机器视觉中特征点提取算法的探讨

机器视觉中特征点的提取是基于点特征图像配准算法的一个非常关键的步骤。首先从特征点定义出发,介绍了近年来国内外出现的几种常用的点特征提取算法,然后利用特征提取方法评价标准,对各种检测算子进行了综合的分析和讨论,最后,通过运用VC++编程语言工具进行算法仿真,对试验结果进行比较分析,理论与实践相结合,合理评价了各种方法的性能、优缺点、适用环境,论述对学习图像检测和具体工程应用具有很好的参考价值,并且为这些方法的改进提供了指导性意见。     

基于图像质量分区的指纹特征提取

低质量指纹图像的特征提取和变形指纹的匹配是当前指纹识别研究中的两个主要问题。很多算法在特征提取时不区分高、低质量区域,结果在高质量区域耗费了过多的运算时间和计算资源。本文提出了一种基于图像质量分区的指纹特征提取方法,先用一种简单的图像区域质量计算方法评价各区域的图像质量,然后对高质量区域直接从灰度图像跟踪纹线、提取节点,对低质量区域执行传统的方向计算、增强、二值化和细化后提取特征。实验结果表明,该方法不仅提高了特征提取的速度,在准确性上也有所提高。     

煤矿高压电网小电流接地选线技术的比较与研究

文章分析了在理论研究和实践应用中都较为成熟的煤矿高压电网小电流接地选线方法的优缺点,并根据选线准确性、无误动作、对故障类型的广泛适应性及能够适用于中性点不接地或经消弧线圈接地的方式等要求,提出了一种结合稳态和暂态选线方法、优势互补的综合选线法,并给出了该方法的原理框图。     

正弦信号测试仪的设计

正弦波信号是工程实践中应用最多的电信号之一,对幅值、频率、相位三要素的测量是测量正弦波信号的主要内容,本设计从该三要素出发提出了设计正弦信号测试仪的具体方案;正弦信号的幅值测量模块以高精度峰值检波电路为核心,结合单片机与AD采样技术实现幅值测量功能;频率测量模块采用专用芯片ICM7216D实现频率测量功能;相位测量模块则使用相位检测电路与单片机相结合实现相位测量功能;通过实践证明,该正弦信号测试仪设计切实可行,且具有硬件结构简单、软件设计灵活、适用面广等优点,是一种有较高应用价值的正弦信号测量仪器.     

基于DSP的数字倾角仪的设计与实现

为了能测量连续动态变化的倾角,需要提出一种动态性能良好的倾角仪;为了减小误差,采用数字信号进行测量;基于重力原理,属于摆式倾角仪,摆角由编码器转换为数字信号测量,用DSP处理数字信号;测量机架中采用磁阻尼原理能够使摆锤迅速稳定到平衡位置,具有良好的瞬态性能,实践结果表明,本倾角仪可测量±45°范围内的任意倾角,精度可达±0.07%,读数稳定时间在0.05s以内,本倾角仪量程大、精度高、线性好、抗干扰强、结构简单、读数快,能满足对连续变化角度测量的需要。     

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

Human tracking and silhouette extraction for human–robot interaction systems

In this study, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with a pan-tilt stereo camera, so that it can assist in gesture and gait recognition in the field of Human–Robot Interaction (HRI). The proposed system consists of three modules: detection, tracking and silhouette extraction. These modules are robust to camera movements, and they work interactively in near real-time. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift-based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap was estimated in advance and then effectively incorporated into the graph-cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data, and it was shown to detect and track multiple people very well and also produce high-quality silhouettes.

Finding the number of clusters in ordered dissimilarities

As humans, we have innate faculties that allow us to efficiently segment groups of objects. Computers, to some degree, can be programmed with similar categorical capabilities, which stem from exploratory data analysis. Out of the various subsets of data reasoning, clustering provides insight into the structure and relationships of input samples situated in a number of distributions. To determine these relationships, many clustering methods rely on one or more human inputs; the most important being the number of distributions, c, to seek. This work investigates a technique for estimating the number of clusters from a general type of data called relational data. Several numerical examples are presented to illustrate the effectiveness of the proposed method.     

Text extraction and enhancement of binary images using cellular automata

Text characters embedded in images represent a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values, and complex backgrounds. Existing methods cannot handle well those texts with different contrast or embedded in a complex image background. In this paper, a set of sequential algorithms for text extraction and enhancement of image using cellular automata are proposed. The image enhancement includes gray level, contrast manipulation, edge detection, and filtering. First, it applies edge detection and uses a threshold to filter out for low-contrast text and simplify complex background of high-contrast text from binary image. The proposed algorithm is simple and easy to use and requires only a sample texture binary image as an input. It generates textures with perceived quality, better than those proposed by earlier published techniques. The performance of our method is demonstrated by presenting experimental results for a set of text based binary images. The quality of thresholding is assessed using the precision and recall analysis of the resultant text in the binary image.     

An FPGA-based face recognition using combined 5/3 DWT with PCA methods

This paper describes a new scheme for feature extraction from facial images on FPGA. The proposed method is comprised of two stages. The first stage uses the 5/3 DWT to decompose the original face image into LL, LH, HL, and HH wavelet coefficient to reduce the size of the image. In the second stage, PCA is employed to extract the face features from the wavelet coefficients. Here we use the power iteration algorithm to find the eigenvector of the covariance matrix. We present an efficient hardware architecture using combination of parallel processing module and serial processing module. This method can take the benefits of parallel usage advantage of FPGAs and can save hardware resources. Complete hardware implemented on a Stratix II FPGA. The experimental results show that the system works with high processing rate and only 21% of the logic resources an FPGA are consumed by face recognition logic Thus it is very suitable for the low cost implementation of the face recognition system.