Properties,implementations and applications of rank filters

The results of research on rank filters are presented. The relationship of rank filters with other filters is briefly discussed. The main properties of rank filters are listed and an explanation is given for these properties. Several software and hardware implementations of the filter are described. Major applications to image processing are discussed, including noise smoothing, cluster detection, skeletization, edge enhancement and edge detection.     

Finite-dimensional filters with nonlinear drift,VI: Linear structure of Ω

Ever since the concept of estimation algebra was first introduced by Brockett and Mitter independently, it has been playing a crucial role in the investigation of finite-dimensional nonlinear filters. Researchers have classified all finite-dimensional estimation algebras of maximal rank with state space less than or equal to three. In this paper we study the structure of quadratic forms in a finite-dimensional estimation algebra. In particular, we prove that if the estimation algebra is finite dimensional and of maximal rank, then the Ω=(∂f _j /∂x _i −∂f _i /∂x _j )matrix, wheref denotes the drift term, is a linear matrix in the sense that all the entries in Ω are degree one polynomials. This theorem plays a fundamental role in the classification of finite-dimensional estimation algebra of maximal rank. This research was supported by Army Research Office Grants DAAH 04-93-0006 and DAAH 04-1-0530.     

基于优化设计Gabor滤波器的边缘提取方法

基于Gabor滤波器在空间和频率域上都显示良好的局部性能,提出一种用于边缘检测的优化Gabor滤波器的设计方法,并用Canny边缘检测准则对其性能进行了评测．实验结果表明该方法具有良好的边缘检测效果．     

Design of consensus and adaptive consensus filters for distributed parameter systems

This work establishes an abstract framework that considers the distributed filtering of spatially varying processes using a sensor network. It is assumed that the sensor network consists of groups of sensors, each of which provides a number of state measurements from sensing devices that are not necessarily identical and which only transmit their information to their own sensor group. A modification to the local spatially distributed filters provides the non-adaptive case of spatially distributed consensus filters which penalize the disagreement amongst themselves in a dynamic manner. A subsequent modification to this scheme incorporates the adaptation of the consensus gains in the disagreement terms of all local filters. Both the well-posedness of these two consensus spatially distributed filters and the convergence of the associated observation errors to zero in appropriate norms are presented. Their performance is demonstrated on three different examples of a diffusion partial differential equation with point measurements.     

Analysis of interaction between chemical agents and porous Si nanostructures using optical sensing properties of infra-red Rugate filters

Porous silicon based optical Rugate filters operating in the infra-red spectral range have been used to study chemical modifications (alkylation and oxidation) of the porous silicon internal specific surface. Influences of the chemical modifications on the filter response and on its sensitivity for solvent detection is described in details. Important quantitative information concerning structure and chemical coating of the filters is obtained. Application of the porous filters for the detection of different solvents filling their nanopores is studied.     

Construction and properties of spline dyadic wavelet filters

This paper focuses on the construction and properties of spline dyadic wavelet that equals its reconstruction wavelet. A general construction method of finite spline dyadic low-pass and high-pass filters is given. It proves that finite spline dyadic low-pass filters are symmetric about 0 or 1/2, but there are no finite spline high-pass filters possessing symmetry with respect to 0 or 1/2. It further shows that there exist infinite spline high-pass filters possessing symmetry with respect to 0 or 1/2, which can be constructed. Their energy is concentrated and so finite symmetric spline dyadic wavelet filter that equals its reconstruction filter can be obtained approximately. Construction examples for quadratic and cubic spline dyadic wavelet filters are given.     

一种用于图像边缘检测的自适应指数滤波器

本文提出一种用于图像边缘检测的自适应滤波器,利用它既可滤除图像噪声,又可获取精确度高的图像边缘。文中比较详细地分析了这种滤波器,并给出了递归实现算法。理论分析和实验结果表明,自适应指数滤波器的性能优于高斯滤波器。     

Finite dimensional filters with non-linear drift IX Construction of finite dimensional estimation algebras of non-maximal rank

The idea of using estimation algebra to construct finite-dimensional nonlinear filters was first proposed by Brockett and Mitter independently. It has proven to be an invaluable tool in the study of nonlinear filtering problem. In 1983, Brockett proposed to classify all finite-dimensional estimation algebras. In this paper, we give the construction of finite-dimensional estimation algebras of non-maximal rank. These non-maximal rank finite-dimensional estimation algebras play an important role in Brockett's classification problem.     

An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection

Psychophysical and neurophysiological evidence about the human visual system shows the existence of a mechanism, called surround suppression, which inhibits the response of an edge in the presence of other similar edges in the surroundings. A simple computational model of this phenomenon has been previously proposed by us, by introducing an inhibition term that is supposed to be high on texture and low on isolated edges. While such an approach leads to better discrimination between object contours and texture edges w.r.t. methods based on the sole gradient magnitude, it has two drawbacks: first, a phenomenon called self-inhibition occurs, so that the inhibition term is quite high on isolated contours too; previous attempts to overcome self-inhibition result in slow and inelegant algorithms. Second, an input parameter called “inhibition level” needs to be introduced, whose value is left to heuristics. The contribution of this paper is two-fold: on one hand, we propose a new model for the inhibition term, based on the theory of steerable filters, to reduce self-inhibition. On the other hand, we introduce a simple method to combine the binary edge maps obtained by different inhibition levels, so that the inhibition level is no longer specified by the user. The proposed approach is validated by a broad range of experimental results.     

Statistical properties,fixed points,and decomposition with WMMR filters

WMMR ^m filters weight the m ordered values in the window with minimum range. If m is not specified, it is assumed to be N + 1 for a window of length 2N + 1. Previous work has demonstrated a subclass of these filters that may be optimized for edge enhancement in that their output converges to the closest perfect edge. In this work it is shown that normalized WMMR ^m filters, whose weights sum to unity, are affine equivariant. The concept of the breakpoint of a filter is discussed, and the optimality of median and WMMR filters under the breakpoint concept is demonstrated. The optimality of a WMMR ^m filter and of a similar generalized-order-statistic (GOS) filter is demonstrated for various non-L ^Pcriterion, which we call closeness measures. Fixed-point results similar to those derived by Gallagher and Wise (see N.C. Gallagher and G.L. Wise, IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-29, 1981, pp. 1136–1141) for the median filter are derived for order-statistic (OS) and WMMR filters with convex weights (weights that sum to anity and are nonnegative), i.e., we completely classify the fixed points under the assumption of a finite-length signal with constant boundaries. These fixed points are shown to be almost always the class of piecewise-constant (PICO) signals. The use of WMMR filters for signal decomposition and filtering based on the Haar basis is discussed. WMMR filters with window width 2N + 1 are shown to be linear over the PICO(N + 1) signals (minimum constant length N+1). Concepts similar to lowpass, highpass, and bandpass for filtering PICO signals are introduced. Application of the filters to 1-dimensional biological data (non-PICO) and images of printed-circuit boards is then demonstrated, as is application to images in general.     

Multi-view fast object detection by using extended haar filters in uncontrolled environments

In this paper, we propose multi-view object detection methodology by using specific extended class of haar-like filters, which apparently detects the object with high accuracy in the unconstraint environments. There are several object detection techniques, which work well in restricted environments, where illumination is constant and the view angle of the object is restricted. The proposed object detection methodology successfully detects faces, cars, logo objects at any size and pose with high accuracy in real world conditions. To cope with angle variation, we propose a multiple trained cascades by using the proposed filters, which performs even better detection by spanning a different range of orientation in each cascade. We tested the proposed approach by still images by using image databases and conducted some evaluations by using video images from an IP camera placed in outdoor. We tested the method for detecting face, logo, and vehicle in different environments. The experimental results show that the proposed method yields higher classification performance than Viola and Jones’s detector, which uses a single feature for each weak classifier. Given the less number of features, our detector detects any face, object, or vehicle in 15 fps when using 4 megapixel images with 95% accuracy on an Intel i7 2.8 GHz machine.     

Kalman filters in non-uniformly sampled multirate systems: For FDI and beyond

The first part of the paper is the development of a data-driven Kalman filter for a non-uniformly sampled multirate (NUSM) system. Algorithms for both one-step predictor and filtering are developed and analysis of stability and convergence is conducted in the NUSM framework. The second part of the paper investigates a Kalman filter-based methodology for unified detection and isolation of sensor, actuator, and process faults in the NUSM system with analysis on fault detectability and isolability. Case studies using data respectively collected from a pilot experimental plant and a simulated system are conducted to justify the practicality of the proposed theory.     

基于随机Hough变换的深度图像分割

提出了基于随机Hough变换的深度图像分割算法,该算法采用随机Hough变换在深度图像中寻找平面,具有对噪声不敏感的优点．通过对一常用深度图像数据库(ABW图像库)的分割实验,并将实验结果同4种经典的深度图像分割算法在同一数据库中的分割结果作了比较分析,表明该算法对噪声不敏感,分割性能优于4种经典算法。     

基于传输范围覆盖的无线传感器网络广播算法

广播在无线传感器网络中具有广泛的应用.泛洪广播虽然简单,但极为耗能.基于概率、基于计数以及基于位置等广播算法不使用任何邻节点信息,简单但性能改善有限.MPR以及多数基于连通支配集的广播算法利用2-hop邻节点信息计算转发节点集,虽然获得了较好的性能,但需更多的通信开销以获取和维护2-hop邻节点信息.为减少转发节点并节约通信开销,较好的实现无线传感器网络广播,本文利用1-hop邻节点信息提出了一种基于传输范围覆盖的广播算法TRCB.算法的主要思想是如果发送节点U的1-hop邻节点V被U和不包括节点V的其余1-hop邻节点集S覆盖,则节点V将不参与转发.分析与仿真表明,TRCB有效减少了冗余转发节点,从而节约了无线传感器网络能耗;并且利用1-hop邻节点信息,具有通信开销小、适应网络拓扑变化的特点;且其性能优于同样利用1-hop邻节点信息的边缘转发算法EFB.     

