局部高亮干扰文本图像的二值化方法研究

本文提出一种新的基于Curvelet变换的文本图像二值化处理方法,以消除文本图像中局部高亮度区域对二值化图像质量的影响.首先对具有局部高亮度区域干扰的原始文本图像进行Curvelet变换,得到图像在曲波域的Curvelet系数集；然后根据各Curvelet系数所表征的图像特征,对Curvelet系数进行非线性增强,以优化文本图像的直方图分布；对增强的Curvelet系数集进行反变换,得到直方图优化后的时域图像,进而应用Otsu方法实现文本图像二值化.应用本文方法对具有带状及点状局部高亮度区域的文本图像进行二值化处理,并采用ABBYYFineReaderl0对二值图像进行OCR识别.实验结果表明,通过本文提出的处理方法所得到的二值化图像,其字符的OCR识别准确率最高可达94.81％,优于其他四种典型的图像二值化处理方法.     

Text binarization in color documents

This article presents a new method for the binarization of color document images. Initially, the colors of the document image are reduced to a small number using a new color reduction technique. Specifically, this technique estimates the dominant colors and then assigns the original image colors to them in order that the background and text components to become uniform. Each dominant color defines a color plane in which the connected components (CCs) are extracted. Next, in each color plane a CC filtering procedure is applied which is followed by a grouping procedure. At the end of this stage, blocks of CCs are constructed which are next redefined by obtaining the direction of connection (DOC) property for each CC. Using the DOC property, the blocks of CCs are classified as text or nontext. The identified text blocks are binarized properly using suitable binarization techniques, considering the rest of the pixels as background. The final result is a binary image which contains always black characters in white background independently of the original colors of each text block. The proposed document binarization approach can also be used for binarization of noisy color (or gray‐scale) document images. Several experiments that confirm the effectiveness of the proposed technique are presented. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 262–274, 2006     

An integrated approach to document decomposition and structural analysis

A document image is a visual representation of a paper document, such as a journal article page, a cover page of facsimile transmission, office correspondence, an application form, etc. Document image understanding as a research endeavor consists of developing processes for taking a document through various representations, from scanned image to semantic representation. This article describes document decomposition and structural analysis, which constitutes one of the major processes involved in document image understanding. The current state of the art and future directions in the areas of document segmentation, layout analysis, and logical block grouping are indicated. A system that performs decomposition and structural analysis (including logical grouping and read-order determination) on complex multiarticled documents is presented. This system uses bottom-up segmentation techniques to identify the block structure of a document, and layout rules to classify and group these blocks into logical units that represent meaningful subdivisions of the document. Experimental results showing the efficiency of this approach are presented and discussed. © 1996 John Wiley & Sons, Inc.     

Efficient document-image super-resolution using convolutional neural network

Experiments performed by us using optical character recognizers (OCRs) show that the character level accuracy of the OCR reduces significantly with decrease in the spatial resolution of document images. There are real life scenarios, where high-resolution (HR) images are not available, where it is desirable to enhance the resolution of the low-resolution (LR) document image. In this paper, our objective is to construct a HR image, given a single LR binary image. The works reported in the literature mostly deal with super-resolution of natural images, whereas we try to overcome the spatial resolution problem in document images. We have trained and obtained a novel convolutional model based on neural networks, which achieves significant improvement in terms of the peak-signal-to-noise ratio (PSNR) of the reconstructed HR images. Using parametric rectified linear units, mean PSNR improvements of 2.32, 4.38, 6.43 and 8.92 dB have been achieved over those of LR input images of 50, 75, 100 and 150 dots per inch (dpi) resolution and average word level accuracy of almost 43%, 45% and 57% on 75 dpi Tamil, English and Kannada images, respectively.     

偏微分方程在生物医学图像分析中的应用

基于偏微分方程的图像处理技术是最近十多年在图像处理与分析领域得到快速发展的一类新的图像处理技术。该类技术一定程度上克服了经典的图像处理技术难以处理的某些困难问题,因此成为图像处理领域的一个研究热点,并在生物医学图像的分析中得到广泛的应用。本文拟通过对该类技术在生物医学图像分析中的应用的介绍,对基于偏微分方程的图像处理技术的主要发展过程、研究现状、技术特点、应用等诸方面做一个简单综述。     

An Elevator Button Recognition Method Combining YOLOv5 and OCR

Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in real-time. The advantages of YOLOv5 make it an ideal choice for detecting the position of multiple buttons in an elevator, but it’s not good at specific word recognition. Optical character recognition (OCR) is a well-known technique for character recognition. This paper innovatively improved the YOLOv5 network, integrated OCR technology, and applied them to the elevator button recognition process. First, we changed the detection scale in the YOLOv5 network and only maintained the detection scales of 40 * 40 and 80 * 80, thus improving the overall object detection speed. Then, we put a modified OCR branch after the YOLOv5 network to identify the numbers on the buttons. Finally, we verified this method on different datasets and compared it with other typical methods. The results show that the average recall and precision of this method are 81.2% and 92.4%. Compared with others, the accuracy of this method has reached a very high level, but the recognition speed has reached 0.056 s, which is far higher than other methods.     

Automated performance evaluation of document image analysis systems: Issues and practice

The performance of document image analysis systems is affected by a variety of variables that alter the quality of documents. Objective evaluation and characterization of systems usually require large quantities of test data, and it is important to automate evaluation processes. In this article, issues in designing tools for automated evaluation of document image analysis techniques and systems are discussed, and some examples are presented. © 1996 John Wiley & Sons, Inc.     

