puzzle: We try to compose a new observed image region or a new video segment (“the query”) using chunks of data (“pieces of puzzle”) extracted from previous visual examples (“the database”). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in images and video, for detecting suspicious behaviors and for automatic visual inspection for quality assurance. Patent Pending     

一种二维边沿检测滤波器

文章提出了一种新型二维边沿检测滤波器,利用二维滤波器模板与任意方向边沿的互相关最大值检测边沿,具有良好的噪声特性及较小的系统误差。     

一种新的图像空间特征提取方法

提出一种新的图像空间特征提取方法,采用基于颜色向量角和Euclid距离的CCVAE算法,检测原始图像的彩色边缘,分别对边缘像素、非边缘像素建立边缘直方图描述子和空间颜色直方图,描述颜色分布信息。仿真实验结果表明,该方法具有较高的准确率和召 回率。     

一种自适应的视频帧中字幕检测定位方法

视频帧中的字幕往往包含当前视频的高层语意内容,对视频内容的自动理解、索引和检索有重要意义。文中提出了一种视频帧中字幕的自适应检测定位方法,与以往根据经验设定阈值的方法相比,该方法简单,对视频帧的复杂变化的适应能力更强,检测定位更快速、准确。大量实验结果显示该方法是有效的。     

虹膜内外边缘的快速定位算法

虹膜定位是虹膜识别过程中的重要环节,定位的速度和精度决定了整个虹膜识别系统的性能。论文提出一种基于Hough变换的虹膜定位算法,根据虹膜图像特点,先对其进行预处理,再用灰度投影法粗定瞳孔圆心;通过对虹膜内外边缘的“先采样后变换”,减少Hough变换的运算量;利用虹膜内外边缘之间的耦合关系,由内至外精确地确定出边缘。对300多幅虹膜样本的处理结果表明,此算法快速、精确、鲁棒。     

基于子波谱的图象空间冗余度研究

从子波谱的性质出发，通过子波变换提取图象的灰度和边缘特征，并采用两级子波分解技术，快速搜索在一定相似性测度和相似性水平下这两种特征的空间冗余度。研究结果可作为衡量景象匹配系统所用参考图可匹配度的一个重要指标     

An efficient visual tracking method for multiple moving targets

An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as color, size, etc., a new modified Nearest Neighbor （NN） algorithm for data association using the target features is designed. A designed Interacting Multiple Model （IMM） filter is utilized to track the maneuvering target motion, i.e. the feature point （called the centroid of the target） motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.     

一种自适应的运动目标模糊边缘检测方法

提出了一种有选择的局部背景快速分割方法;针对模糊边缘难以检测的问题,提出一种将梯度法与灰度阈值法相结合的方法.该方法首先通过大津法确定目标的位置;然后在其邻域内求出关于灰度值分布的直方图的极值点,再通过灰度阈值法得到目标的初步边缘;最后根据梯度信息对边缘进行微调,减少了灰度不均匀等因素造成的影响.实验结果表明,文中的方法可以有效地提高模糊边缘的检测效率.