复杂环境中车辆检测与跟踪方法的研究

提出了一种在复杂环境中车辆检测与跟踪方法。该方法首先分析车辆的外形特点,确定采用从Blob块提取的几何形状参数作为车辆的特征并用于运动车辆的检测;然后结合Blob分析和CamShift算法提出了车辆跟踪方法。实验结果表明,本文采用的车辆特征参数能够准确地检测车辆,并能准确地区分车辆和行人群体,车辆检测与跟踪准确率分别达到了96.7%和86.7%,证明该方法适用于复杂环境下车辆的检测与跟踪应用要求。     

支持向量机在语音激活检测中的应用研究

提出将支持向量机(SVM)方法应用于语音激活检测(VAD),并验证SVM方法在VAD检测中的有效性。采用了快速训练支持向量机的序列最小最优化方法(SMO)进行训练。提出的基于SVM的VAD方法仍然采用G.729附件B(G.729B)中的VAD方法所采用的特征参数作为分类的特征参数。经过基于SVM的VAD方法与G.729B的VAD方法进行比较,表明SVM方法应用于VAD中是有效的。     

基于GMM-UBM模型的说话人辨识研究

为了进一步提高基于传统的GMM模型的说话人辨识的识别率,引入了GMM-UBM模型,并且在特征提取方面采用多种特征参数组合来代替单一特征参数,以提高有效特征维数来弥补特征样本的不足,同时在说话人辨识的端点检测部分,用基于MFCC相似度和谱熵的端点检测方法来代替传统的基于短时能量和过零点的方法,以解决其对含噪语音检测不准确而影响说话人辨识的问题。实验表明,与传统的GMM模型相比,GMM-UBM模型能够有效地提高说话人辨识的性能,并且使用组合特征参数和利用基于MFCC相似度和谱熵的端点检测方法都可以进一步提高说话人辨识的性能。     

色素性病变皮肤的吸收光谱及色素分布的测定

运用光电无损检测技术，采用皮外反射式测量方法，结合虚拟仪器技术构建的人体皮肤组织吸收光谱检测平台；分析了光在皮肤中的吸收光谱、穿透深度和色素分布三者之间的关系；提出了如何根据所测结果分析色素的分布情况，并建议采用两级分类方法对病例进行分类。根据这种方法对临床测试结果进行了分析，得到了对治疗有价值的参考依据。     

基于人工神经网络与VPMCD的葡萄干等级检测方法研究

为了准确鉴别葡萄干等级,提出一种基于人工神经网络和VPMCD的葡萄干等级检测新方法。以新疆绿无核三个等级的葡萄干作为研究对象,提取颜色、形状的特征参数。采用BP神经网络算法,对比各特征组合对识别率的影响,确定了识别率较高的4个特征参数组合。最后应用VPMCD方法进行样本训练并进行葡萄干等级检测。将提出的方法与SVM、BP神经网络识别结果进行对比分析,结果表明,VPMCD算法识别率达到100%,分级效果明显,运算时间少,识别精度高,为农产品等级检测提供了一个新途径。     

基于分形技术的目标检测算法研究

分形特征参数对于自然背景和人造目标存在着本质差别,充分利用这些差别,能为目标检测提供一种新思路,就分形技术在数字图像处理领域中目标检测方面的应用进行了讨论。分形的应用总是以各种分形参数为中介,有时完全依赖于分形参数,有时将分形参数和其他参数相结合,有时将分形方法和其他方法相结合,在处理的不同阶段分别采用分形方法和其他方法。对几种分形特征参数的估算方法及其在目标检测中的应用进行了研究。     

基于马氏距离的皮肤图像症状识别系统的设计

介绍了一种皮肤图像症状的自动识别系统,利用马氏距离图像识别的算法,经过图像颜色空间转换、自适应法求取阈值进行图像分割、数学形态学后处理和边缘检测、症状区域的轮廓提取和种子填充等图像处理过程,最后提取了皮肤样本的26个特征参数,计算皮肤样本距离黄褐斑、粉刺、黑头、雀斑4种症状的马氏距离,根据马氏距离最小原则对被测皮肤样本进行症状归类.     

