基于FPGA的图像锐化实现

简要介绍了图像锐化中的Laplacian算法和FPGA实现图像处理的优点,然后给出了基于FPGA的Laplacian算法实现方案,并且利用Altera公司提供的IP核实现了算法。对关键部分进行了仿真分析,将程序下载到开发板上进行验证,从结果可以看出,本算法很好地满足了实时性和增强图像的要求。     

基于多分辨率分析与形态学分水岭算法的图像分割

由于分水岭算法存在着过分割的问题,本篇论文提出了一个有效解决该问题的方法。首先,在图像预处理过程中先对图像进行小波分解;其次,采用形态学求梯度的方法得到小波分解后的低频图像的梯度图并对其进行开闭重建,在保留区域重要轮廓的同时去除噪声和图像细节;第三,对重建后的低频梯度图像进行基于标记约束的分水岭分割,并将低分辨率的分割结果图像向全分辨率映射。试验结果表明该方法能够很好地抑制过分割,同时通过结构元素的选择而具备一定的灵活性,整个过程无需进行合并处理,从而降低了分割的复杂性。     

应用视觉注意多分辨率分析的图像检索

基于人类视觉感知理论,提出一个改进的Itti视觉注意模型用于图像检索。该改进视觉注意模型是在充分考虑纹理特征与视觉感知关系的基础上,构造一个粗糙度图,用作视觉注意模型的一个初级视觉特征。首先通过该改进视觉注意模型得到50个视觉特征图;然后分别对每个视觉特征图采用局部二值模式傅里叶直方图（LBP-HF）方法抽取其分布信息,从而获得每幅图像的高维特征;最后利用局部保持投影(LPP)方法进行维数约简,以获取具有图像间局部几何和鉴别信息的低维特征用于图像检索。实验结果表明,该算法能获得较好的检索效果。     

Image saliency detection based on rectangular-wave spectrum analysis

Saliency detection is widely used in the fields of computer graphics and multimedia processing. Many computer graphics tasks, such as image segmentation, image labeling, and tracking, rely on the accurate generation of saliency maps. However, most current methods lack the ability to generate a fine boundary between the foreground and background while also providing a high recall rate. The saliency detection algorithm proposed in this paper is based on rectangular-wave spectrum analysis. In this method, we divide a given image into several regions, which are then convoluted using a pre-set rectangular-wave template. We determine the final saliency value by calculating the difference between a region and its adjacent regions, and its uniqueness compared with the entire image. Repeated tests using different data sets produced a high accuracy-recall rate. Moreover, the boundary in our saliency map is clear and fine.     

Frame-rate conversion detection based on periodicity of motion artifact

Multimedia Tools and Applications - With the advances in digital video technology, it is becoming easier to forge the digital video without introducing any artificial visual trace. The temporal...     

基于小波多分辨率分析的线条画生成方法

研究了小波变换在二维图像处理中的多分辨率分析特性,提出了一种基于小波多分辨率分析的线条画生成方法,并采用MATLAB语言进行了编程实现.将彩色图像转换为灰度图像,采用bior1.1小波对灰度图像进行二层小波分解,滤除分解系数中的低频系数进行图像重构,并通过反向、滤波和亮度调整等一系列处理,最终获得一种模拟手绘线条画的效果图.试验结果表明,用该方法生成线条画效果图是行之有效的.     

Image filtering using multiresolution representations

It is shown how multiresolution representations can be used for filter design and implementation. These representations provide a coarse frequency decomposition of the image, which forms the basis for two filtering techniques. The first method, based on image pyramids, is used for approximating the convolution of an image with a given mask. In this technique, a filter is designed using a least-squares procedure based on filters synthesized from the basic pyramid equivalent filters. The second method is an adaptive noise reduction algorithm. An optimally filtered image is synthesized from the multiresolution levels, which in this case are maintained at the original sampling density. Individual pixels of the image representation are linearly combined under a minimum mean square error criterion. This uses a local signal-to-noise ratio estimate to provide the best compromise between noise removal and resolution loss     

Digital change detection with the aid of multiresolution wavelet analysis

This Letter presents the preliminary findings of a new approach to deal with misregistration effects on change detection results. A multiresolution analysis with wavelet transforms applied to image differencing results enabled the extraction of changed sites according to size classes. Changes of interest were pinpointed successfully without the necessity of accurate spatial registration or radiometric rectification while differences not related to land cover changes were bypassed. The method's applicability is demonstrated with a multitemporal data set of Landsat MSS and TM images for the detection of deforestation and new areas of rock exploitation in south-eastern Brazil.     

