指针式仪表读数的机器视觉智能识别方法

为快速准确地自动识别指针式仪表读数,采用机器视觉技术,结合减影法和Hough变换法对仪表读数进行智能识别.对指针式仪表图像进行图像二值化、形态学处理和边缘检测预处理;利用Hough变换检测仪表中的指针,计算得到指针方向和定位圆形,实现指针式仪表的智能识别.实验结果表明:读数识别的平均相对误差为0.91%,精度较高,能较好地识别指针式仪表读数.     

一种指针式仪表非接触测量方法

提出了一种基于图像处理技术的指针式仪表非接触测量方法.研究了指针式仪表的圆心、半径、指针角度以及零刻度自动检测与校准的计算,在基于点Hough变换拟合其圆心与半径以及中心投影法确定指针大概位置的基础上,提出了一种基于亚像素定位的拟合指针直线的方法,具有指针式仪表高精度的自动检测与定位,从而实现了指针式仪表非接触测量.实验表明,该方法具有快速、准确等特点且切实可行.     

非均匀光照下指针式仪表自动判读算法设计

拟合人眼视觉机制提出了非均匀光照下指针仪表图像的预处理算法,在此基础上进一步设计了座舱指针式仪表自动判读算法。首先对仪表盘图像进行亮度均衡、二值化变换,再将指针细化,然后根据改进的Hough变换提取目标信息,确定座舱指针式仪表的读数。实验结果表明,该算法有效地解决了在非均匀光照情况下的飞机座舱指针式仪表自动判读,降低了判读误差。     

一种圆形指针式仪表倾斜检测方法

圆形指针式仪表的倾斜,不利于仪表数字参数区域字符图像的定位和识别。提出一种基于检测仪表名称倾斜的方法来获得圆形指针式仪表的倾斜角度。该方法将仪表表盘上的仪表名称看成一个整体,结合Canny算子、形态学与Hough变换算子来检测其倾斜角度,然后根据该倾斜角度绕仪表表盘中心点对表盘进行旋转校正,使仪表参数区域字符回到固定位置,方便仪表数字参数区域字符的定位与提高数字字符识别的正确率。实验结果表明：该算法能快速准确地检测出圆形指针式仪表图像的倾斜角度,有一定的实用价值。     

改进ORB和Hough变换的指针式仪表识读方法

为了实现指针式仪表的自动识读,提出一种基于改进ORB（Oriented FAST and Rotated BRIEF）和Hough变换算法的指针式仪表识读算法。利用角点强化方法加强ORB算法检测的特征点,通过特征点匹配对计算模板图像与待检测图像之间的透视变换矩阵。利用数学形态学处理、阈值分割等图像预处理提取指针,并提出一种用于确定指针旋转圆心的基于ORB特征匹配对的相似特征三角形方法,结合投影法定位指针方向。利用指针细化算法和添加圆心约束的Hough变换算法检测指针角度。最后根据仪表的先验信息得到读数结果。实验结果表明该算法在识读速度和精度等方面都能够满足指针式仪表识读的要求,具有较高的可靠性和工程应用价值。     

基于图像处理的指针式仪表智能识别方法研究

针对仪表的智能识别方法在电力系统的工程应用,利用图像处理的方法对指针式仪表的智能读数进行研究;首先,针对摄像头多视角、多距离的安装问题,利用定向二进制描述符ORB (oriented FAST and rotated BRIEF)算法求解仪表模板图像与待测图像的透视变换矩阵,用于定位指针旋转区域;然后根据表盘灰度特征信息,提出基于圆周区域的累积直方图法(circle-based regional cumu-lative histogram,CRH)对指针进行定位,由指针偏转角度得到读数;实验结果表明,该方法对指针读数识别十分有效,达到了实用化要求,且具有实时性好和精度高的特点.     

一种新型指针仪表识别方法研究

本文在简单介绍指针式仪表及其常用识别方法的基础上，提出了一种基于最大灰度相减法的新型识别方法。然后详细介绍了这一方法的工作过程，着重阐述了几个关键环节所用到的图像处理算法。最后，给出了试验的结果及分析，指出了优化的途径。     

嵌入式环境指针式仪表快速识别算法研究

论文在对已有的各种使用图像处理手段进行仪表指针识别方案进行研究的基础上,根据工控环境中指针式仪表所具有特点,设计了基于多分辨率处理、霍夫变换的快速、准确的指针识别算法,该算法是已有识别方案的改进与融合。为了对算法性能进行测试,论文在所提算法的基础上,设计了简单并且容易部署的指针识别系统。识别系统的运行数据证明,论文所提算法可在资源有限的嵌入式系统中对工控环境中的指针式仪表读数进行快速而准确的识别。     