基于循环谱理论的弱信号检测及特征参数估计

研究了循环平稳信号的基本特性,通过分析通信系统中典型信号的循环平稳特性,得到了循环平稳的特征参数循环频率和信号特征参数的关系。在此基础上研究了该理论在通信领域的具体应用,提出了应用谱相关理论完成弱信号检测及信号特征参数（信号载频、码元速率）的估计方案,仿真实验结果表明,在-17dB的条件下能够进行信号的无误检测,可在-10dB条件下进行特征参数的无误估计。     

Cynergy双波长激光治疗儿童皮肤血管性病变的临床疗效分析

目的：探讨Cynergy双波长激光系统治疗儿童皮肤血管病变的疗效和不良反应。方法：采用Multiplex技术的Cynergy双波长（595nm和1064nm）激光治疗小儿草莓状血管瘤254例,鲜红斑痣86例,探讨鲜红斑痣疗效与患者年龄、皮损类型和病变部位的关系。结果：Cynergy双波长激光治疗儿童各类皮肤血管性病变的有效率分别为：鲜红斑痣粉红型55．6％,紫红型18．2％,增厚型0％,平均有效率为36％,草莓状血管瘤91．3％;总体不良反应发生率为0．59％。经χ^2检验,鲜红斑痣疗效因患者年龄,皮损类型和部位而异：患者年龄越小,皮损颜色越浅疗效越好,眶周,唇部,额颞部位皮损疗效显著好于四肢：结论：Cynergy双波长激光治疗儿童皮肤血管瘤的临床疗效好,不良反应发生率低;鲜红斑痣的疗效与多种因素相关,宜在婴幼儿早期进行治疗。     

基于多特征联合的太赫兹药品检测方法

以药品为研究对象,利用太赫兹时域光谱系统对3种不同药品进行测量并提取折射率、介电常数和物质因子等多个特征参数,然后联合多个特征参数作为输入,采用后向传播(BP)神经网络、支持向量机(SVM)和学习矢量化(LVQ)3种机器学习方法分别对药品进行多特征联合检测分类识别。实验结果表明,多特征联合检测方法识别准确率能够达到95%以上,有效提高药品的检测分类准确度,可用于药品的检测和分类识别。     

Progressive image transmission using vector quantization on imagesin pyramid form

A progressive image transmission scheme in which vector quantization is applied to images represented by pyramids is proposed. A mean pyramid representation of an image is first built up by forming a sequence of reduced-size images by averaging over blocks of 2×2 pixels. A difference pyramid is then built up by taking the differences between successive levels in the mean pyramid. Progressive transmission is achieved by sending all the nodes in the difference pyramid starting from the top level and ending at the bottom level. The kth approximate image can be formed by adding the information of level k to the previously reproduced (k-1)st approximation. To gain efficiency, vector quantization is applied to the difference pyramid of the image on a level-by-level basis. If the errors due to quantization at level k are properly delivered and included in the next level, k+1, then it is demonstrated that the original image can be reconstructed. An entropy coder is used to encode the final residual error image losslessly, thus ensuring perfect reproduction of the original image. The experiments demonstrate that it is possible to achieve simultaneously lossless and progressive transmission with compression. At the intermediate level, the use of vector quantization results in a coding gain over that obtained using only a Huffman coder. Excellent reproduction is achieved at a bit rate of only 0.06 bits/pixel     

Features of human skin in HSV color space and new recognition parameter

Features of human skin in HSV color space are widely applied in the area of image retrieval based on content. H is selected as the basic recognition parameter because its value has a narrow range for the skin color and can keep stable while the illumination intensity or the curvature of skin surface is changing. Rules of parameters with the change of illumination in HSV color space are studied. It is firstly found that the mean of saturation and value (S V)/2 can keep stable when the illumination intensity is changed or the skin surface is inflected, and (S V)/2 changes with skin color, but the tendency of change is contrary to that of H. Therefore, (S V)/H can be used as a new recognition parameter which can enhance HSV ability to recognize human skin.     

