一种改进的高亮度区域肤色像素检测算法

高亮度条件下肤色和非肤色具有很高的相似性,导致高亮度肤色像素的检测一直是基于肤色分割的一个难点。为了提高肤色分割的效果,提出一种能够检测出图像中高亮度肤色区域的方法。首先在YCbCr颜色空间中利用阈值自适应的单高斯模型检测出图像中具有正常亮度的肤色像素,再结合像素的邻域信息和亮度边缘信息进一步检测出图像中高亮度区域的肤色像素,有效地解决了传统肤色检测方法对高亮度肤色区域漏检的问题。实验结果表明,采用该算法可以较准确地检测出图像中的高亮度肤色区域。     

Color image segmentation: a novel spatial fuzzy genetic algorithm

Image segmentation is a very important low-level vision task. It is the perceptual grouping of pixels based on some similarity criteria. In this article, we have applied spatial fuzzy genetic algorithm (SFGA) for the unsupervised segmentation of color images. The SFGA adds diversity to the search process to find the global optima. The performance of SFGA is influenced by two factors: first, K number of clusters—should be known in advance; second, the initialization of the cluster centers. To overcome these issues, a progressive technique based on self-organizing map is presented to find out the optimal K number of clusters automatically. To handle the initialization problem, peaks are identified using the image color histograms. The genetic algorithm with fuzzy behavior maximizes the fuzzy separation and minimizes the global compactness among the segments. The segmentation is performed on wavelet transform image which not only reduces the dimensionality and computational cost but also makes more compact segments. A novel pruning technique is proposed to handle the problem of over-segmentation. The results show that the proposed technique outperforms state-of-the-art methods.     

一种优化的FCM图像分割方法

结合粗糙集理论,利用像素邻域的空间信息,对图像色彩的分布进行了量化表示,并据此提出一种基于量化粗糙信息的改进图像分割方法,该方法使用局部量化粗糙度和待定算子来更新FCM算法中的隶属度函数,实现图像的初步分割,并针对初步分割后的小区域和相似区域,进行色彩区域的合并操作,来对分割结果进行优化。实验结果表明,相对于传统的模糊C-均值(FCM)聚类分割算法,提出的方法降低了时间复杂度,且具有良好的分割效果。     

Similarity relation matrix-based color edge detection

A novel edge detection algorithm for color images was described in this paper. In the proposed method, smoothness of each pixel in color image is firstly calculated by means of similarity relation matrix and is normalized to maximum gray level. In other words, color image in three-dimensional color spaces is mapped into one dimension. Accordingly the edges are performed in such a way that pixels lower than thresholds are assigned to be edge. Thus with proposed method, edge pixels in a color image are detected simultaneously without any complex calculations such as gradient, Laplace and statistical calculations.     

基于区域最大相似度的快速图像分割算法

针对基于区域最大相似度图像分割(MSRM)算法中利 用颜色直方图描述符计算相邻区域之间相似度存 在着计算量大和描述能力的不足,提出改用基于局部二值模式(LBP)纹理描述符计 算相邻区域相似度的改进MSRM(IMSRM)算法。LBP描述符通过对像素点之间局 部微结构进行编码实 现了对其空间关系的描述,有效提高了对区域特征的描述能力,并且所获得的特征矢量维数 远小于颜色直 方图,区域之间的相似度计算效率大幅度得到提高。与MSRM算法对比实验表明,IMSRM算法 可以从各种复杂背景中有效提取待分割对象的轮廓,所提取的轮廓边缘细节上更优,算法执 行效率能够提高50%左右。     

图像区间模糊模型及其在图像检索中的应用

在HSV颜色空间下,通过非均匀量化方法,构建了图像区间模糊模型.在此模型下,图像可以看作是一个区间模糊集合(IVFS,interval-valued fuzzy set).IVFS之间相似性度量可以用来衡量图像之间的相似程度.给出一种基于LP范数距离的IVFS集合度量(简称IVFSLp)并将它应用于图像检索中.实验数据表明:IVF-SLP与直方图距离(histogram distance)和普通模糊集的相似性度量(general fuzzy similarity measure)相比具有更好的性能.     

