基于小波变换的分形图像编码压缩算法

有效的压缩算法可以给数据的储存和传输带来极大的方便。针对现有压缩算法存在的不足,定义图像子块的九块和特征,并提出结合小波和分形编码优势的有损压缩算法。该算法将图像进行二级小波分解,得到7个子带;保留低频子带的小波系数,并对其余子带采用基于图像块九块和特征的分形编码。新定义的九块和算法,把全搜索问题变为在九块和意义下的近邻搜索问题,缩短了搜索范围。实验结果表明,与同类算法相比,该算法不仅提高了重构图像的质量,还缩短了编解码时间。     

基于Mandelbrot集和Logistic映射的分形图像压缩编码

改变传统的一幅图像对应一个压缩字典、一幅图像固定一张量化表的分形图像压缩方法,提出基于M集和Logistic映射的分形图像压缩编码算法.采用函数f(z)=z2 c,生成M集曲线,使用Logistic混沌映射生成的量化表量化M集曲线,生成图像块,构成丰富的压缩字典.编码时将量化后的M集图像块与压缩字典中的图像块进行匹配,选出满足条件的图像块,然后对该图像块进行编码;解码时读取压缩字典,重建图像.该算法生成了丰富的压缩字典,解码图像质量高,并且比传统分形图像压缩算法压缩比高.     

基于遗传分形编码的嵌入式小波图像编码算法

在分析小波变换与分形编码特点的基础上,提出了一种新的基于遗传分形编码的嵌入式小波图像编码算法。根据图像进行小波变换分解为不同空间频带上的子图像这一特性,对其低频子带进行基于遗传算法的分形编码,利用遗传算法的快速全局搜索能力提高分形编码中值域块与定义域块的匹配搜索速度;对其高频子带部分进行阈值去噪处理后进行SPIHT编码,两部分同时进行,提高图像的压缩效率。实验结果表明：同传统的分形编码和SPIHT编码压缩方法相比,该算法在图像的重构质量方面有所提高,尤其是在中低码率下PSNR(峰值信噪比)得到了较大提高,并且算法执行时间明显减少。     

一种基于小波域的分形图像编码改进算法

提出了一种基于小波域的分形图像编码改进算法。该算法首先将图像分解到小波域,然后根据各子图像所包含能量的大小和所代表的方向等信息,采用不同大小、形状和类别的图像块及相似块,进行分形编码。在编码过程中按照零树结构在同方向不同分辨率的各个子带图像上确定要预测的图像块,同时在同方向低一级分辨率的子带图像上寻找与其最佳分形匹配的相似块,并由各级相似块构成预测树。实验证明,这种改进算法能够大大提高分形编码的速度,并取得较高的压缩比。     

基于像素采样的分形图像编码算法

分形图像编码是一种基于自然图像局部自相似性的有效压缩算法技术．但是,基本的分形编码算法是耗时的,由于在基本编码算法中值域块要在庞大的定义域块库中搜索最佳的匹配块．为了减少编码时间,该文提出了基于像素采样的分形编码方案．该方案既不需要复杂的理论分析,也不需要改变现有的分形编码、解码过程,因此能够以直接的方式引进其他的块速的编码算法．计算机仿真显示,在PSNR降低的情况下,编码的匹配搜索时间大幅度减少,同时解码图像的主观质量并没有很大程度上明显降低．     

半叉迹特征的快速分形图像编码

传统的分形图像编码时间过长,限制了它的应用。为了加快编码速度,提出基于半叉迹特征的快速分形编码算法,该算法主要包括：定义子块半叉迹特征,导出子块的均方根误差与子块半叉迹特征之间的关系。实验结果表明：该算法较基本分析编码算法,在解码图像PSNR平均提高约0.63 dB的情况下,平均加快编码速度55倍;较文献[10]提出的主对角和算法和文献[11]提出的叉迹算法,在编码时间不变的情况下,改善了编码性能,提高了解码图像质量;基于子块特征的快速编码算法,其编码性能与图像的复杂程度有关,细节信息越丰富的图像,编码性能越差。     

基于图像子块特征的快速分形图像编码算法

根据图像子块的像素分布特征,提出了一种基于方差和DCT变换的混合快速分形图像编码算法,并在此基础上引入了平滑块的概念.该算法在大幅度提高分形图像编码速度的同时,很好地改善了压缩率和解码图像的质量.实验表明该方法具有优良的性能,在编码时间优于方差快速编码方法的前提下,解码图像的质量和压缩率可以好于基本分形图像编码算法.     