图像多尺度边缘检测算法研究

通过检测二维小波变换的模极大值线可以确定图像的边缘点.由于小波变换在各尺度上都提供了图像的边缘信息,所以称为多尺度边缘检测.沿着边界方向将任意尺度下的边缘连接起来,可形成该尺度下沿着边界的模极大线.小波变换能够把图像分解成多种尺度成分,并对大小不同的尺度成分采用相应的时域或空域取样步长,从而能够不断地聚焦到对象的任意微小细节.小波变换具有的多尺度特性,正好可以用于图像的边缘检测.     

基于图像显著性检测的图像分割

图像分割在许多图像处理和机器视觉问题中是一个非常重要的过程,是将一幅图分割成几个显著的区域,然而不能将其中最显著的目标直接分割出来,需要进一步处理。为此本文采用显著性检测的算法实现了对目标的分割。显著性区域检测可以应用于目标检测、图像检索、图像分割等机器视觉问题。使用杨等人提出的基于图论的流形排序算法检测显著性算法得到显著性图,再结合mean-shift分割算法,实现了对视觉显著性目标分割提取,可获得可观的图像分割结果,并将此算法应用到了森林火灾检测中,能对图像中的火焰部分进行有效的分割提取。     

Convex multiresolution analysis

A standard wavelet multiresolution analysis can be defined via a sequence of projectors onto a monotone sequence of closed vector subspaces possessing certain properties. We propose a nonlinear extension of this framework in which the vector subspaces are replaced by convex subsets. These sets are chosen so as to provide a recursive, monotone approximation scheme that allows for various signal and image features to be investigated. Several classes of convex multiresolution analyses are discussed and numerical applications to signal and image-processing problems are demonstrated     

基于小波分析与KPCA的人脸识别方法

对于一种有效的人脸识别方法,特征选择是极为重要的问题。而小波多分辨率分析可以获得对人脸识别有用的低频特征,KPCA则可用于提取人脸非线性特征。为此,本文〖BP）〗提出结合小波变换及KPCA的特点获取人脸特征,设计线性SVM分类器进行分类识别。由于KPCA中核函数的参数选择以及训练样本与测试样本的划分对分类识别有一定的影响,为了获得最优的识别效果,在UMIST人脸数据库上进行相应的实验。结果表明本方法可以获得较好的分类识别率,是一种快速、有效的人脸识别方法。     

Image segmentation by a multiresolution approach

Multiresolution approaches to computer vision are able to rapidly detect and extract global structures from an image. In this paper we present (a) a pyramid-based algorithm that can detect the bimodality of the population of pixels in a grey level digital image and (b) a pyramid-based algorithm that maps the values of a bimodal population into two constant values which are approximately the means of the two component subpopulations. A population is considered bimodal if it can be divided into two component subpopulations whose variances are small relative to the population variance. An improvement to the above algorithm, which uses an iterative scheme, is also given, as well as some examples of segmented images. Both algorithms require processing times on the order of the logarithm of the population size.     

基于独立成分分析和支持向量机的图像型火灾探测

图像型火灾探测具有非接触性、反应快等优点,可有效解决大空间火灾探测难题,是火灾探测新的研究方向,其核心问题是火焰和干扰物的分类识别。常用方法是提取火焰在图像上表现的单个或某几个特征信息作为识别依据,需要设置大量经验阈值,识别率常因特征选择不合适而受到影响。通过对火焰整体特性的研究,提出了基于独立成分分析和支持向量机的火焰探测方法。首先在RGB空间建立颜色模型对连续数帧火灾图像预处理,并进行频闪特性和模糊聚类分析提取疑似目标区域,根据独立成分分析线性变换一对一和可逆性估计出基函数描述火焰图像特征,最后用支持向量机模型实现火灾探测。实验结果表明,该方法提高了图像型火灾探测精度和速度,可适用于多种火灾探测场景。     

Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Since shape is one of the important low level features of any Content Based Image Retrieval (CBIR) system, this paper proposes a new edge based shape feature representation method with multiresolution enhanced orthogonal polynomials model and morphological operations for effective image retrieval. In the proposed method, initially the orthogonal polynomials model coefficients are computed and reordered into multiresolution subband like structure. Edge image is then obtained by utilizing the two level adaptive thresholds and local maxima of the gradient in horizontal, vertical, diagonal and anti-diagonal directions. The approximate shape boundary of the image is recovered with morphological operations. Then the Pseudo Zernike moment based global shape features, which are invariant to basic geometric transformations, are extracted. The obtained features are termed as global shape feature vector and are used for retrieving similar images with Canberra distance metric. The efficiency of the proposed method is experimented on a subset of standard Corel, Yale and MPEG-7 databases and the results are compared with existing techniques.     