融合对称特性的混合标签传递半监督直觉模糊聚类图像分割

现有的直觉模糊聚类算法应用于图像分割时,往往只考虑图像的像素信息,忽略了图像的几何特征和区域信息,使得分割效果不太理想。为了提高直觉模糊聚类算法的分割性能,提出一种融合对称特性的混合标签传递半监督直觉模糊聚类算法。该算法首先对图像进行对称轴检测获取图像的对称特性,接着利用图像的对称特性进行对称像素的标签传递并改进像素对聚类中心的直觉模糊距离测度,然后设计一种混合标签传递半监督策略,对所有像素进行隶属度的估计并将其作为监督隶属度进行引入,随后构建融合对称特性的混合标签传递半监督直觉模糊聚类目标函数,通过聚类获得最终的分割结果。两个彩色图像库上的实验结果表明,该算法能够将目标从复杂背景中完整的分割出来,分割性能优于对比算法。      

基于自适应相似度距离的改进FCM图像分割

为了改善传统FCM算法抗噪性差的问题,提出了基于自适应相似度距离的FCM算法.算法将像素分为两个特征:第一个描述的是像素的内在属性(灰度级特征),第二个描述邻域像素特征(空间特征).在此基础上,基于自适应相似度距离,根据像素在图像中的空间位置决定哪一个特征拥有优先级,对其进行聚类.图像分割结果表明,算法比标准FCM算法有明显改善,具有很好的抗噪性能,取得了更好的分割效果.     

基于各向异性热扩散方程的多聚焦图像融合算法

本文根据多聚焦图像的光学成像原理,利用各向异性热扩散方程来估计图像深度信息,并讨论一种基于深度信息提取的多聚焦图像的融合算法.算法首先对两幅多聚焦图像进行光学成像过程的模拟,分别建立正向区域的热扩散方程,通过对热扩散方程的迭代求解估计出图像场景的深度信息.然后对深度信息进行自适应的区域划分,分别得到每幅图像的清晰区域,模糊区域和中间过渡区域.最后通过提取清晰区域的像素点,融合过渡区域的像素点实现多聚焦图像的融合.理论推导和实验验证表明,本方法能够克服常用算法中出现的块效应和人工痕迹,是一种有效的图像融合算法.     

A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation

Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions (GIFP_FCM) is a novel fuzzy clustering algorithm. However when GIFP_FCM is applied to image segmentation, it is sensitive to noise in the image because of ignoring the spatial information contained in the pixels. In order to solve this problem, a novel fuzzy clustering algorithm with non local adaptive spatial constraint (FCA_NLASC) is proposed in this paper. In the proposed method, a novel non local adaptive spatial constraint term is introduced to modify the objective function of GIFP_FCM. The characteristic of this technique is that the adaptive spatial parameter for each pixel is designed to make the non local spatial information of each pixel playing a different role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially magnetic resonance (MR) images, are performed to assess the performance of an FCA_NLASC in comparison with GIFP_FCM and fuzzy c-means clustering algorithms with local spatial constraint. Experimental results show that the proposed method is robust to noise in the image and more effective than the comparative algorithms.     

彩色图像置乱方法研究

这里主要研究彩色图像的置乱方法,目的是提高置乱效果及置乱效率。分别研究了基于像素灰度变换的彩色图像置乱方法及基于像素位置变换的彩色图像置乱方法,仿真分析了2种置乱方法的不足,提出了一种新的基于Arnold变换的彩色图像置乱方法。采用同时改变像素位置及灰度值的方法,兼顾了置乱图像在视觉上及理论上的置乱效果。仿真实验表明该置乱算法提高了置乱度,节约了恢复时间,是一种简单、高效的彩色图像置乱方法。     

基于偏移差值的结构化稀疏图像修复算法

针对基于匹配的图像修复算法中存在线性结构和 重复区域不能良好保持结构连贯性的问题,提出基 于偏移差值的结构化稀疏图像修复算法。首先定义一个新的特征偏移差值,结合图像颜色特 征构造样本块 间相似度项,并利用该相似度项寻找最佳匹配块;然后将偏移差值信息和颜色信息共同作为 稀疏约束项来 构造约束方程,以提高稀疏表示的准确性,从而保证邻域信息一致性。实验结果表明, 文 中方法对线性结 构和重复区域的破损修复能获得更好的效果。     

Real-time Face Detection using Skin Color Model

1　Introduction　Facedetectionandrecognitionarestilltoughtasksofpatternrecognition ,in particularinreal timeapplica tions ,e .g .,securitysystems ,humanandmachinein teraction ,smartmediasystems,andsoon .Thediffi cultiesaremainlyduetothevariationsofilluminationconditions,viewpoints,pose ,imagesize ,etc ..Facedetectionisthecrucialstepinfacerecognition .Ifthepresenceofhumanfaceisdetected ,thelocationandsizeofthefaceregionarereturned .Overthelastthreedecades,manyfacedetectionmethodshavebeendevel …     

Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision‐based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand‐alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state‐of‐the‐art fire detection method.     

