一种改进的基于模板匹配的集装箱字符识别方法

字符识别是计算机模式识别中较为常见的问题,对于印刷体的字符来说,经常采用二值化和模板匹配方法进行识别。在实际应用中,为了提高识别率,减少计算量和运行时间,解决正确识别二值化后的正反不同图象的问题,提出一种改进的自适应的模板匹配方法,理论与实验结果表明具有较高的识别和坚韧性。     

太阳能电池板检测中实时二值化阀值设定的方法

分析了太阳能电池板图像的灰度直方图特征及大津法算法原理,在电池板外形尺寸测量过程中提出将大津法分类思想应用于电池板图像二值化阀值的实时设定。实验表明,这种实时设定阀值的方法克服了光照变化对太阳能电池板测量的影响,从而提高测量的准确性和稳定性。     

Display text segmentation after learning best-fitted OCR binarization parameters

In this paper text segmentation in generic displays is proposed through learning the best binarization values for a commercial optical character recognition (OCR) system. The commercial OCR is briefly introduced as well as the parameters that affect the binarization for improving the classification scores. The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention. The problem to be solved is to recognize text characters that appear on the display, as well as the color of the characters’ foreground and background. The paper introduces how the thresholds are learnt through: (a) selecting lightness or hue component of a color input cell, (b) enhancing the bitmaps’ quality, and (c) calculating the segmentation threshold range for this cell. Then, starting from the threshold ranges learnt at each display cell, the best threshold for each cell is gotten. The input and output data sets for testing the algorithms proposed are described, as well as the analysis of the results obtained.     

Extracting characters of license plates from video sequences

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle, given a sequence of perspective-distortion-corrected license plate images. Different from many existing single-frame approaches, our method simultaneously utilizes spatial and temporal information. We first model the extraction of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing a posteriori probability based on a given prior knowledge and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted and some of our experimental results are presented in the paper. It is shown that our approach provides better performance than other single frame methods. Received: 13 August 1997 / Accepted: 7 October 1997     

一种新的动态二值化算法

本文提出了一种新的动态二值化算法，以实现从灰度点阵图象中提取其对象。通过分析象素和其所在的局部区和灰度值等数字特征，可以判断局部区的内是否存在对象，阈值和判断条件对应。     

基于修正思想的车牌图像定位与二值化处理

车牌图像定位和二值化是车牌识别系统中一个关键的部分。传统的处理方法是使用单一的算法,以便能够满足实时性的要求。但是算法本身的缺陷性使其不能适应一些具有独特特点的车牌,不能正确地识别出结果。基于提高算法的适应范围考虑,在满足识别速率要求的前提下,该文提出了基于修正思想的“去粗取精”处理模式,可以大大改善定位和二值化处理的效果,为后续的处理提供了一个良好效果的输入。     

基于RGB颜色特征的印鉴图像预处理

针对票据印鉴在RGB颜色空间分布的特点,通过算法将红色印鉴从杂乱的背景和噪声中分离出来,得到二值化的印鉴图像,再通过去噪得到较好的预处理印鉴图像。实验证明,该方法的计算速度较快,并能在一定程度上恢复出被背景覆盖的印鉴部分,使得提取的印章有较理想的视觉效果。     

An analysis of the transition proportion for binarization in handwritten historical documents

In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO?s benchmarks.     

Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images

In this paper we present an algorithm to segment the nuclei of neuronal cells in confocal microscopy images, a key technical problem in many experimental studies in the field of neuroscience. We describe the whole procedure, from the original images to the segmented individual nuclei, paying particular attention to the binarization of the images, which is not straightforward due to the technical difficulties related to the visualization of nuclei as individual objects and incomplete and irregular staining. We have focused on the division of clusters of nuclei that appear frequently in these images. Thus we have developed a clump-splitting algorithm to separate touching or overlapping nuclei allowing us to accurate account for both the number and size of the nuclei. The results presented in the paper show that the proposed algorithm performs well on different sets of images from different layers of the cerebral cortex.