基于显著性检测的图像隐写分析方法

针对图像隐写分析难度大、现有的检测模型难以对图像隐写区域进行针对性检测的问题,提出了一种基于显著性检测的图像隐写分析方法.该方法利用显著性检测技术引导隐写分析模型更加关注图像隐写区域的特征.首先,显著性检测模块生成图像的显著性区域;其次,区域筛选模块筛选出与隐写区域重合度较高的显著性图,利用图像融合技术与原始图像进行融...     

融合显著物体检测的图像显示适配

目的 图像显著适配旨在自动调节图像尺寸,对图像内容进行非均匀缩放,以便在受限的展示空间内更好地保留显著物体。为了解决显示适配过程中显著物体部分扭曲的问题,提出一种基于显著物体检测的图像显示适配方法。方法 本文方法采用显著物体分割结果来替代显著性图,以改进显示适配结果。首先,采用显著性融合和传播的方法生成显著性图;接着,结合输入图像和显著性图,采用自适应三阈值方法实现显著物体分割;然后,以此为基础,生成输入图像的曲边网格表示;最后,通过对不同网格的非均匀缩放,生成符合目标尺寸的适配结果。结果 在面向图像显示适配的公开数据集RetargetMe上,将本文方法与现有的10种代表性显示适配方法的结果进行了人工评估和比较。本文方法可以有效地减少显著物体出现部分扭曲的现象,能在48.8%的图像上取得无明显缺陷的适配效果,比现有最好的方法提高了5%。结论 基于显著物体检测的图像显示适配方法有助于提高显示适配过程中对显著物体处理的一致性,减少由于显著物体部分扭曲而引起的明显人工处理痕迹,从而达到提升显示适配效果的目的。     

基于通道间相关性的图像重着色检测

图像重着色是一种新兴的图像编辑技术,通过篡改像素值达到改变图像颜色风格的目的。随着社交网络和图像编辑技术的快速发展,重着色图像已经严重阻碍了信息传达的真实性。然而,专门为重着色而设计的工作少之又少,现有的重着色检测方法在传统重着色场景下仍有很大提升空间,在应对手工重着色图像时效果不佳。为此,提出了一种基于通道间相关性的重着色图像检测方法,该方法适用于重着色任务中的传统重着色和手工重着色场景。基于相机成像和重着色图像生成方式之间存在显著差异这一现象,提出重着色操作或许会破坏自然图像的通道间相关性这一假设。通过数值分析说明,通道间相关性差异可作为区分重着色图像和自然图像的重要鉴别度量。基于上述先验知识,所提方法通过提取差分图像的一阶微分残差的通道共生矩阵,获得图像的通道间相关性特征集。此外,根据实际情况,假设了3种检测场景,包括训练-测试数据之间匹配、不匹配以及手工重着色场景。实验结果表明,所提方法能够准确识别重着色图像,在假设的3种场景下均优于现有方法,取得了较高的检测精度。除此之外,所提方法对训练数据量的依赖性较小,在训练数据有限的情况下,能实现相当精确的预测结果。     

Image resampling detection based on texture classification

Multimedia Tools and Applications - This study presents a method for resampling detection. By combining texture analysis with resampling detection, the task of resampling detection is considered as...     

基于量化表的图像篡改检测算法

数字图像盲取证技术是近年来研究的一个热点问题。提出一种利用量化表来进行定位和检测数字图像双重JPEG压缩的篡改检测算法。首先对数字图像进行压缩消除图像本身的噪声,然后利用图像压缩模型来描述图像首次压缩与第二次压缩之间的关系进而估计首次压缩量化表,最后提出一种高效率的方法通过利用量化表来定位图像篡改区域。实验结果表明,该算法能够有效地对双重JPEG压缩的图像进行检测和定位,并具有很好的鲁棒性。