基于卷积神经网络的指针式仪表识别

目前大部分研究指针式仪表识别的方法中提取指针是完全基于传统的图像处理技术,提取过程较为复杂且步骤繁多.为了有效解决指针式仪表读数识别中指针中轴线所在直线提取困难及识别精度不高等问题,本文提出了一种基于深度学习的指针式仪表的识别方法.首先用Faster R-CNN算法检测仪表圆盘,再采用基于深度学习的方法Faster R...     

A novel method for automatic modulation recognition

Automatic recognition of the digital modulation plays an important role in various applications. This paper investigates the design of an accurate system for recognition of digital modulations. First, it is introduced an efficient pattern recognition system that includes two main modules: the feature extraction module and the classifier module. Feature extraction module extracts a suitable combination of the higher order moments up to eighth, higher order cumulants up to eighth and instantaneous characteristics of digital modulations. These combinations of the features are applied for the first time in this area. In the classifier module, two important classes of supervised classifiers, i.e., multi-layer perceptron (MLP) neural network and hierarchical multi-class support vector machine based classifier are investigated. By experimental study, we choose the best classifier for recognition of the considered modulations. Then, we propose a hybrid heuristic recognition system that an optimization module is added to improve the generalization performance of the classifier. In this module we have used a new optimization algorithm called Bees Algorithm. This module optimizes the classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent technique has very high recognition accuracy even at low levels of SNR with a little number of the features.     

A genetic classification method for speaker recognition

Gaussian mixture model (GMM) has been widely used for modeling speakers. In speaker identification, one major problem is how to generate a set of GMMs for identification purposes based upon the training data. Due to the hill-climbing characteristic of the maximum likelihood (ML) method, any arbitrary estimate of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, this paper proposes a hybrid training method based on genetic algorithm (GA). It utilizes the global searching capability of GA and combines the effectiveness of the ML method. Experimental results based on TI46 and TIMIT showed that this hybrid GA could obtain more optimized GMMs and better results than the simple GA and the traditional ML method.     

A novel matrix-based method for face recognition

Two-dimensional principal component analysis (2DPCA) is one of the representative techniques for image representation and recognition. However, it fails in detecting the local variation of images, which characterizes the most important modes of variability of face images. Motivated by the fact that the local spatial geometric structure of images is effectual in learning the representative image space, we assign different weight to each training image and then present a novel method, namely local two-dimensional principal component analysis (L2DPCA), which explicitly considers the variations among nearby data. Finally, we describe an effective algorithm L2DPCA+2DPCA to further reduce dimensionality reduction. Extensive experimental results on two-face databases (Yale and AR) show the efficiency of the proposed method.     

A method of recognition for handwritten block capitals

Handwritten block capitals are expressed as lines and junctions; and measurements from these are applied to a Gaussian statistical classifier. Results for an alphanumeric test set are 6.7% reject and 0.65% error.     

A configurable method for multi-style license plate recognition

Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness.     

A Kernel-based sparse representation method for face recognition

Sparse Representation Method has been proved to outperform conventional face recognition (FR) methods and is widely applied in recent years. A novel Kernel-based Sparse Representation Method (KBSRM) is proposed in this paper. In order to cope with the possible complex variation of the face images caused by varying facial expression and pose, the KBSRM first uses a kernel-induced distance to determine N nearest neighbors of the testing sample from all the training samples. Then, in the second step, the KBSRM represents the testing sample as a linear combination of the determinate N nearest neighbors and performs the classification by the representation result. It can be inferred that the N nearest training samples selected are closer to the test sample than the rest, so using the N nearest neighbors to represent the testing sample can make the ultimate classification more accurate. A number of FR experiments show that the KBSRM can achieve a better classification result than the algorithm mentioned in Xu et al. (Neural Comput Appl doi:10.1007/s00521-012-0833-5).     

A feature construction method for general object recognition

This paper presents a novel approach for object detection using a feature construction method called Evolution-COnstructed (ECO) features. Most other object recognition approaches rely on human experts to construct features. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, and no limitations to certain types of image sources. We show in our experiments that ECO features perform better or comparable with hand-crafted state-of-the-art object recognition algorithms. An analysis is given of ECO features which includes a visualization of ECO features and improvements made to the algorithm.