Extraction and analysis of forensic document examiner features used for writer identification

In this paper we present a study of structural features of handwriting extracted from three characters “d”, “y”, and “f” and grapheme “th”. The features used are based on the standard features used by forensic document examiners. The process of feature extraction is presented along with the results. Analysis of the usefulness of features was conducted via searching the optimal feature sets using the wrapper method. A neural network was used as a classifier and a genetic algorithm was used to search for optimal feature sets. It is shown that most of the structural micro features studied, do possess discriminative power, which justifies their use in forensic analysis of handwriting. The results also show that the grapheme possessed significantly higher discriminating power than any of the three single characters studied, which supports the opinion that a character form is affected by its adjacent characters.     

基于特征提取和Delta编码的超声波数据压缩方法

为了保存超声检测过程中产生的大量数据,通过对超声波数据提取波形沿交点等特征,并在此基础上进行Delta编码,将特征提取与Delta编码进行结合,建立了一份十进制二进制编码表,使编码后所得的数据压缩比提高20%~40%。特征提取和Delta编码可以互补提高压缩比,并且可以根据重构精度的要求动态改变压缩比。最后用实例说明了这种压缩方法的压缩效果和还原效果。     

基于混合码本与因子分析的文本独立笔迹鉴别

针对已有的笔迹鉴别方法对笔迹版式的要求比较严格、训练过程耗时、对内容不受限制的小样本数据情况下鉴别性能较低等问题, 提出了基于混合码本与因子分析的文本独立笔迹鉴别算法. 该算法提取写作时常用的子图像, 并用描述符标注“代码”建立“码本”. 在特征提取层, 分别采用加权的方向指数直方图法和距离变换法, 对于具有相同描述符的“代码”计算特征距离. 把影响特征距离的因素分为书写因子和字符因子, 对码本中的每个书写模式进行双因子方差分析. 在IAM和Firemaker这两个标准数据集上的实验结果证明, 相比目前国内外的先进已有方法, 本文提出的算法在精度和速度方面有一定的优势, 具有一定的推广价值, 适合处理多语种的笔迹鉴别问题.     

基于优化分割重建图像的文本无关笔迹鉴定

笔迹鉴定的主要过程首先是系统把手写的笔迹文字通过扫描仪输入计算机，然后对原始笔迹的图像进行预处理。在预处理阶段，本文提出了优化分割重建图像的归一化预处理方法，在参数提取阶段，本文采用多通道二维G2bro滤波器，通过计算4个方向每个方向4个频率来提取的笔迹特征。本文对10个人任意书写的笔迹进行实验，鉴别正确率得到较好的提高。     

基于多通道分解与匹配的笔迹鉴别研究

笔迹鉴别是通过分析手写字符的书写风格来判断书写人身份的一门技术.笔迹鉴别的关键步骤是提取反映书写风格的笔迹特征.笔迹特征包括笔划位置、方向、搭配关系等,它们可以通过图像多通道分解提取和表达出来.本文提出一种用于笔迹鉴别的二值图像多通道分解方法,利用字符的笔划方向性先进行方向分解,然后对每个方向的子图像进行频带分解.用分解后的采样信号值作为笔迹特征,用特征匹配方法进行书写人识别,得到了很好的实验结果.     

笔迹鉴别的字符予处理与匹配

笔迹鉴别多用匹配方法比较字并的书写风格, 而字符困像的预处理和归一化对匹配是昨常重要的本文介绍笔迹鉴别的字符图像预处理和一种形状匹配方法。预处理主要介绍二值图像的噪声消除和归一化方法。嗓声消除的方法是平滑、轮廓跟踪和填充为保持字符中的书写特征, 点阵的归一化是线性的, 但字符位五和尺度的确定昨常重要。本文给出了三种归一化方法四边定界法、重心对准法和单边定界法, 并在此基拙上用图像匹配方法进行书写人识别的实验。匹配方法是通过距离变换快速实现的。实验结果表明, 重心对·准归一化最适合于笔迹鉴别问题, 距离变换匹配得到的识别率也比较令人满意     

Texture as the basis for individual tree identification

Recognizing plants from imagery is a complex task due to their irregular nature. In this research, three tree species, Japanese yew (Taxus cuspidata Sieb. & Zucc.), Hicks yew (Taxus x media), and eastern white pine (Pinus strobus L.), were identified using their textural properties. First, the plants were separated from their backgrounds in digital images based on a combination of textural features. Textural feature values for energy, local homogeneity, and inertia were derived from the co-occurrence matrix and differed significantly between the trees and their backgrounds. Subsequently, these features were used to construct the feature space where the nearest-neighbor method was applied to discriminate trees from their backgrounds. The recognition rates for Japanese yew, Hicks yew, and eastern white pine were 87%, 93%, and 93%, respectively. The study demonstrates that the texture features selected and the methods employed satisfactorily separated the trees from their relatively complex backgrounds and effectively differentiated between the three species. This research can lead to potentially useful applications in forestry and related disciplines.     

综合利用目标区域颜色纹理特征的彩色图像检索

基于内容的图像检索是当前图像数据库领域的一个研究热点。文章提出了一种基于目标区域的多特征彩色图像检索算法。首先获取彩色图像HSI色彩空间的亮度分量灰度图像,再计算得到亮度分量灰度图像的二值边缘图像,并根据边缘图像的连通性提取彩色图像的目标区域。然后抽取目标区域的颜色特征和纹理特征,并分别对两个特征的相似距离进行归一化。最后综合利用这两个特征共同进行检索。实验结果表明,该文的算法具有良好的性能,在检索准确率上获得了较好的效果。     

基于Delta算子的统一代数Lyapunov方程解的上下界

基于Delta算子描述,统一研究了连续代数Lyapunov方程(CALE)和离散代数Lyapunov方程(DALE)的定界估计问题.采用矩阵不等式方法,给出了统一的代数Lyapunov方程(UALE)解矩阵的上下界估计,在极限情形下可分别得到CALE和DALE的估计结果.计算实例表明了本文方法的有效性.     

Image identification and segmentation according to random-field models

Some images have texture variations which are most easily characterized by random-field models. The identification of the model which evoked a given such image is described. The method involves a readily computable criterion which is a refinement of those previously used for image identification. The general method is also shown to be suitable for the more specialized problem of image segmentation, a preprocessing operation fundamental to many techniques in artificial intelligence.     

Closed-loop subspace identification using the parity space

It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting.     

基于纹理特征的指纹识别算法

基于纹理特征的指纹识别方法,具有计算量小的特点。本文对该指纹识别算法进行了系统研究,提出了一套新颖的方向图修正算法,对于噪声相对较小的情况具有良好的效果。本文对多窗口法求块方向图给出了减少运算量的方法;在方向滤波器设计方面,给出了一个方向滤波器的闭合等式,可以简化方向滤波器设计,大大减少运算量,并取得了较好的增强效果;在中心点求取方面,提出了一种新的粗搜索算法,可以减少计算量,加快中心区域搜索速度。     

基于RS和GIS的黄河三角洲主要人工地物时空特征分析

油井、风机和道路是黄河三角洲区域的主要人工地物,明确其时空特征对保障黄河三角洲的生态安全具有重要意义。利用Landsat、SPOT、高分二号等中、高分辨率卫星影像,提取并建立2000年和2015年黄河三角洲主要人工地物分布数据集,使用核密度法、平均最近邻法、加权分析法、克里金插值法和空间自相关分析法分析2000～2015年黄河三角洲人工地物的时空特征。结果表明：(1) 2015年风机空间分布具有不均衡性,呈现两大核心区为主、3个小集聚区为辅的空间分布格局,总体呈西北—东南走向,空间集聚特征明显;(2) 2000年和2015年油井空间分布具有不均衡性且存在时间变异特征,2000年油井呈现两大核心为主,团块成片分布为辅的空间分布格局,2015年油井呈现三大核心区为主、核心区周围油井呈鞍状分布的空间格局特征;(3) 2000～2015年黄河三角洲路网密度不断提高,空间集聚特征明显,路网密度高值主要分布在黄河三角洲中部地区,并呈现向西部和南部扩散趋势。总的来说,黄河三角洲地区人工地物数量大、分布范围广、增长速度快,对自然保护区的生态安全造成了巨大威胁。通过识别和分析黄河三角洲人工地物及其时空特...     

Reducing the Dimensions of Texture Features for Image Retrieval Using Multi-layer Neural Networks

This paper presents neural network-based dimension reduction of texture features in content-based image retrieval. In particular, we highlight the usefulness of hetero-associative neural networks to this task, and also propose a scheme to combine the hetero-associative and auto-associative functions. A multichannel Gabor-filtering approach is used to derive 30-dimensional texture features from a set of homogeneous texture images. Multi-layer feedforward neural networks are then trained to reduce the number of feature dimensions. Our results show that the methods lead to a reduction of up to 30% while keeping or even improving the performance of similarity ranking. This has the benefit of alleviating the ill-effects of the high dimensionality of features in current image indexing methods and resulting in significant speeding up retrieval rates. Results using principal component analysis are also provided for comparison. Receiveed: 6 July 1998?,Received in revised form: 6 November 1998?Accepted: 15 December 1998     

递推最小二乘参数辨识与仿真实例

介绍了基于递推最小二乘法进行系统辨识的基本原理,对给定的实际输入输出数据运用MATLAB的M语言编写递推最小二乘算法,最后给出相应的仿真结果和分析,并对得到的模型进行验证。     

Automatic identification of butterfly species based on local binary patterns and artificial neural network

Butterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification.     

Novel fractal image encoding algorithm using normalized one-norm and kick-out condition

For fractal image encoding, based on a special measure called the one-norm of normalized block, this paper presents a novel kick-out method to discard impossible domain blocks in early stage for the current range block. It leads to speed up the encoding time. Since our proposed kick-out method is based on Jacquin’s full search method, both methods need to search the whole image and the decoded image quality are the same. Based on five typical testing images, our proposed method has 22% execution time improvement ratio in average when compared with Jacquin’s full search method. Combining our proposed method with Truong et al.’s DCT inner product method, Lai et al.’s kick-out method, or both methods, the encoding-time performance can be improved further.