改进的Canny算子与SIFT算法相结合的医学图像特征提取

微创外科手术中的图像特征快速提取与分析,可以使计算机具备图像实时识别的能力,提高手术的成功率。对常用的边缘与角点特征进行了分析,提出了边缘与角点相结合的特征提取方法。以二尖瓣索修复微创手术图像为例,对常用的几种边缘算法和改进的Canny算法进行了分析比较,利用改进的Canny算子所得到的较为完整的检测边缘信息,运用SIFT算法提取图像角点,剔除了一些伪特征点,得到了图像匹配时所需要的更加精确的信息。为后续的图像融合与复原以及三维图像重建打下了良好的基础。     

基于多尺度Harris角点的图像文字检测

为解决传统的基于Harris角点的图像文字检测算法易受非文字角点干扰,检测准确率低的问题,提出一种基于多尺度Harris图像文字检测算法.该算法在多个尺度下提取角点,并利用分块方法分析文字局部特征,有效剔除了非文字角点.使用多次迭代逐步剔除非文字区域角点,精确提取备选块中的文字角点;通过区域融合形成文字区域,用轮廓跟踪法标识文字区域.实验结果表明,该算法明显提高了图像/视频文字检测的稳定性和准确率.     

基于USAN的改进的角点检测算法

提出了一种基于USAN的改进的角点检测算法。该算法在原有SUSAN算法的基础上做了如下改进：使用一个3×3的方形预检测窗口对图像的像素进行预检测，在精确检测角点前剔除掉大部分的背景点、边界点及脉冲噪声点，提高了算法的效率；根据图像不同区域对比度不同的特性，采用根据对比度自动调节核心点与其邻域像素的灰度差值门限的方法，使所检测出的角点分布均匀；利用基于USAN定义的角点所应具有的特征(角的边缘及USAN的连续性)来剔除伪角点，降低了角点虚报和漏检的发生率。仿真实验证明了该文所提出的算法具有抗噪能力强、运算量小的特点，适于实时实现。     

基于AP聚类图像分块的角点检测改进方法

将已有改进的Harris角点检测方法应用于图像拼接时,在单一尺度下检测角点会存在角点信息丢失、角点位置不准确和对噪声敏感致使检测率不高等缺点。为此,结合双边滤波和多尺度检测,提出一种Harris角点检测改进方法。采用双边滤波器代替原有的高斯低通滤波器,以增强方法的鲁棒性,引入近邻传播聚类方法将图像分块,避免因阈值问题造成角点分布不均,并将多尺度概念引入到改进方法中,判断候选角点是否为真实角点,剔除伪角点,使得角点检测更加精确。在 VS2010+OpenCV平台上进行实验,结果表明,与基于图像分块的多尺度Harris角点检测方法相比,该方法在去伪角点、漏检、准确度等方面取得了更好的效果,且具有更好的抗噪性。     

一种改进的基于Harris的角点检测方法

在研究Harris角点检测算法时发现该算法对一些图像进行角点提取时,存在提取伪角点、角点信息丢失和位置偏移,而且在进行非极大值抑制时不易设置阈值等现象.提出了在进行非极大值抑制时采用双阈值法,分别设置一个相对大和一个相对小的两个阈值,从而得到同一图像不同阈值的角点信息,通过角点信息对比能够很好地解决角点信息丢失和位置偏移并能消除一部分伪角点,然后利用SUSAN的思想消除剩余的伪角点.通过对比实验表明,文中算法提取角点非常有效,比Harris算法具有更好的角点检测性能.     

机器视觉复杂平面边缘角点的高精度定位方法

基于机器视觉的二维复杂平面尺寸测量中,边缘角点包含丰富的图像目标特征信息,其检测精度对后续图像分析及参数计算精度有着至关重要的影响。本文提出了一种边缘轮廓角点的高精度定位方法,该方法先利用角点处的曲率变化及相邻两角点线段与x轴正向的夹角变化,初步确定角点的位置坐标,然后利用优化方法去除伪角点,以实现对真实角点的高精度定位。研究表明,该方法便捷、高效,在服装衣片的样片边缘测量中取得了理想的结果。     

基于图像分块的多尺度Harris角点检测方法

Harris角点检测是一种经典的角点检测算法,在现实中应用广泛,但不具有尺度变化特性。为了改变其单一尺度的特性,使得角点提取更加精确和有效,将多尺度的概念和图像分块方法引入到Harris算法中,在多个尺度下对角点进行提取。将每个尺度上的角点响应值的本地最大值作为该尺度上的候选角点,并同时对图像进行分块;最后,沿小尺度到大尺度方向判断候选角点是否是真实角点,剔除伪角点,使得角点检测更加精确。通过对比实验,新算法明显地提高了图像角点的检测性能。     

一种改进的快速图像拼接方法

为了提高图像拼接的速度,提出了一种快速的图像拼接方法.首先在SUSAN角点检测算法检测出图像角点的基础上,采用图像分块和邻近角点剔除的方法来保证图像角点分布均匀并且避免出现角点聚簇现象,利于提高拼接的精度.其次,利用灰度相关性进行特征角点的匹配并消除伪匹配.然后采用改进的RANSAC算法快速地估计变换矩阵,该算法中采用预检测的方法快速抛弃那些不是候选模型的临时模型,加快了算法的速度.最后进行颜色融合,生成无缝拼接图像.实验结果表明,该方法在得到较高精度的情况下,大大减少了运算量,提高了图像拼接的速度.     

