分割图形粘连字符的图元屏蔽技术研究

在工程图纸计算机处理过程中,分割字符与图形是非常重要的步骤,但字符与图形粘连的问题很难处理。本文在分析了几种字符与图形分割技术的原理和实现方法的基础上,提出一种分割与图形粘连字符的方法——图元屏蔽技术。试验结果表明,这种方法对于分割与正交方向线段粘连的字符十分有效,特别适用于处理字符串中全部与图形粘连或多个粘连的现象。     

Design of cursive characters using arm dynamics as a generation mechanism

This paper considers the problem of reconstructing characters and words. As the basic tool for generating characters, we employ the 'dynamic font' method, where designing characters and words reduces to the design of three-dimensional motions of a virtual writing pen. Assuming that some standard characters and words are already designed, we propose a method for transforming their typefaces to the omitted running styles as seen in Japanese calligraphy. Since this is viewed as the problem of redesigning motions of the pen, we introduce a robotic arm with the pen in its hand and plan the hand motion by employing a minimum torque change model. Using a velocity linearized model of arm dynamics, we solve the associated optimization problem completely. The solution is given in compact form and the computational process is straightforward as well as stable numerically. Design examples show the usefulness of the proposed method.     

On-line recognition of cursive Korean characters using DP matching and fuzzy concept

This paper proposes an on-line recognition method of cursive Korean characters based on dynamic programming (DP) matching and fuzzy concept. The proposed algorithm, invariant to rotation and size, reduces greatly the computational requirement of DP matching by matching phonemes rather than character patterns, where the angle difference and the ratio of lengths between input and reference patterns are adopted as matching features. Correct matching of poorly-written cursive characters becomes possible by introducing the fuzzy concept in representing phoneme features and the positional relationships between adjacent phonemes. Computer simulation results show the effectiveness of the proposed algorithm.     

基于HHT的脱机手写体汉字特征提取

提出了一种基于数学形态学方向笔画提取的希尔伯特一黄变换(HHT)方法,对脱机手写体汉字进行特征提取.通过对HHT得到的Hilbert谱和边际谱的分析,构成4维结构特征和32维统计特征,并对其进行特征融合后得到36维特征向量作为最终的识别特征.试验结果表明其识别率比单独使用Gabor变换、小波变换等方法的识别率高.在识别速度上虽然比矩变换、数学形态学等方法慢,但是比Gabor变换的速度有明显提高,比多方法特征融合的方法在速度上有一定提高.该研究表明HHT作为一种新的信号分析方法,可以被有效地运用于提取汉字图像的特征.     

Methods and theory for off-line machine learning

Many problems in machine learning can be abstracted to the sequential design task of finding the minimum of an unknown erratic and possibly discontinuous function on the basis of noisy measurements. In the present work, it is presumed that there is no penalty for bad choices during the experimental stage, and at some time, not known to the decision maker, or under his control, the experimentation will be terminated, and the decision maker will need to specify the point considered best, on the basis of the experimentation. In this paper, we seek the best trade-off between: i) acquiring new test points, and ii) retesting at points previously selected so as to improve the estimates of relative performance. The algorithm is shown to achieve a performance standard described herein. This decision setting would seem natural for function minimization in a simulation contest or for tuning up a production process prior to putting it into service     

A review of thresholding strategies applied to human chromosome segmentation

Karyotype analysis is a widespread procedure in cytogenetics to assess the presence of genetic defects by the visualization of the structure of chromosomes. The procedure is lengthy and repetitive and an effective automatic analysis would greatly help the cytogeneticist routine work. Still, automatic segmentation and the full disentangling of chromosomes are open issues. The first step in every automatic procedure is the thresholding step, which detect blobs that represent either single chromosomes or clusters of chromosomes. The better the thresholding step, the easier is the subsequent disentanglement of chromosome clusters into single entities. We implemented eleven thresholding methods, i.e. the ones that appear in the literature as the best performers, and compared their performance in segmenting chromosomes and chromosome clusters in cytogenetic Q-band images. The images are affected by the presence of hyper- or hypo-fluorescent regions and by a contrast variability between the stained chromosomes and the background. A thorough analysis of the results highlights that, although every single algorithm shows peculiar strong/weak points, Adaptive Threshold and Region Based Level Set have the overall best performance. In order to provide the scientific community with a public dataset, the data and manual segmentation used in this paper are available for public download at http://bioimlab.dei.unipd.it.     

基于FSVM脱机手写体汉字分类识别研究

针对脱机手写体汉字特点,给出一种采用模糊支持向量机粗分类的方法。根据小波分解像素密度特征,利用模糊支持向量机对汉字进行粗分类。细分类识别提取外围特征,同时融合小波多网格特征,采用一对多算法进行细识别。仿真实验表明,该方法有较高识别率。     

基于主曲线算法的手写字符特征分析与提取

模式特征的提取与选择是提高手写体字符识别率的关键因素。主曲线是主成分分析的非线性推广,它是通过数据分布“中间”并满足“自相合”的光滑曲线,能够很好地描述数据分布的结构特征。利用软K段主曲线算法提取训练数据的特征,在分析手写体字符结构特点的基础上,选出手写体字符识别所使用的粗分类与细分类特征,利用这些分类特征对手写字符进行识别。该方法在CEDAR手写体数字和字符数据库上的实验表明：选取的分类特征能够有效区分相似的手写体字符,提高手写字符的识别率,为脱机手写字符识别研究提供了一种新的方法。     

基于部首合并的自适应文字切分制作笔迹纹理

目前笔迹鉴别预处理阶段的纹理图像制作通常采用压缩行距及字间距的方式,由于样本中存在随机出现的过长笔画,导致制作出的纹理不均匀,以致影响最终的笔迹鉴别结果。提出了一种基于偏旁部首合并的自适应文字切分算法,实验表明采用此算法能够对粘连的文字进行有效切分并且能够确保分字的正确,从而制作出的纹理更加均匀,能够有效提高笔迹鉴别的效果。     