Perceptual similarity between color images using fuzzy metrics

In many applications of the computer vision field measuring the similarity between (color) images is of paramount importance. However, the commonly used pixelwise similarity measures such as Mean Absolute Error, Peak Signal to Noise Ratio, Mean Squared Error or Normalized Color Difference do not match well with perceptual similarity. Recently, it has been proposed a method for gray-scale image similarity that correlates quite well with the perceptual similarity and it has been extended to color images. In this paper we use the basic ideas in this recent work to propose an alternative method based on fuzzy metrics for perceptual color image similarity. Experimental results employing a survey of observations show that the global performance of our proposal is competitive with best state of the art methods and that it shows some advantages in performance for images with low correlation among some image channels.     

Real time interpolation algorithm based on Lanczos kernel

In order to improve the timeliness and reliability of color image scaling algorithms,a high effective real-time color image scaling algorithm was proposed,which first generated the kernel look up table that could be used for whole target image based on Lanczos kernel,and interpolation of all target image pixels had only fixed point operation which had low computational complexity.Meanwhile,an optimized method for rasterizing pixels of target image was proposed,which needed little memory space but embraced high efficiency of memory utilization.The experiment results show that the proposed algorithm achieves good image quality,and the computational complexity is far more less than classic image scaling algorithms.So the proposed algorithm can effectively implement real-time resizing of color images and color videos in any scaling factors.     

Automatic segmentation of color images with transitive closure

In this paper, a mask based automatic segmentation algorithm for color images which uses pixel similarity has been presented. Main concept of the algorithm relies on spatial mask for course segmentation and the Warshall's transitive closure (TC) computation algorithm for region merging. Although the proposed spatial mask approach reduces the computational burden required for segmentation or clustering techniques such as seeded region growing (SRG) or fuzzy c-means (FCM) in which user supplied parameters are essential, it has over segmentation drawback. Therefore, the transitive closure algorithm, which uses adjacency and similarity matrix associated to undirected graph of the over segmented image, has been employed to merge the regions. After comparing to existing methods, the obtained experimental results confirmed that the color images as well as gray level images could be segmented with considerable accuracy. Also computational complexity of image segmentation is significantly reduced. Furthermore, there is no need any user supplied parameter such as the number of clusters or seed points.     

Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L_*a_*b_* color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.     

基于非局部相似性和交替迭代优化算法的图像压缩感知

压缩感知理论突破了信号带宽对奈奎斯特采样定理的限制,并且实现了在数据采样的同时进行压缩。目前压缩感知系统通常利用图像在某个变换域具有稀疏性的先验知识,从少量观测值中重构原始图像。本文利用图像像素的邻域结构信息及图像子块的相似性,将图像的非局部相似性作为先验知识运用到压缩感知图像重构中。结合图像的非局部相似性及其在变换域的稀疏性先验知识,提出了基于非局部相似性和交替迭代优化算法的图像压缩感知重构算法,该算法利用迭代阈值法和非局部全变差来交替迭代求解变换域的稀疏性优化问题和非局部相似性的优化问题。实验结果表明,本文算法可以有效提高图像重构的视觉效果和峰值信噪比。      

A High Quality Depth Map Upsampling Method Robust to Misalignment of Depth and Color Boundaries

In recent years, fusion camera systems that consist of color cameras and Time-of-Flight (TOF) depth sensors have been popularly used due to its depth sensing capability at real-time frame rates. However, captured depth maps are limited in low resolution compared to the corresponding color images due to physical limitation of the TOF depth sensor. Most approaches to enhancing the resolution of captured depth maps depend on the implicit assumption that when neighboring pixels in the color image have similar values, they are also similar in depth. Although many algorithms have been proposed, they still yield erroneous results, especially when region boundaries in the depth map and the color image are not aligned. We therefore propose a novel kernel regression framework to generate the high quality depth map. Our proposed filter is based on the vector pointing similar pixels that represents the unit vector toward similar neighbors in the local region. The vectors are used to detect misaligned regions between color edges and depth edges. Unlike conventional kernel regression methods, our method properly handles misaligned regions by introducing the numerical analysis of the local structure into the kernel regression framework. Experimental comparisons with other data fusion techniques prove the superiority of the proposed algorithm.