基于改进主动表现模型的人脸面部特征定位

采用主成分分析(PCA)作为统计分析方法的主动表现模型(AAM)是建立二维可形变模型的有效方法.提出一种将改进的AAM用于人脸面部特征定位的新方法,并与传统AAM进行比较,实验证明此方法要优于传统AAM.     

彩色电视机用有源天线分配器的设计

随着数字信号技术的不断发展,数字电视成为未来电视的主流,但在模/数转换相当长时间内,会存在模数信号共存的情况,有源天线分配器根据消费者的使用情况对两种信号起转换作用。同时有源天线分配器的信号平坦度、隔离特性对信号传输有很大影响。通过分析该分配器的产品结构和工作原理,介绍了其设计目标、设计方法、测试结果及对多功能彩色电视机的影响。     

基于高斯混合模型的纹理图像的分割

文章提出了使用高斯混合模型对纹理图像分割的方法。该模型考虑到图像纹理的特点，使用二状态的高斯混合分布来对图像各纹理类像素的分布进行拟和。首先对待分割的各类像素寻找与之匹配的最佳模型参数，然后再进行分割。最后，对分割结果进行多尺度综合，得到了各尺度的分割结果。实验表明该算法不仅能够较精确地定位各纹理区域，而且还有运算速度快的优点。     

基于小波概率神经网络的彩色纹理识别

提出了一种将颜色信息融入到纹理识别中的新方法--基于小波概率神经网络的彩色纹理识别.首先将RGB彩色纹理图像转化为HSV彩色模型,用小波变换(WT)进行树形结构小波分解提取彩色纹理的特征,然后使用概率神经网络对测试样本进行分类识别.实验结果证明,该方法的识别效果比较好.     

Efficient Content-Based Image Retrieval Methods Using Color and Texture

In this paper, we propose efficient content-based image retrieval methods using the automatic extraction of the low-level visual features as image content. Two new feature extraction methods are presented. The first one is an advanced color feature extraction derived from the modification of Stricker's method. The second one is a texture feature extraction using some DCT coefficients which represent some dominant directions and gray level variations of the image. In the experiment with an image database of 200 natural images, the proposed methods show higher performance than other methods. They can be combined into an efficient hierarchical retrieval method.     

Leaf Image-Based Plant Disease Identification Using Color and Texture Features

Wireless Personal Communications - Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time...     

活动轮廓模型综述

基于活动轮廓模型的目标分割、物体跟踪方法是近十几年来图像和视频领域研究的热点,它可以将待处理问题的先验知识与各种图像处理算法有效地融合在一起,比以往的计算机视觉理论有更强的实用性.本文结合图像分割方法从指导思想和所用的数学方法两方面对活动轮廓模型特别是几何活动轮廓模型中基于水平集方法的C-V方法做了一定综述.     

基于色貌模型CIECAM02和CIECAM16的观察者色觉差异研究

为研究现有CIECAM02和CIECAM16色貌模型对个体观察者颜色分辨的计算性能,选用了5个光谱功率分布不同的照明光源和GretagMacbeth ColorChecker标准色卡的24个颜色,代入208套个体观察者颜色匹配函数,量化计算不同观察者的色觉差异.两个色貌模型中,对于所有选用的照明光源和颜色中心,个体观察...     

Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models

We propose a new approach to diagnostic evaluation of screening mammograms based on local statistical texture models. The local evaluation tool has the form of a multivariate probability density of gray levels in a suitably chosen search window. First, the density function in the form of Gaussian mixture is estimated from data obtained by scanning of the mammogram with the search window. Then we evaluate the estimated mixture at each position and display the corresponding log-likelihood value as a gray level at the window center. The resulting log-likelihood image closely correlates with the structural details of the original mammogram and emphasizes unusual places. We assume that, in parallel use, the log-likelihood image may provide additional information to facilitate the identification of malignant lesions as untypical locations of high novelty.      

基于小波变换和支持向量机的彩色纹理识别

为了提高纹理图像的识别率,提出了一种将颜色信息融入到纹理识别中的新方法--基于小波变换和支持向量机的彩色纹理识别.首先将彩色纹理图像转化到HSV彩色空间,用小波变换进行树形结构小波分解提取彩色纹理的特征,然后用SVM对不同的特征进行纹理分类识别.对不同的彩色自然纹理图像进行了实验,并将结果与已有的进行了比较.实验结果证明,此方法的正确识别率比较高.     

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.     

Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images

Thyroid nodules are solid or cystic lumps formed in the thyroid gland and may be caused by a variety of thyroid disorders. This paper presents a novel active contour model for precise delineation of thyroid nodules of various shapes according to their echogenicity and texture, as displayed in ultrasound (US) images. The proposed model, named joint echogenicity–texture (JET), is based on a modified Mumford–Shah functional that, in addition to regional image intensity, incorporates statistical texture information encoded by feature distributions. The distributions are aggregated within the functional through new log-likelihood goodness-of-fit terms. The JET model requires only a rough region of interest within the thyroid gland as input and automatically proceeds with precise delineation of the nodules, revealing their shape and size. The performance of the JET model was validated on a range of US images displaying hypoechoic and isoechoic nodules of various shapes. The quantification of the results shows that the JET model: 1) provides precise delineations of thyroid nodules as compared to “ground truth” delineations obtained by experts and 2) copes with the limitations of the previous thyroid US delineation approaches as it is capable of delineating thyroid nodules regardless of their echogenicity or shape.      

Unsupervised Texture Segmentation Based on Artificial Immune Gaussian Mixture Models Network

We propose a novel unsupervised texture image segmentation scheme based on an Artificial immune Gaussian mixture models network （AIGMMN）, witch is really a mixture-of-mlxtures models. Starting from this algorithm, we coarsely group the homogeneous blocks into the known texture classes by the Fuzzy C-mean （FCM） clustering algorithm. Then we refinedly determine the heterogeneous blocks classification by AIGMMN. The new algorithm incorporates Expectation-maximization （EM） with the ideas of the Artificial immune clonal selection （AICS）, which can get a global optimal value and overcome the local maximum problems associated with the traditional EM algorithm. Moreover, the new algorithm is capable of determining the optimal number of components automatically in each subnet mixture model, which can approximate the non-gaussian multimodal densities distribution. We compare the proposed method with two common mixturebased segmentation methods for both synthetic and natural texture images, and achieve an overall improvement in performance.     

一种基于主动轮廓模型的自适应模板更新算法

在目标相关跟踪过程中,由于目标的姿态、大小发生变化,所以合理地更新模板极为重要.而已有的模板更新方法都不能适应目标的姿态和大小变化.提出了一种模板更新的新方法,该方法以颜色直方图的Bhattacharyya系数为基础,来进行模板更新时机的判断.并利用主动轮廓模型算法得到目标的边缘,自适应调整模板的大小和内容,从而实现对目标在发生姿态和大小等变化下的可靠跟踪.     

Video Copy Detection Method Based on Color and Texture Features of Key Frames

提出了一种基于关键帧颜色和纹理特征的视频拷贝检测方法.首先通过子片段方法提取视频的关键帧,然后将关键帧分成3个子块,提取每个子块的三维量化颜色直方图,通过直方图相交法来进行颜色特征的匹配.对检索得到的结果视频关键帧进行纹理特征提取,通过其灰度共生矩阵的角二阶矩和熵来表征其纹理特征,纹理特征的匹配可进一步过滤不相关的视频.实验结果表明,该方法效果好、稳健性强且可应用于多种类型的视频.     

Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models

Most denoising and enhancement methods for color images have been formulated on linear color models, namely, the channel-by-channel model and vectorial model. In this paper, we study the total variation (TV) restoration based on the two nonlinear (or nonflat) color models: the chromaticity–brightness model and hue–saturation–value model. These models are known to be closer to human perception. Recent works on the variational/PDE method for nonflat features by several authors enable us to denoise the chromaticity and hue components directly. We present both the mathematical theory and digital implementation for the TV method. Comparison to the traditional TV restorations based on linear color models is made through various experiments.     

基于颜色传感器的LED色温检测

目前节能环保的LED照明逐步流行,色温是LED灯的重要指标之一,在生产老化过程中对LED灯色温的实时监测是迫切需要的。而采用传统的光谱仪或照度计测量色温具有设备体积大、不能网络化的缺点。针对传统色温检测方法的缺点,提出一种基于颜色传感器的色温检测新方法,并结合CAN总线实现了多路色温在线监测系统,在监测LED色温的同时,对色温超出容限的LED给出标注并报警,给出了相关硬件电路和测试结果。     

Models of Restoration Probability in WDM Networks Employing Active Restoration

The ever-increasing demand for network bandwidth makes network survivability an issue of great concern. Lightpath restoration is a valuable approach to guaranteeing an acceptable level of survivability in WDM optical networks with better resource utilization than that of its protection counterpart. Active restoration (AR) is a newly proposed lightpath restoration scheme [M. Mostafa et al. OSA Journal of Optical Networking, vol. 3, no. 4, pp. 247–260] that combines the best of protection and reactive restoration while avoiding their shortcomings. In this paper, we conduct detailed performance analysis on the restoration probability of AR-based WDM networks. In particular, analytical models of restoration probability are developed respectively for networks with full-wavelength conversion capability and for networks without wavelength conversion capability under different backup path searching schemes. Based on the new models, we investigate the effects of wavelength availability, wavelength conversion capability, path length as well as backup path seeking methods on the restoration probability.