Deep learning techniques for tumor segmentation: a review

Recently, deep learning, especially convolutional neural networks, has achieved the remarkable results in natural image classification and segmentation. At the same time, in the field of medical image segmentation, researchers use deep learning techniques for tasks such as tumor segmentation, cell segmentation, and organ segmentation. Automatic tumor segmentation plays an important role in radiotherapy and clinical practice and is the basis for the implementation of follow-up treatment programs. This paper reviews the tumor segmentation methods based on deep learning in recent years. We first introduce the common medical image types and the evaluation criteria of segmentation results in tumor segmentation. Then, we review the tumor segmentation methods based on deep learning from technique view and tumor view, respectively. The technique view reviews the researches from the architecture of the deep learning and the tumor view reviews from the type of tumors.

     

弱小目标分割算子耦合特征匹配的杆菌识别

针对复杂背景下弱小目标识别困难,实时性要求高等难题,提出了弱小目标分割算子;并引入特征匹配,设计了二者相融合的自动图像识别机制。该方案基于H－S分割与图像融合,再结合形态学滤波与特征匹配,解决弱小目标难识别的问题。该方案计算量小并基于经过汇编优化的OpenCV实现,解决实时性要求高等难题。实验结果表明,相对于传统方法,该方案应用在自动图像识别上可以达到较好的效果,在工程上应用是可行的。     

U-Net网络医学图像分割应用综述

病灶精确分割对患者病情评估和治疗方案制定有重要意义,由于医学图像中病灶与周围组织的对比度低,同一疾病病灶边缘和形状存在很大差异,从而增加了分割难度。U-Net是近些年深度学习研究中的热点,为医生提供了一致性的量化病灶方法,一定程度上提高了分割性能,广泛应用于医学图像语义分割领域。本文对U-Net网络进行全面综述。阐述U-Net网络的基本结构和工作原理;从编码器个数、多个U-Net级联、与U-Net结合的其他模型以及3D U-Net等方面对U-Net网络模型的改进进行总结;从卷积操作、下采样操作、上采样操作、跳跃连接、模型优化策略和数据增强等方面对U-Net网络结构改进进行总结;从残差思想、密集思想、注意力机制和多机制组合等方面对U-Net的改进机制进行总结;对U-Net网络未来的发展方向进行展望。本文对U-Net网络的原理、结构和模型进行详细总结,对U-Net网络的发展具有一定积极意义。     

铸件DR图像工件号的增强和分割

铸件DR(DigitalRadiography,数字化X射线照相)图像中工件号的识别对提高检测信息录入的自动化程度具有重要意义。由于受铸件厚薄不均等因素的影响,某些铸件DR图像字符区域存在字符与背景区分不明显等问题。要正确识别出铸件的工件号,必须对图像进行处理。采用加性模型消除字符与背景区分不明显的影响,gamma校正调整图像灰度范围,可增强对比度,获得适宜于二值化的增强图像。改进基于二次边缘提取的二值化算法,用于对铸件DR字符图像的二值化,可以减少笔画断裂问题,得到良好的二值化字符图像。字符分割采用小波变换提取图像列和的突变信息,从而确定字符间隔,能够完整地分割出每个字符。对实际的铸件DR图像进行实验表明,研究的方法获得了良好的效果,为铸件DR图像的工件号自动识别做好了准备。     

Mossar: motion segmentation by using splitting and remerging strategies

This paper presents a novel approach for motion segmentation by using strategies of splitting and remerging. The presented approach, Mossar, hybridizes two existing ones to obtain their potential advantages while covering weaknesses: (1) velocity-based, one of the most widely used approaches that has fairly low accuracy but provides computational simplicity and (2) graph-based, a state-of-the-art approach that provides outstanding accuracy, yet bears high computational complexity and a burden in setting of thresholds. An initial set of key frames is generated by a velocity-based splitting process and then fed into a graph-based remerging process for refinement. We present mechanisms that improve key-frames capturing in the velocity-based approach as well as details on how the graph-based approach is modified and later applied to remerging. The proposed approach also allows users to interactively add or reduce the number of key frames to control segmentation hierarchy without the need to change threshold values and re-run segmentation, as usually done in existing approaches. Our experimental results show that the presented hybrid approach, compared to both velocity-based and graph-based, demonstrates superior performance in terms of accuracy and in comparison to graph-based, our approach has not only less complexity but also a lesser number of thresholds, the values of which can be much more simply determined.     

标牌粘连字符自适应定位分割重建与识别

目的 针对仪表、电梯等标牌上一些字符间距较小,传统分割方法分割不准确,字符识别率不高的问题,提出了一种标牌粘连字符自适应定位分割重建识别算法。方法 首先对标牌图像进行中值滤波、二值化等预处理;其次运用数学形态学方法对预处理后的图像进行开运算及腐蚀,将字符间一些无用的信息去掉,增大字符间距;继而通过形心算法找出每个字符的几何中心,并通过Sobel边缘检测算子根据几何中心获取每个字符边框,建立ROI（region of interest）,再返回标牌原图利用已经建立的ROI从中分割字符,依据国家字符间距相关标准,在分割的每个字符后加一定像素宽的矩形间隔条后重建字符图像,再进行OCR（optical character recognition）字符识别。结果 经过对993块标牌进行字符识别实验,算法的识别率达到95.7%。结论 实验结果表明本文算法是对标牌字符识别的一种有效算法。     

Online and off-line handwriting recognition: a comprehensive survey

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered     

A survey of methods and strategies in character segmentation

Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the “classical” approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called “dissection.” The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described