The lossless compression of AVIRIS images by vector quantization

The structure of hyperspectral images reveals spectral responses that would seem ideal candidates for compression by vector quantization. This paper outlines the results of an investigation of lossless vector quantization of 224-band Airborne/Visible Infrared imaging Spectrometer (AVIRIS) images. Various vector formation techniques are identified and suitable quantization parameters are investigated. A new technique, mean-normalized vector quantization (M-NVQ), is proposed which produces compression performances approaching the theoretical minimum compressed image entropy of 5 bits/pixel. Images are compressed from original image entropies of between 8.28 and 10.89 bits/pixel to between 4.83 and 5.90 bits/pixel     

Optimal context quantization in lossless compression of image data sequences

In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context Ct = (Xt-t1,Xt-t2,...,X t-tK) of a source symbol Xt into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and alpha-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.     

皮肤反射式共聚焦显微成像扫描畸变校正

皮肤反射式共聚焦显微镜是一种重要的皮肤影像学诊断工具,其采用共振型振镜扫描成像会带来非线性畸变,为校正这种畸变,提出一种像素值反正弦过采样信号重建的RCM图像畸变校正方法,对间距为20 m的矩形光栅扫描成像实验表明,在校正畸变前光栅图像间距的标准差为7.78 m,图像的畸变率达到38.9%,经过像素值反正弦过采样信号重建后,光栅图像间距的标准差缩小为0.85 m,畸变率缩小到4.2%。结合分辨率板和实际人皮肤成像情况,表明文中提出的畸变校正方法能够较好地校正共振型振镜引起的图像畸变,满足人皮肤实时无创影像学诊断要求。     

夜视融合图像质量客观评价方法

为建立通用、客观的融合图像质量评价方法，在分析图像质量评价与融合图像质量评价关系基础上，给出了图像质量评价与融合图像质量评价的一般表达式。依据信息理论和结构相似度评价方法，对建立的4种客观评价指标，采用4种融合方法获得的36幅融合图像进行了主观评价实验，统计分析结果显示，结合人类视觉系统的客观评价方法优于熵、交互信息量等评价指标，但仍未达到高度的主客观一致性，说明构建通用、高效、主客观一致性好的融合图像质量评价指标存在较大难度，同时对可能存在的原因进行了分析。     

深度学习和迭代量化在图像检索中的应用研究

基于内容的图像检索的关键在于对图像进行特征提取和对特征进行多比特量化编码 。近年来,基于内容的图像检索使用低级可视化特征对图像进行描述,存在“语义鸿沟”问题;其次,传统量化编码使用随机生成的投影矩阵,该矩阵与特征数据无关,因此不能保证量化的精确度。针对目前存在的这些问题,本文结合深度学习思想与迭代量化思想,提出基于卷积神经网络VGG16和迭代量化(Iterative Quantization, ITQ)的图像检索方法。使用在公开数据集上预训练VGG16网络模型,提取基于深度学习的图像特征;使用ITQ方法对哈希哈函数进行训练,不断逼近特征与设定比特数的哈希码之间的量化误差最小值,实现量化误差的最小化;最后使用获得的哈希码进行图像检索。本文使用查全率、查准率和平均精度均值作为检索效果的评价指标,在Caltech256图像库上进行测试。实验结果表明,本文提出的算法在检索优于其他主流图像检索算法。      

基于方向纹理信息的图像融合

鉴于Brushlet分解方向纹理信息的特性,提出一种基于方向纹理信息的图像融合新方法,给出一种基于加权局域能量的融合算子。该方法充分利用已知图像中的高维奇异性信息,有效地保持了各项异性的边缘信息和细节方向纹理信息。对普通图像、航拍图像和医学图像分别进行融合操作,并将融合结果与已知图像的熵和交叉熵作为客观评价指标,与相应小波融合方法进行了比较。仿真结果表明,本文方法融合结果与小波域相应方法比较有明显改善。     

A radius and ulna TW3 bone age assessment system

An end-to-end system to automate the well-known Tanner--Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well-founded feature selection mathematical criteria, based on the ideas of maximizing the classes' separability. Third, bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed Posterior Probability Model Selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple-class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses.