共查询到19条相似文献,搜索用时 62 毫秒
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一种指针式仪表非接触测量方法 总被引:1,自引:0,他引:1
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. 相似文献
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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。 相似文献
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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。 相似文献
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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。 相似文献
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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. 相似文献
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一种新型指针仪表识别方法研究 总被引:1,自引:0,他引:1
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。 相似文献
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孔陈祥 《单片机与嵌入式系统应用》2021,21(7):38-42
针对传统变电站表盘识别方法误差较大的问题,设计了 一种新型表盘图像识别方法,通过直方图均衡算法对CMOS采集的变电站表盘图像信息进行处理,提高图像对比度,使表盘图像识别更加精准,利用高效(Hough)变换指针检测法减少表盘识别过程中产生的误差,使识别误差波形更加稳定,保证表盘识别数据的准确性.最后通过Visual计算机... 相似文献
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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. 相似文献
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Ataollah Ebrahimzade Sherme 《Applied Soft Computing》2012,12(1):453-461
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. 相似文献
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《Engineering Applications of Artificial Intelligence》2005,18(1):13-19
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. 相似文献
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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. 相似文献
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Jongju Shin Author Vitae Author Vitae Daijin Kim Author Vitae 《Pattern recognition》2011,44(3):559-571
This paper proposes a real-time lip reading system (consisting of a lip detector, lip tracker, lip activation detector, and word classifier), which can recognize isolated Korean words. Lip detection is performed in several stages: face detection, eye detection, mouth detection, mouth end-point detection, and active appearance model (AAM) fitting. Lip tracking is then undertaken via a novel two-stage lip tracking method, where the model-based Lucas-Kanade feature tracker is used to track the outer lip, and then a fast block matching algorithm is used to track the inner lip. Lip activation detection is undertaken through a neural network classifier, the input for which being a combination of the lip motion energy function and the first dominant shape feature. In the last step, input words are defined and recognized by three different classifiers: HMM, ANN, and K-NN. We combine the proposed lip reading system with an audio-only automatic speech recognition (ASR) system to improve the word recognition performance in the noisy environments. We then demonstrate the potential applicability of the combined system for use within hands free in-vehicle navigation devices. Results from experiments undertaken on 30 isolated Korean words using the K-NN classifier at a speed of 15 fps demonstrate that the proposed lip reading system achieves a 92.67% word correct rate (WCR) for person-dependent tests, and a 46.50% WCR for person-independent tests. Also, the combined audio-visual ASR system increases the WCR from 0% to 60% in a noisy environment. 相似文献
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Kirt Lillywhite Dah-Jye Lee Beau Tippetts James Archibald 《Pattern recognition》2013,46(12):3300-3314
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. 相似文献