基于改进Harris算法的汉字图像角点检测

根据汉字图像的特点,借鉴加速分割检测特征算法的思想,提出一种改进的Harris算法对汉字图像进行角点检测。首先,计算像素值初步判断出非角点并排除;然后,通过计算传统Harris算法中的角点响应函数对剩余的像素进行角点检测;最后,借鉴加速分割检测特征算法的思想对伪角点进行删除。最终检测出的角点是汉字笔画的起点和末端的角点,为下一步特征提取中确定线段的位置和计算线段的长度提供有利的技术基础。通过对一定数量的汉字图像的实验仿真,将本文方法与几种常用的角点检测方法进行比较,本文方法在检测正确率方面有所提高,但在运行时间上没有达到最短,综合考虑正确率和运行时间,本文方法较其他几种方法有所提高。      

基于Harris角点检测的人体特征提取与测量

为准确地对人体图像特征尺寸进行自动识别和提取,提出一种基于Harris角点检测的人体特征提取与测量算法;首先对原始图像规范化处理,并采用Canny算子进行边缘检测,得到图像的二值边缘图;然后根据人体特征尺寸位置的突变性可用角点来描述的特点对轮廓图像进行特征点提取;最后利用人体关键尺寸与身高(由用户提供)的比例关系进行特征点筛选,计算获取人体测量学中的关键尺寸;经对实际测量数据分析比较得知,实验测量结果产生的误差较小,实验值和实际值之间无显著性差异,因此验证了该方法的可行性。     

融合特征和先验知识的车牌字符图像检测算法

提出一种融合车牌字符切割后的二值字符图像的结构特征及对应的彩色小字符图像的颜色信息对车牌分割后的小字符图像进行真伪字符图像区分,以此达到检测字符图像目的的算法。为满足实时车牌对时间响应的要求,对车牌字符切割得到的灰度图像作快速二值化,在二值化的图像上提取结构信息,结合车牌字符分布的特点去除了大部分非颜色车牌的伪字符图像。对于难以从结构上进行字符检测的颜色车牌伪字符图像,在结构特征分析的基础上再次通过从彩色图像提取的颜色信息进行相似性分析,排除伪字符图像。对候选字符图像融合大间隔这个先验知识得到输出的字符图像。实验结果表明算法有良好的字符检测效果,可以用于实时车牌识别系统中作为字符切割后期处理一部分。     

ConvNet frameworks for multi-modal fake news detection

An upsurge of false information revolves around the internet. Social media and websites are flooded with unverified news posts. These posts are comprised of text, images, audio, and videos. There is a requirement for a system that detects fake content in multiple data modalities. We have seen a considerable amount of research on classification techniques for textual fake news detection, while frameworks dedicated to visual fake news detection are very few. We explored the state-of-the-art methods using deep networks such as CNNs and RNNs for multi-modal online information credibility analysis. They show rapid improvement in classification tasks without requiring pre-processing. To aid the ongoing research over fake news detection using CNN models, we build textual and visual modules to analyze their performances over multi-modal datasets. We exploit latent features present inside text and images using layers of convolutions. We see how well these convolutional neural networks perform classification when provided with only latent features and analyze what type of images are needed to be fed to perform efficient fake news detection. We propose a multi-modal Coupled ConvNet architecture that fuses both the data modules and efficiently classifies online news depending on its textual and visual content. We thence offer a comparative analysis of the results of all the models utilized over three datasets. The proposed architecture outperforms various state-of-the-art methods for fake news detection with considerably high accuracies.

     

基于图像扩散速度模型和纹理信息的人脸活体检测

为了解决人脸身份认证中的欺诈问题,提出了一种基于图像扩散速度模型和纹理信息的人脸活体检测算法。真实人脸和虚假人脸图像的空间结构不同,为了提取这种差异特征,该方法使用各向异性扩散增强图像的边缘信息。然后,将原始图像与扩散后图像的差值作为图像的扩散速度,并构建扩散速度模型。接着使用局部二值算法提取图像扩散速度特征并训练分类器。真实人脸图像和虚假人脸图像之间存在很多差异特征,为了进一步提高人脸活体检测算法的泛化能力,该方法同时提取人脸图像的模糊程度特征和色彩纹理特征,通过特征矩阵级联的方法将两种特征进行融合,并训练另一个分类器。最后根据分类器输出概率加权融合的结果做出判决。实验结果表明,该算法能够快速有效地检测出虚假的人脸图像。     