基于云模型编码算法的图像消噪研究

为了提高图像消噪的质量,采用云模型编码算法。首先通过正态云发生器产生云 滴;接着正云滴映射为图像中大于平均灰度的像素,负云滴映射为图像中小于平均灰度的像素, 不同云滴的编码则表示不同的像素的灰度特征;然后根据编码规则将图像子块分为平滑块和非 平滑块,平滑块区域保持其增强质量;最后给出了基于云模型编码算法的图像消噪模型和算法 流程。实验仿真显示该文算法对图像消噪效果最好,能以较大概率找到全局最优解。     

基于像素分布与三角形分割的快速分形图像压缩算法

为解决传统分形图像压缩算法中编码速度慢的问题,提出了一种基于像素分布和等腰直角三角形分割的快速分形图像压缩方法。利用离散质点系质心的唯一性以及质心位置不变的特点,实现了定义域块与值域块的匹配。对原始图像采用等腰直角三角形的划分方式,减少了定义域块的数量和搜索匹配块的运算次数,提高了编码效率。实验结果表明,该方法在保证图像解码质量的前提下,编码速度是传统分形图像压缩编码方法的3倍。     

以分形图形为载体的信息隐藏算法

针对现有信息隐藏提取得不到原始载体图像使提取困难、隐藏容量高又易被隐密分析攻破的问题,提出以分形图形为载体的信息隐藏方法。首先,利用数学仿射变换及分形迭代函数系统生成黑白的分形图形（BWFG）;然后,根据坐标变换的思想,将分形图形转化为黑白像素图像（BWPI）;最后,将像素图像分成不重叠的小块,通过调整小块内黑白像素的位置来隐藏秘密信息,形成含密图像。知道仿射变换参数及迭代次数的秘密信息接收方,首先生成原始载体分形图像,然后通过对比两幅图像相应小块黑白像素差异,即可提取隐藏的秘密信息。理论分析和仿真实验表明,与频率域信息隐藏算法相比,提出的算法隐蔽性强、隐藏容量高,能抵抗基于图像特征和变换域系数变化的隐密分析。     

分形编码在人脸识别中的应用

利用人脸固有的对称特性,提出了基于对称性与方差的快速分形人脸图象压缩算法.对于一值域块,与其匹配的定义域块被限定在其对称区域中,从而可加快编码速度.本文分析了分形码的特点,定义了图象间分形码距离,进而提出了基于分形码的人脸识别算法,并对该算法的时间复杂性作了分析.通过在ORL和YALE人脸库上的实验结果表明,本文提出的识别算法的识别率分别可达到88%和92%,与本征脸方法基本持平,比直接采用基于方差的分形编码方法要高,且本文算法在人脸有装饰物情况下具有一定的稳定性,实验还对识别算法中参数的取值作了分析.     

Manufacturing features recognition using backpropagation neural networks

A backpropagation neural network (BPN) is applied to the problem of feature recognition from a boundary representation (B-rep) solid model to facilitate process planning of manufactured products. It is based on the use of the face complexity code to represent the features and a neural network for the analysis of the recognition. The face complexity code is a measure of the face complexity of a feature based on the convexity or concavity of the surrounding geometry. The codes for various features are fed to the network for analysis. A backpropagation network is implemented for recognition of features and tested on published results to measure its performance. Any two or more features having significant differences in face complexity codes were used as exemplars for training the network. A new feature presented to the network is associated with one of the existing clusters, if they are similar, or the network creates a new cluster, if otherwise. Experimental results show that the network was consistent in recognizing features, hence is appropriate for application to the problem of feature recognition in automated manufacturing environment.     

