一种提取图象细节边缘的新方法

边缘是图象的基本特征，边缘信息是进行图象分析和识别的重要属性，但由于常用的边缘提取方法在提取边缘的同时，容易丢失图象的细节边缘信息，为此提出了一种基于灰度形态学和图象分解技术相结合的图象细节边缘提取方法，该方法首先运用灰度形态学方法检测出包含图象细节的边缘图象并去除部分背景和噪声，然后进行区域分解，再通过对不同的区域选取不同的阈值来保证边缘提取的完整性．仿真结果表明，与传统方法相比，该方法能有效地提取一般图象的细节边缘，甚至能提取被噪声污染图象的边缘．     

一种新的彩色图象文字提取算法

文字信息在描述图象内容时起着重要的作用,因此文字提取及识别是基于内容视频检索的关键技术。提出了一个从彩色图象背景中提取文字的快速而有效的算法。由于文本字符串的对比度较高,首先用一个改进的sobel算子将彩色图象变换为二值的边缘图象,再对该边缘图象进行涂抹处理,然后基于候选文本区的特征从不同复杂度的彩色图象中提取文本信息,最后将提取出的文本输入到文字识别(OCR)引擎,识别结果证明了此方法的有效性。     

图象模糊涟缘检测的改进算法

图象在检测技术是图象处理中最重要的内容之一,且已在图象分析和识别领域中得到广泛的应用。针对图象边缘由模糊性引起的不确定性问题,提出了一种图象模糊边缘检测的改进算法,该算法是道德民确定一个阈值参数,然后根据此阈值参数来定义一个新的隶属函数,从而钭图象转化为等效的图象模糊特征平面,通过在模糊特征平面上进行增强运算,将其转换为空域图象,最后再进行边缘提取,同时还对具有多峰直方图分布图象的模糊边缘检测方法进行推广,仿真结果表明,该算法是有效的。     

显微图象中目标对象的边缘提取研究

研究了显微图象中目标对象边缘的提取，提出了一种新方法．该方法分三步进行，首先识别目标对象，并与背景区分开，然后滤除误识别象素和脏点，最后提取目标对象的边缘。使用最优颜色通道识别显微图象中的目标对象，动态的调节识别阈值，减少了误识别象素的数量，对残存的误识别象素和脏点采用面积滤波的方式去除，进一步提高边缘提取的效果。试验结果表明，采用本文的方法提取目标对象的边缘优于已有的熵算符法提取的结果。     

基于边缘的图象配准改进算法

现有的图象配准方法对旋转处理比较困难,且处理效率不高。本文提出一种基于边缘的图象配准改进算法,该算法首先用小波的快速方法提取边缘特征点,通过线拟合模型消除噪声;然后得出特征点的边缘方向,定义“角度直方图”来估计两幅待配准图象间的旋转角,应用线性加权方法消除奇异点,最后得到图象间的精确变换关系。     

用细胞神经网络提取二值与灰度图象边缘

边缘是图象的重要特征,采用细胞神经网络提取图象边缘时,网络参数的选择是一个重要问题。为了能够有效地提取图象边缘,基于高通滤波模板,选择了细胞神经网络的一组简单易行的参数,首先将其用于检测二值图象边缘,再在此基础上,通过综合灰度值各位面边缘检测的结果提取出灰度图象的边缘。与传统边缘提取方法Sobel和Log方法的比较可见,该方法是有效的,并且由于细胞神经网络具有高速并行运算、便于硬件实现等特点,因此使其在图象实时处理中具有更大的潜力。     

自动图象边缘检测方法的研究

本文提出了一种新的自动图象边缘检测方法,该方法使用新的隶属函数将图象转化为等效的图象模糊特征平面,在此基础上进行模糊增强,然后再转换为空域图象,顾后进行边缘提取,模糊增强提高了低灰度区域和高硬功率和高灰度区域之间的对比度,从而使提取的边缘效果更好,最后本文对具有多峰直方图分布图象的模糊边缘检测问题进行了
了推广。     

基于能量累积与顺序形态滤波的经外小目标检测

针对红外序列图象中弱小目标的检测问题，提出了基于能量累积与顺序形态滤波的小目标检测方法，该方法通过设置一定大小的滑动窗口，对窗口内的图象序列进行能量累积，以达到去除图象中的随机噪声和提高目标的信噪比的目的，其目标检测采用由粗到精3个步骤，即首先利用顺序形态滤波抑制背景，并通过提取目标广义边缘来实现目标的粗定位，然后对可能存在目标的区域进行分割，通过提取目标几何特征来完成精确定位；最后利用序列图象中目标运动的连续性和轨迹的一致性来筛选出真正的目标，实验结果表明，该方法能有效地抑制背景和能提取目标广义边缘，并能通过自适应地选择分割门限来完成红外小目标的定位和检测。     

离散分布的二值图象快速跟踪填充算法

本文提出了一个快速、新颖的图象填充算法.该法将二值图象边缘用一种特定的链码来表示,仅通过对链码节的移入移出分析运算,就能够自动完成离散分布的目标轮廓的搜索与填充,而不需要象通常的填充算法那样进行边缘检测.     

一种有效的文本图象识别方法

针对文本图象的识别,此文提出了一种新的方法.该方法中分别利用了图象的颜色及纹理信息,并将其结果进行融合得到了很好的效果.首先根据颜色直方图的特征,利用其分布特征及信息熵,对图象做识别;其次以图象的灰度共生矩阵用来表达纹理,并提取该矩阵的相关特征量用来对图象做识别.该方法充分考虑了文本图象的颜色及空间分布特性,提取了有效的表达参数,实验结果与性能比较表明,该方法是有效的.     

基于边界对同时检测的纤维识别

纸浆纤维的形态参数对于成纸性能有重要的影响,但纤维存在卷曲、扭结等现象,因而常规的检测与识别方法很难较好地完成复杂纤维图像的检测。论文利用边界对同时检测的最优边界检测方法:构建三维图,并结合纤维的自身特点(如形状、方向等知识)定义费用函数,通过动态规划法在三维图中搜索最优路径,完成最优边界对的检测。该方法能实现对噪声高、对比度低、有分叉或重叠结构的图像的检测。最后通过对低质量纤维图的检测,验证了该方法的可靠性和鲁棒性。     

基于无线分离架构的横机控制器的研究

横机是羊毛衫的主要生产设备,适合棉、毛、麻、丝、羊绒及各种化纤、混纺纱线的编织。基于无线分离架构的设计,正是综合考虑多种改良因素,包括横机控制器运算处理能力、控制性能的增强以及机头大量数据线在伴随机头高速往复运动时容易损坏而导致系统崩溃问题的解决等,从体系结构方面做一个全面性的开发改进。     

基于图像分析的纤维直径和曲率的测量方法

纤维直径(细度)和曲率的测量在羊毛产业和纺织工业中具有举足轻重的作用。提出了一种基于图像分析的全自动测量方法,该方法首先对采集的图像进行图像增强,利用Canny边缘检测提取纤维边缘,根据纤维图像的特点用聚类的方法对增强后图像进行二值化,并对二值化图像进行一系列后处理,然后提取出纤维的骨架,进而通过距离变换和特定的搜索算法计算骨架上每个点对应处的纤维直径和曲率。实验结果表明,该方法与基于直线拟合的方法相比,不仅计算速度快,而且绕开了很多可能产生误差的环节,测量准确率较高。     

适用于机场跑道识别的改进Hough变换

机场跑道的卫星图片经过处理后表现出来的骨架特征为边缘直线,在边缘图像中检测直线通常使用的方法是Hough变换（HT）.由于（HT）是一种穷举式的搜索,在处理复杂图像时存在大量无效计算,实时性较差.针对机场跑道识别的实时性要求,提出一种改进的用于在二值图像中检测直线的快速Hough变换算法,此算法克服了标准Hough变换以图像边界点为扫描边界的缺点,并且能及时中断无谓的扫描,较好地解决了无效累积问题,实验证明,与标准Hough变换相比,它不仅具备Hough变换原有的高可靠性和抗干扰能力,而且具备Hough变换所不具备的高效性和低存储,克服了标准Hough变换的高计算代价和耗存储的缺点.     

Supervised training database for building recognition by using cross ratio invariance and SVD-based method

This paper describes an approach to training a database of building images under the supervision of a user. Then it will be applied to recognize buildings in an urban scene. Given a set of training images, we first detect the building facets and calculate their properties such as area, wall color histogram and a list of local features. All facets of each building surface are used to construct a common model whose initial parameters are selected randomly from one of these facets. The common model is then updated step-by-step by spatial relationship of remaining facets and SVD-based (singular value decomposition) approximative vector. To verify the correspondence of image pairs, we proposed a new technique called cross ratio-based method which is more suitable for building surfaces than several previous approaches. Finally, the trained database is used to recognize a set of test images. The proposed method decreases the size of the database approximately 0.148 times, while automatically rejecting randomly repeated features from the scene and natural noise of local features. Furthermore, we show that the problem of multiple buildings was solved by separately analyzing each surface of a building.     