A Catalog of 1-D Features in Natural Images

This paper explores the local form of actual feature types contained in real images. The local energy feature detector is used to locate points in an image where features are found. An unsupervised neural network is trained to capture the mean luminance values and standard deviations of the luminance values in a small neighborhood of these feature points. This local luminance information is called a feature template. After culling and normalization, we arrive at a catalog of local feature forms for the image. Our experiments indicate that the feature forms are self-similar over different images and across scales. When described by their phase angle, features also show some clustering around a small number of types. The size of the feature catalog is small, and shows promising applications in the area of image compression and reconstruction. Quantization of phase angles around the central angles of clusters yields a catalog of synthetic feature templates that further improves the fidelity of the reconstructed images.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题，通过分析高分二号影像中典型地物的光谱特征，构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理，建立包含波段均值、标准差等特征的规则集，对影像的I和PC1分量进行多尺度分割 ，结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验，实验结果表明:与传统基于像素的阴影提取方法相比，该方法提取出的阴影斑块完整，且能有效地减弱水体和植被的影响。     

Fake finger-vein image detection based on Fourier and wavelet transforms

Recently, finger-vein recognition has received considerable attention. It is widely used in many applications because of its numerous advantages, such as the small capture device, high accuracy, and user convenience. Nevertheless, finger-vein recognition faces a number of challenges. One critical issue is the use of fake finger-vein images to carry out system attacks. To overcome this problem, we propose a new fake finger-vein image-detection method based on the analysis of finger-vein images in both the frequency and spatial domains.This research is novel in five key ways. First, very little research has been conducted to date on fake finger-vein image detection. We construct a variety of fake finger-vein images, printed on A4 paper, matte paper, and overhead projector film, with which we evaluate the performance of our system. Second, because our proposed method is based on a single captured image, rather than a series of successive images, the processing time is short, no additional image alignment is required, and it is very convenient for users. Third, our proposed method is software-based, and can thus be easily implemented in various finger-vein recognition systems without special hardware. Fourth, Fourier transform features in the frequency domain are used for the detection of fake finger-vein images; further, both spatial and frequency characteristics from Haar and Daubechies wavelet transforms are used for fake finger-vein image detection. Fifth, the detection accuracy of fake finger-vein images is enhanced by combining the features of the Fourier transform and Haar and Daubechies wavelet transforms based on support vector machines.Experimental results indicate that the equal error rate of fake finger-vein image detection with our proposed method is lower than that with a Fourier transform, wavelet transform, or other fusion methods.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题,通过分析高分二号影像中典型地物的光谱特征,构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理,建立包含波段均值、标准差等特征的规则集,对影像的I和PC1分量进行多尺度分割 ,结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验,实验结果表明:与传统基于像素的阴影提取方法相比,该方法提取出的阴影斑块完整,且能有效地减弱水体和植被的影响。     

Revealing digital fakery using multiresolution decomposition and higher order statistics

With the advance of digitization and digital processing techniques, digital images are now easy to create and manipulate, and leave no clues of artificial evidence. There are some known digital fakery for images, e.g., computer graphics (CGs) and digital forgeries. As valid records of natural world, natural images, i.e., photographic images, are no longer believable. In this paper, a detection scheme for natural images and fake images is proposed. Features are first extracted using multiresolution decomposition and higher order local autocorrelations (HLACs). The support vector machines (SVMs) are then used to differentiate the natural and fake images. Because the inner product between features can be obtained directly without computing features, it can be integrated into SVM, and the computation complexity is decreased. Experiments show that the proposed detection scheme is effective, demonstrating that the proposed statistical features can model the differences between natural images and fake images.     

基于改进的C-V模型虚拟人脑图像分割模型

C-V模型是一种较为经典的分割模型,但传统的C-V模型仅能够将图像分割成单一的目标部分与背景部分;用于彩色图像分割往往基于目标的强度信息;在曲线演化过程中需要重新初始化水平集函数保持符号距离函数。针对这些问题,使用PCA理论将颜色空间投影到新的空间中,可以扩大两者的颜色距离;使用局部信息可校正颜色强度不均匀;将距离约束项引入到模型中,使模型能够无需重新初始化,提高了演化速度。实验结果表明改进的算法能较精确地得到分割结果。     

Detecting image forgeries using metrology

Image forgery technology has become popular for tampering with digital photography. This paper presents a framework for detecting fake regions using single view metrology and enforcing geometric constraints from shadows. In particular, we describe how to (1) estimate the region of interest’s 3D measurements from a single perspective view of a scene given only minimal geometric information determined from the image, (2) determine the fake region by exploring the imaged shadow relations that are modeled by the planar homology. We also show that image forgery on the vertical plane or arbitrary plane can be detected through the measurement on such plane. Our approach efficiently extracts geometric constraints from a single image and makes use of them for the digital forgery detection. Experimental results on both the synthetic data against noise and visually plausible images demonstrate the performance of the proposed method.