基于结构局部边缘模式的绿色植物物种识别

植物图像的边缘检测是基于图像分析植物物种识别技术的重要环节,利用边缘检测可以增强图像中的轮廓边缘、细节等信息,达到将目标植物从图像中分离出来的目的。因此,为了在绿色植物物种识别中,将图像中的目标植物与背景分离,首先利用可变局部边缘模式算子提取植物图像的边缘特征,再通过结构化局部边缘模式对边缘特征进行编码,来刻画局部边缘。实验结果表明,提出的边缘模式思想在绿色植物物种识别上能得到更高的识别率。     

基于多尺度边缘和局部熵原理的前方车辆检测

提出一种基于多尺度边缘和局部熵原理的前方车辆的检测算法。该算法利用车辆图像的边缘和纹理等视觉特征,根据摄像机参数得到远、中、近距离的三个尺度的图像,用一种改进的边缘检测算法分析每幅图像的边缘,得到车辆的感兴趣区域ROI,最后通过应用局部熵原理来排除错误的结果。对同一帧序列用文中算法和传统算法进行测试,文中算法提高了检测的正确率,并减少了误检的数量,该算法同时适用于静止和运动的车辆,并且对中远距离车辆有较好的检测效果。     

基于测距传感器的焊缝位置检测方法与实现

焊缝跟踪技术是焊接自动化领域一项重点研究内容,可以分为前置焊缝寻位和实时焊缝跟踪。提出了一种前置焊缝寻位方法:在焊枪夹具上安装激光测距传感器,介绍了激光测距传感器的选型和接入机器人控制系统的方法,建立了光点位置和焊枪TCP位置的转换的模型,描述了基于边缘检测的焊缝寻位过程。最后,以KUKA弧焊机器人系统为平台搭建实验系统,测试了所提出的方法。实验表明,该方法可以成功纠正出因工件加工、拼接、装夹和焊接热变形等因素导致的焊缝位置偏移;较之原有的接触寻位法,提高了寻位效率,且易于实现。     

An integrated edge detection method using mathematical morphology

This paper presents an edge detection method based on mathematical morphology. The proposed scheme consists of four steps: preprocessing, edge extraction, edge decision, and postprocessing. In the preprocessing step, a morphological central transformation is applied to remove noise. In the edge extraction and decision steps, a morphological edge extractor is designed to estimate the edge information of an image, and an edge decision criterion is followed to determine whether a pixel is an edge or not. In the postprocessing step, the morphological hit-or-miss transformation is utilized to improve the correctness of the detected edges. It is proved theoretically for the correctness and effectiveness for detecting ideal edges. Experimental results show that the proposed method works well on both artificial and real images. The text was submitted by the authors in English. Chin-Pan Huang was born in 1959 in Taiwan, Republic of China. He received the B.S. and M.S. degrees in electrical engineering from Chung Cheng Institute of Technology, Taiwan, in 1981 and in 1985, respectively. In 1996, he received the Ph.D. degree in electrical engineering from the University of Pittsburgh in the United States. From 1996 to 2002, he was an associate scientist of the Electronic System Division in Chung Shan Institute of Science and Technology. He then joined the Department of Computer and Communication Engineering at Ming Chuan University in August 2002 and is currently an assistant professor there. His recent research interests include data compression, computer vision, digital image processing, and pattern recognition. Ran-Zan Wang was born in 1972 in Fukien, Republic of China. He received his B.S. degree in computer engineering and science in 1994 and M.S. degree in electrical engineering and computer science in 1996, both from Yuan-Ze University. In 2001, he received his Ph.D. degree in computer and information science from National Chiao Tung University. In 2001–2002, he was an assistant professor at the Department of Computer Engineering at the Van Nung Institute of Technology. He joined the Department of Computer and Communication Engineering at Ming Chuan University in August 2002 and is currently an assistant professor there. His recent research interests include data hiding and digital watermarking, image processing, and pattern recognition. Dr. Wang is a member of the Phi Tau Phi Scholastic Honor Society.     

Line Extraction in 2D Range Images for Mobile Robotics

This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint detection and line extraction. In data pre-processing, low-level data organization and processing are discussed, with emphasis to sensor bias compensation. Breakpoint detection allows to determine sequences of measurements which are not interrupted by scanning surface changing. Two breakpoint detectors are investigated, one based on adaptive thresholding, and the other on Kalman filtering. Implementation and tuning of both detectors are also investigated. Line extraction is performed to each continuous scan sequence in a range image by applying line kernels. We have investigated two classic kernels, commonly used in mobile robots, and our Split-and-Merge Fuzzy (SMF) line extractor. SMF employs fuzzy clustering in a split-and-merge framework without the need to guess the number of clusters. Qualitative and quantitative comparisons using simulated and real images illustrate the main characteristics of the framework when using different methods for breakpoint and line detection. These comparisons illustrate the characteristics of each estimator, which can be exploited according to the platform computing power and the application accuracy requirements.     

一种自适应的图像边缘检测方法

边缘检测是一项基本而重要的图像处理技术。本文设计了一种基于局部小窗口的自适应门限方法,可以快速而精确的确定局部门限,从而将小窗口内的边缘像素点和非边缘像素点区分开来。