基于Android系统的人脸识别门禁系统的设计

智能门禁系统是安全防护系统中重要的组成部分 ,本文采用人脸识别技术与手机端身份识别技术,设计了一种基于Android的人脸识别门禁系统,实现了用户在Android手机端进行人脸识别身份验证获取服务器发来的二维码作为开门“软钥匙”,用获取到的二维码去智能门禁终端进行扫码开门的功能。在人脸识别方面,采用了Adaboost人脸检测算法和PCA人脸识别算法,并结合OpenCV实现了门禁系统的人脸识别。测试结果表明：本系统具有良好的易用性、安全性,并且获得较高的人脸识别率和识别速度,从而弥补传统门禁的缺陷与不足。     

The fractal neighbor distance measure

Fractal image coding has been used successfully to compress and segment images, and more recently, utilized in a new distance measure to recognize objects. This paper discusses how the process of decoding a set of region-based contractive transformations has invariance properties that can be advantageous in object recognition. We will show that the recognition ability of the proposed fractal neighbor classifier (FNC), utilizing the fractal neighbor distance (FND) measure is a function of the contrast scaling factor and the illumination shift factor. Our investigation of the FND required accurate control over the convergence of a fractal decoding process. Convergence can be determined by examining the contractivity and eventual contractivity factors. We have derived theorems that allow these two factors to be calculated for a general class of fractal codes consisting of affine transformations with integral geometric scaling. Experiments were performed that verified our ability to control and modify these convergence properties. Furthermore, experiments on human face recognition revealed that the performance of the FNC improved through the use of eventual convergence and the imposition of limits on the illumination shift factor.     

New method for feature extraction based on fractal behavior

In this paper, a novel approach to feature extraction based on fractal theory is presented as a powerful technique in pattern recognition. This paper presents a new fractal feature that can be applied to extract the feature of two-dimensional objects. It is constructed by a hybrid feature extraction combining wavelet analysis, central projection transformation and fractal theory. New fractal feature and fractal signatures are reported. A multiresolution family of the wavelets is also used to compute information conserving micro-features. We employed a central projection method to reduce the dimensionality of the original input pattern. A wavelet transformation technique to transform the derived pattern into a set of sub-patterns. Its fractal dimension can readily be computed, and to use the fractal dimension as the feature vectors. Moreover, a modified fractal signature is also used to distinguish the distinct handwritten signatures. We expect that the proposed fractal method can also be used for improving the extraction and classification of features in pattern recognition.     

基于完整LBP特征的人脸识别*

提出一种基于完整局部二值模式(CLBP)进行人脸识别的方法,CLBP算子包括三个部分:中心像素的LBP(CLBP_C)、符号部分的LBP(CLBP_S)、数值部分的LBP(CLBP_M)。该方法首先采用CLBP算子提取人脸灰度图像的直方图;然后融合成CLBP直方图,进行直方图相似性比较;最后根据最近邻原则进行识别。在ORL和YALE标准人脸数据库上的实验表明,该方法得到的结果比LBP效果更好,鲁棒性更高。     

CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

The development of web cameras and smart phones is mature, and more and more facial recognition-related applications are implemented on embedded systems. The demand for real-time face recognition on embedded systems is also increasing. In order to improve the accuracy of face recognition, most of the modern face recognition systems consist of multiple deep neural network models for recognition. However, in an embedded system, integrating these complex neural network models and execute simultaneously is not easy to achieve the goal of real-time recognition of human faces and their identities. In view of this, this study proposes a new frame analysis mechanism, continuous frames skipping mechanism (CFSM), which can analyze the frame in real time to determine whether it is necessary to perform face recognition on the current frame. Through the analysis of CFSM, the frames that do not need to be re-recognized for face are omitted. In this way, the workload of the face recognition system will be greatly reduced to achieve the goal of real-time face recognition in the embedded system. The experimental results show that the proposed CFSM mechanism can greatly increase the speed of face recognition in the video on the embedded system, achieving the goal of real-time face recognition.

     

基于BEMD和分形维数的人脸识别方法

提出一种新的人脸图像特征提取方法,即利用二维经验模态分解方法（BEMD）结合分形维数（Fractal dimension）进行特征量提取,将提取得到的特征量用于人脸识别。该方法将图像通过BEMD算法分解为不同的二维固有模态分量（BIMF）,然后将得到的BIMF图像进行分块得到BIMF子区域,对每一个BIMF子区域进行分形盒维数估计,采用BP神经网络作为分类器。实验选用ORL人脸数据库,实验结果表明,用该算法进行特征量提取的人脸识别方法具有理想的识别效果并提高识别系统性能。