Recognition for 3‐D objects based on the determination of the position and the orientation from a noisy single perspective view

Abstract

Recognizing 3‐D objects from 2‐D images has been an important area of research in computer vision. In this paper, an efficient method is developed to determine the position and the orientation of a 3‐D polyhedron in a noisy image. This method assumes that the direction of a straight line on an image can be determined from noisy data. A set of linear equations are used to determine the orientation of an object. In cases where there are not enough straight lines of an image presented, the constraint equations can be introduced and the iterative process is applied. However the iterative process is simple and the divergence is not a problem. After the orientation is determined, the straight lines, corresponding to the visible edges of the object, are used to determine the intersection points. From the intersections and the corresponding corners of the polyhedron, another set of linear equations are used to obtain the position of the object.

If the sum of the perpendicular distances, between the endpoints of the image projection of each model line and the corresponding image line, divided by the number of end‐points is less than a threshold, then the matching process is passed. It is shown, in this paper, that we cannot distinguish between geometrically similar objects without texture analysis from a single perspective view. Thus, the threshold, ?_i , which is determined by a scheme developed here, of model i is used to check whether the candidate model is accepted. Finally, all the concepts, presented in this paper, are illustrated through several examples given in this paper.     

Cellular logic array processing techniques for high-throughput image processing systems

This paper describes certain image processing techniques within the framework ofcellular automata andnormal algorithms for high-throughput data processing. The central idea on which these techniques have been developed is that a digital image can be treated as acellular automaton configuration, and an image processing operation, as anevolution of the automaton due to an updating rule that describes a relational attribute among the pixel values in a specific neighbourhood. Filtering operations on digital images, like that of thinning, edge detection segmentation, erosion and dilation are modelled and realized using cellular automata.     

An automatic approach for line detection and removal without smash-up characters

Abstract

In composite document image, handwritten and printed text is often found to be overlapped with printed lines. The problem becomes critical for obscure and broken lines at multiple positions. Consequently, line removal is unavoidable pre-processing stage in the development of robust object recognisers. Moreover, the restoration of the smash-up characters after removal of lines still persists to be a problem of interest. This paper presents a new approach to detect and remove unwanted printed line inherited in the text image at any position without character distortion to avoid restoration stage. The proposed technique is based on connected component analysis. Experiments are conducted using single line images that scanned and extracted manually from several documents and forms. It is demonstrated that our approach is equally suitable to deal with line removal in printed and handwritten text written in any language circumvent restoration stage. Promising results are reported in comparison with the other researchers in the state of the arts.     

基于小波纹理的文种改进算法

基于小波纹理分析的文种识别方法，提取的是文档图像的整体特征，具有算法简单、处理较快的特点。我们对这种算法进行了改进，使用可变阈值代替距离公式，使得判决结果可按用户的意愿调节，并增加了拒识功能，用1420幅图进行实验，得到的准确率是76．40％。     

Forward searching – A complement to keyword- and class-based patentability searches

Patent searches are commonly carried out using keyword- and/or class-based search strategies. The objective of this paper is to present a complementary technique to these traditional strategies – “forward searching”. Starting from a known and relevant document and looking at the documents citing this known document, – the “forward citations” – it is possible, by indexing these citing documents by time, author name or other bibliographical parameters, to retrieve more relevant documents. Forward searching here is further extended using citation analysis to find documents technically related to the known document. The benefits of forward searching and citation analysis, and the most convenient tools currently available online for implementing these techniques, are highlighted. Based on the advantages provided by these online tools, an attempt is made to show which attributes an “ideal” search platform should exhibit.     

LDA算法在图像特征提取中的研究

人体细胞直接关系到身体各项指标的性能,在图像特征的研究过程中,显微细胞图像数据非常复杂,这些数据对下一步的研究将会受到很大的影响。为了提高图像的识别效果,文章以尿液中红细胞图像分析研究为例,采用LDA算法,对细胞图像的主要特征进行变换,提取数据的主要特征,以达到提取图像特征的目的。     

Multivariate image analysis: A review with applications

Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.     

Texture augmented analysis of high resolution satellite imagery in detecting invasive plant species

Abstract

During recent decades, a considerable number of alien species have been brought into Taiwan and have caused significant impacts to local ecosystems and biodiversity. High resolution satellite imagery can provide detailed spatial characteristics over a large area and has a great potential for accurate vegetation mapping. However, most traditional multispectral image classification techniques focus on spectral discrimination of ground objects and may overlook useful spatial information provided by high resolution images. To achieve the best result, analysis of high resolution imagery should also incorporate spatial variations of the data. Therefore, this paper has looked into using a texture augmented procedure to analyze a high resolution satellite (QuickBird) image in order to detect an invasive plant species (Leucaena leucocephala) in southern Taiwan. Samples of primary vegetation covers were selected from the image to determine suitable texture analysis parameters for extracting texture features helpful for classification. Validation with ground truth data showed that the analysis produced high accuracies in detecting the target plant species and overall classification for primary vegetation types within the study site.     

Quantification of flow and retention of oil in compressor discharge pipe

This paper presents a new method to measure the oil retention and oil circulation ratio (OCR) in the compressor discharge pipe based on oil film thickness, oil film average velocity, oil droplet size, oil droplet velocity, and system mass flow rate. Oil flow parameters are quantified based on visualization using high-speed camera and video processing techniques. The estimated oil retention and oil circulation ratio results are compared quantitatively with the results from sampling measurements under different compressor speed and compressor types. The agreement between video results and sampling measurements verify the accuracy of this innovative method, which can also be applied in other annular-mist flow analysis. It also shows that most of the oil exists in film by mass while oil droplets contributes more to the oil mass flow rate because oil droplets travel in a much higher speed.