Genetic programming for feature detection and image segmentation

I.J. Information Technology and Computer Science, 2012, 8, 37-42

Published Online July 2012 in MECS (/)

DOI: 10.5815/ijitcs.2012.08.04

Copyright © 2012 MECS I.J. Information Technology and Computer Science, 2012, 8, 37-42 Offline Handwritten Devanagari Script

Recognition

Ved Prakash Agnihotri

Department of Computer Science and Engineering, LPU Phagwara, India

vedagni86@

Abstract— Handwritten Devanagari script recognition

system using neural network is presented in this paper.

Diagonal based feature extraction is used for extracting

features of the handwritten Devanagari script. After that

these feature of each character image is converted into

chromosome bit string of length 378. More than 1000

sample is used for training and testing purpose in this

proposed work. It is attempted to use the power of

研究方向英文

My research area focuses on the field of artificial intelligence,

specifically in deep learning algorithms and their applications.

Deep learning is a subfield of machine learning that aims to mimic

the functioning of the human brain in order to solve complex

problems. It involves the use of artificial neural networks with

multiple layers to extract high-level features from raw data and

make predictions or decisions. Over the years, deep learning has

revolutionized various areas such as computer vision, natural

language processing, speech recognition, and robotics.

One of my main research interests is the application of deep

learning algorithms in computer vision tasks. Computer vision is

the scientific discipline that deals with how computers can gain

high-level understanding from digital images or videos. Deep

一种广义高斯分布形状参数的快速估计算法

董阳武

【摘 要】广义高斯分布(GGD)在信号处理和图像处理等领域都有着广泛的应用.GGD形状参数的估计通常采用极大似然法和矩估计法.用极大似然法估计形状参数计算复杂、计算量大.用矩估计法的一阶和二阶绝对矩估计可减轻计算的复杂性,但反函数的解析形式很难得到,需要迭代计算,计算效率很低.文中提出了一种基于反函数曲线拟合的GGD形状参数估计方法,在[0.1,2.5]区间与其它现有方法相比具有函数形式简单(仅具有7个系数)、估计精度高、计算简便快速等优点.

【期刊名称】《矿山测量》

【年(卷),期】2012(000)005

【总页数】4页(P45-48)

【关键词】广义高斯分布;形状参数估计;极大似然法;矩估计法

【作 者】董阳武

【作者单位】山西煤炭职业技术学院地测工程系,太原030031

【正文语种】中 文

【中图分类】P208

广义高斯分布(GGD Generalized Gaussian distribution)在信号处理和图像处理等领域都有广泛的应用，如在图像处理中，它被用于对 Discrete cosine

transform(DCT)变换和小波变换系数建模。Müller通过对GGD和Laplacian

distribution(LD)比较发现前者较适合拟合 DCT交流系数［1］;DCT变换、Discrete Wavelet Transform(DWT)变换、Discrete Fourier Transform(DFT)变换的系数都可用GGD来描述［2］;Hernendez等人以GGD为模型提出DCT变换域加嵌入水印的检测方法［3］;Joshi和Fischer采用形状参数为0.5 和 0.6 的

GGD 来拟合 DCT 交流系数［1］。Mallat提出用 GGD 来拟合图像直方图［4］;Aiazzi等用形状参数为［0.4，1］范围内的GGD来拟合高频小波变换系数［1］;Chang等人也以GGD作为图像小波系数的先验模型［5］。在独立成分分析(ICA－Independent component analysis)中，评价函数一般被假设为GGD［6］。所以，对广义高斯分布形状参数的估计显的非常重要。

迭代吉洪诺夫正则化的FCM聚类算法

蒋莉芳;苏一丹;覃华

【摘 要】模糊C均值聚类算法(fuzzy C-means,FCM)存在不适定性问题,数据噪声会引起聚类失真.为此,提出一种迭代Tikhonov正则化模糊C均值聚类算法,对FCM的目标函数引入正则化罚项,推导最优正则化参数的迭代公式,用L曲线法在迭代过程中实现正则化参数的寻优,提高FCM的抗噪声能力,克服不适定问题.在UCI数据集和人工数据集上的实验结果表明,所提算法的聚类精度较传统FCM高,迭代次数少10倍以上,抗噪声能力更强,用迭代Tikhonov正则化克服传统FCM的不适定问题是可行的.%FCM algorithm has the ill posed problem.Regularization

method can improve the distortion of the model solution caused by the

fluctuation of the data.And it can improve the precision and robustness of

FCM through solving the error estimate of solution caused by ill posed

problem.Iterative Tikhonov regularization function was introduced into the

proposed problem (ITR-FCM),and L-curve method was used to select the

optimal regularization parameter iteratively,and the convergence rate of

the algorithm was further improved using the dynamic Tikhonov