Recovering intrinsic images from a single image

Interpreting real-world images requires the ability distinguish the different characteristics of the scene that lead to its final appearance. Two of the most important of these characteristics are the shading and reflectance of each point in the scene. We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, given the lighting direction, each image derivative is classified as being caused by shading or a change in the surface's reflectance. The classifiers gather local evidence about the surface's form and color, which is then propagated using the Generalized Belief Propagation algorithm. The propagation step disambiguates areas of the image where the correct classification is not clear from local evidence. We use real-world images to demonstrate results and show how each component of the system affects the results.     

Hypercatadioptric line images for 3D orientation and image rectification

In central catadioptric systems 3D lines are projected into conics. In this paper we present a new approach to extract conics in the raw catadioptric image, which correspond to projected straight lines in the scene. Using the internal calibration and two image points we are able to compute analytically these conics which we name hypercatadioptric line images. We obtain the error propagation from the image points to the 3D line projection in function of the calibration parameters. We also perform an exhaustive analysis on the elements that can affect the conic extraction accuracy. Besides that, we exploit the presence of parallel lines in man-made environments to compute the dominant vanishing points (VPs) in the omnidirectional image. In order to obtain the intersection of two of these conics we analyze the self-polar triangle common to this pair. With the information contained in the vanishing points we are able to obtain the 3D orientation of the catadioptric system. This method can be used either in a vertical stabilization system required by autonomous navigation or to rectify images required in applications where the vertical orientation of the catadioptric system is assumed. We use synthetic and real images to test the proposed method. We evaluate the 3D orientation accuracy with a ground truth given by a goniometer and with an inertial measurement unit (IMU). We also test our approach performing vertical and full rectifications in sequences of real images.     

光电混合处理系统识别高相似度工业零件图象的研究初探

为了克服对高相似度图象的误判，从形态学角度提出一种基于联合变换相关器的图象相似度定义，并将改善高相似度图象识别的方法划分为非原理性改进和原理性改进两大类，其中，为进行非原理性改进，提出将结构光模式应用于联合图象编码；为进行原理性改进，对联合图象采用基于形态学击中击不中变换的互被编码算法，通过对工业零件基本形状的仿真识别来区分高相似度图象，结果证明，该方法是有效的。     

Finsler Streamline Tracking with Single Tensor Orientation Distribution Function for High Angular Resolution Diffusion Imaging

We introduce a new framework based on Riemann-Finsler geometry for the analysis of 3D images with spherical codomain, more precisely, for which each voxel contains a set of directional measurements represented as samples on the unit sphere (antipodal points identified). The application we consider here is in medical imaging, notably in High Angular Resolution Diffusion Imaging (HARDI), but the methods are general and can be applied also in other contexts, such as material science, et cetera, whenever direction dependent quantities are relevant. Finding neural axons in human brain white matter is of significant importance in understanding human neurophysiology, and the possibility to extract them from a HARDI image has a potentially major impact on clinical practice, such as in neuronavigation, deep brain stimulation, et cetera. In this paper we introduce a novel fiber tracking method which is a generalization of the streamline tracking used extensively in Diffusion Tensor Imaging (DTI). This method is capable of finding intersecting fibers in voxels with complex diffusion profiles, and does not involve solving extrema of these profiles. We also introduce a single tensor representation for the orientation distribution function (ODF) to model the probability that a vector corresponds to a tangent of a fiber. The single tensor representation is chosen because it allows a natural choice of Finsler norm as well as regularization via the Laplace-Beltrami operator. In addition we define a new connectivity measure for HARDI-curves to filter the most prominent fiber candidates. We show some very promising results on both synthetic and real data.     

Using distance transform based algorithms for extracting measures of the fiber network in volume images of paper

Nowadays volume images are frequently used in many applications. Volume images of paper can be analyzed to increase the understanding of the complexity of the fiber network in paper and its effect on the optical and mechanical properties of paper. We show how curve and surface representations of the fiber, the fiber wall, and the fiber lumen can be computed using distance transform based algorithms. These representations are used for easy computation of wall thickness, degree of collapse, fiber length, slenderness ratio, fiber curl, and torsional resistance for the fibers. Free-fiber segments are identified, again using a distance transform based algorithm. Finally, we show tools for qualitative visual inspection of the fibers. The methods are evaluated and illustrated using both sets of synthetic and of real data. In addition to the analysis of the fiber network in paper, they can be used in many other applications where shape analysis of elongated objects is to be performed.