机器人视觉目标数字图像实时处理及分割

机器人视觉目标图像信噪比低、背景噪声干扰大,目标识别处理通常利用目标的灰度信息进行预处理。文中设计一种基于数学形态学和遗传算法的灰度图像实时预处理和阈值处理技术图像分割方法。描述图像的基本结构和特征,用具有一定形态的结构元去度量和提取图像中的对应形态,以达到对图像分析和识别;经过预处理的原始图像,采用基于遗传算法的最大类间方差图像分割法,非线性快速地查找到最优的分割阈值,从噪声图中分割出可能目标。基于ARM嵌入式微处理器,以复杂可编程逻辑器件CPLD作为时序控制单元,进行图像处理、分割,实现了图像信息的采集、存储、传输及处理为一体。仿真实验表明,该方法实时性好,简捷、快速,对运动目标的图像识别有较好的实用价值。     

用数学形态学方法进行红外图像分层解理

将形态学算子作用于高噪声,低质量的红外图像,对之进行分层解理还原,即将原本由各种信号叠加而成的红外图像重新分解成器件噪声,工作噪声和目标,背景等不同层次的信号。这种图像解理方法,以几何论和集合运算为基础,因而原理直观,计算简单,能为红外目标识别与跟踪提供有益的预处理。     

基于Gabor小波变换的汉字识别方法

基于Gabor滤波器时频局域性和方向选择性对应在汉字图像上即是对笔划宽度和方向的选择这一认识,考虑到笔划宽度多峰值、笔划是方向的特点,利用二维Gabor小波的多分辨率特性,对汉字图像进行了多尺度多方向分析,提取多个子平面的滤波器输出系数作为统计特征,实现了字符图像的高性能识别,为开展低质量、低分辨率的汉字字符图像识别研究提供了新方法。实验结果表明,用这种方法识别低分辨率的字符图像,效果优于其他方法。     

基于字符基元视觉短语的图像关键字识别

提出了一种利用字符基元视觉短语进行图像关键字识别的方法.该方法通过提取图像关键字的最大稳定极值区域,并进行归一化后得到字符基元.由于通常情况下每个关键字由若干字符基元构成,因此通过采用利用邻接的字符基元构造的视觉短语来提高图像关键字特征描述的可区分性;由于不同的字符基元组合结构可能构成不同的图像关键字,因此基于字符基元相邻关系判断短语几何结构的相似性.此方法不需要对图像进行二值化、布局分析和文本区域定位等预处理操作,具有更好的灵活性和鲁棒性.实验结果表明,此方法对于不同语言的图像关键字识别都具有较高的准确性.     

一种改进的图像序列运动小目标检测方法

针对目标背景运动的情况,在随机哈夫变换运动检测方法(MDRHT)的基础上,提出了基于中心偏移的哈夫变换运动检测方法(Center-Biased MDHT)。它利用图像中显著的边界信息,有效地估计图像序列帧间图像背景的整体运动;利用图像背景运动的一致性,预先设定帧间位移的阈值,减少了预处理阶段的计算量;对前后两帧相邻图像进行差分处理,充分利用小目标在差分图像中成对出现的特点,达到了在目标检测阶段减少计算量的目的。     

碎片图像字符特征提取方法研究

字符特征提取是含文字碎片图像拼接的关键环节,直接影响拼接效果。提出了一种包含文字信息的碎片图像特征提取方法 ,首先,对原始碎片图像进行预处理,分别得到字符和碎片背景的二值图像;然后,利用边缘检测算子提取字符和碎片背景的单像素边缘;最后,利用结构算子检测碎片图像中字符与碎片背景边缘的交叉点,并根据交叉点的信息提取字体边缘方向特征。仿真实验结果表明本文提出的碎片图像字符特征提取方法思路合理,能够快速、准确地提取出碎片图像中的字符特征,为后续的匹配拼接奠定基础。     

H-Cb混合颜色模型下快递单手写体提取算法研究

目的在不受光照条件的影响下,利用H-Cb混合颜色模型,提取快递单底单图像手写体文字信息。方法首先将图像从RGB颜色空间分别转换到HSI颜色空间和YCbCr颜色空间;然后将改进的YCbCr颜色空间的Cb颜色分量与HSI颜色空间的H颜色分量进行信息融合;最后对提取出的手写体文字信息进行阈值和反相处理,并将该算法提取结果与基于YCbCr颜色空间Cb颜色分量阈值分割方法和基于Lab颜色空间的手写文字聚类算法的提取结果,在分割效果、文字识别率上进行对比。结果利用H-Cb混合颜色模型检测出的手写体文字更准确,具有更高的识别率,在理想文字切分条件下识别率达96%。结论使用H-Cb混合颜色模型提取手写文字受光照条件影响小,提取出的图像噪声小、识别率高,算法简单可行,为彩色图像的检测与判定技术提供了支撑。     

声呐图像背景区域灰度统计特性分析与拟合

利用声呐进行水下目标定位识别是当前水下目标识别与跟踪的重要手段之一,由于声呐图像受噪声影响严重、分辨率低,对声呐图像的背景建模有助于其目标分割与识别。首先,分析声呐图像背景区域灰度的统计特性,结合其特点采用高斯分布、Gamma分布、威布尔分布、瑞利分布模型对6类不同背景区域声呐图像统计特性进行拟合,构建声呐图像背景区域模型。最后,采用?2准则和Kolmogorov距离误差评价准则评估拟合效果。拟合结果表明,高斯分布、Gamma分布和威布尔分布均能较好地逼近声呐图像背景区灰度统计特性。为满足实时性的应用需求,选用高斯分布构建声呐图像背景灰度统计模型是可行、合理的方案,从而为声呐图像预处理和目标分割提供了背景模型建模的理论依据。     

一种有效的红外图像中人造目标分割方法

提出一种红外图像单阈值分割方法。为了减少计算量,结合先验信息选择包含待分割目标的感兴趣区域,利用Bezier曲线法平滑感兴趣区域直方图的噪声;对平滑后的感兴趣区域的直方图求解其曲率曲线,用曲率曲线的波峰所对应的灰度值作为初始分割阈值;基于先验信息从初始分割阈值中确定最佳分割阈值并进行初步分割。为了弥补单纯利用阈值法分割的缺陷,结合上述分割结果和目标的边缘信息得到封闭性良好的完整目标的二值图像。实验结果表明,提出的方法能快速有效地将红外目标从复杂的背景中分割出来,算法的计算复杂度为O(MN)。     

紫外序列图像中目标的提取

分析了"日盲"紫外ICCD(增强型电荷耦合装置)探测系统所采集紫外图像中噪声和目标的特点,基于紫外图像的特点,提出了一种紫外序列图像中目标提取的方法.该方法首先采用时域递归低通滤波算法对紫外图像进行降噪处理,有效抑制了图像中的随机噪声,提高了图像的对比度.然后运用自适应阈值分割算法对目标进行了提取.实验结果表明,该方法能较好地检测出紫外序列图像中的目标,具有较强的噪声抑制能力.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Handwriting segmentation of unconstrained Oriya text

Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper we propose a water reservoir concept-based scheme for segmentation of unconstrained Oriya handwritten text into individual characters. Here, at first, the text image is segmented into lines, and the lines are then segmented into individual words. For line segmentation, the document is divided into vertical stripes. Analysing the heights of the water reservoirs obtained from different components of the document, the width of a stripe is calculated. Stripe-wise horizontal histograms are then computed and the relationship of the peak-valley points of the histograms is used for line segmentation. Based on vertical projection profiles and structural features of Oriya characters, text lines are segmented into words. For character segmentation, at first, the isolated and connected (touching) characters in a word are detected. Using structural, topological and water reservoir concept-based features, characters of the word that touch are then segmented. From experiments we have observed that the proposed “touching character” segmentation module has 96.7% accuracy for two-character touching strings.     

Efficient Analysis of Vertical Projection Histogram to Segment Arabic Handwritten Characters

The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained.     

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.     

一种新颖的汉字笔划提取算法

一些手写体汉字识别系统的笔划提取大都采取基于像素跟踪的细化算法,这些方法具有很大的不稳定性。考虑到汉字的结构特点,我们提出了汉字的直线段描述算法,直线段有0, 45, 90 和 135四种方向。在算法中,充分采用了基于结构的差分方法和基于统计的概率估计方法。实验表明,该算法有很好的鲁棒性,这为汉字的识别创造了条件。     

自然场景图像的字符识别方法

目的基于大津算法(Otsu算法)对图像进行分割,利用光学字符识别方法对自然场景图像中的英文字符进行识别。方法首先用分块Otsu算法对图像进行初步的二值化,然后通过对二值化结果的分析,把原始的输入图片分割成单个字符的子图,再对各子图重新用Otsu算法进行二值化,最后对最终得到的二值化结果进行识别,再结合之前得到的每幅图的字符数量信息和词典信息,对识别结果进行修正,得到最终的识别结果。结果在ICDAR2013数据集上测试文中算法,单词正确识别率为46.03%,总编辑距离为474.5。结论文中提出的以Otsu为基础的分块识别算法,能够更好地分割复杂背景图像的背景和文本,同时结合词典信息对识别结果进行了修正,改善了识别效果。     

小波分析在图像处理中的应用

本文介绍小波分析在图像处理中的应用,有多尺度边缘检测、图像识别、图像分割.边缘检测包括可去除多余边缘的一般灰度图像实例,彩色肿瘤图像的边缘提取,汉字放大.图像识别中,对于汉字或字母的识别,是将二维图像信号降维后再进行小波分解,通过计算各层次的分形维数形成特征向量从而可以进行识别;仿射变换后的汉字或其它图像可用多尺度小波的仿射不变函数进行对比识别.最后介绍了两种图像分割算法,首先利用基于能量分析的方法将纹理部分和平滑部分先分开,然后再利用基于奇异性分进行细分,得到最终分割的结果.