Exposing photo manipulation with inconsistent perspective geometry

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value （NMV） and normalized standard deviation （NSD） of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.     

AN ADAPTIVE COLOR PLANE INTERPOLATION METHOD BASED ON EDGE DETECTION

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional （1D） gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.     

Research on high-precision hole measurement based on robot vision method

A high-precision vision detection and measurement system using mobile robot is established for the industry field de- tection of motorcycle frame hole and its diameter measurement. The robot path planning method is researched, and the non-contact measurement method with high precision based on visual digital image edge extraction and hole spatial circle fitting is presented. The Canny operator is used to extract the edge of captured image, the Lagrange interpolation algorithm is utilized to determine the missing image edge points and calculate the centroid, and the least squares fitting method is adopted to fit the image edge points. Experimental results show that the system can be used for the hi gla-precision real-time measurement of hole on motorcycle frame. The absolute standard deviation of the proposed method is 0.026 7 mm. The proposed method can not only improve the measurement speed and precision, but also re- duce the measurement error.     

A personalized image retrieval based on visual perception

A new scheme named personalized image retrieval technique based on visual perception is proposed in this letter, whose motive is to narrow the semantic gap by directly perceiving user＇s visual information. It uses visual attention model to segment image regions and eye-tracking technique to record fixations. Visual perception is obtained by analyzing the fixations in regions to measure gaze interests. Integrating visual perception into attention model is to detect the Regions Of Interest （ROIs）, whose features are extracted and analyzed, then feedback interests to optimize the results and construct user profiles.     

Complete approach to automatic identification and subpixel center location for ellipse feature

To meet the need of automatic image features extraction with high precision in visual inspection, a complete approach to automatic identification and sub-pixel center location for similar-ellipse feature is proposed. In the method, the feature area is identified automatically based on the edge attribute, and the sub-pixel center location is accomplished with the leastsquare algorithm. It shows that the method is valid, practical, and has high precision by experiment. Meanwhile this method can meet the need of instrumentation of visual inspection because of easy realization and without man-machine interaction.     

A Reliable Method of Image Registration Based on Optical Flow Field and Feature Extraction

A novel method for solving the automated registration problems based on the global optical flow field and feature extracted from images is proposed. The method can be preformed with three steps. Firstly, the global optical flow field is computed, which provides a platform of background registration. Secondly, the optical flow field and the edge feature of moving target are integrated to segment the whole target. Finally, the registration of moving targets is finished through the transformation computation of perspective projection model. The simulation experiments for infrared images show that the method proposed is not accurate, but robust to motion of scene, which is ideally suited for the application of automated image registration.     

An Improved Algorithm for Image Edge Detection Based on Lifting Scheme

Wavelet transform is an ideal way for edge detection because of its multi-scale property, localization both in time and frequency domain, sensitivity to the abrupt change of signals,and so on. An improved algorithm for image edge detection based on Lifting Scheme is proposed in this paper. The simulation results show that our improved method can better reflect edge information of images.     

A new objective stereoscopic image assessment model based on stereoscopic perception

In order to establish a stereoscopic image quality assessment method which is consistent with human visual perception, we propose an objective stereoscopic image quality assessment method. It takes into account the strong correlation and high degree of structural between pixels of image. This method contains two models. One is the quality synthetic assessment of left-right view images, which is based on human visual characteristics, we use the Singular Value Decomposition （SVD） that can represent the degree of the distortion, and combine the qualities of left and right images by the characteristics of binocular superposition. The other model is stereoscopic perception quality as- sessment, due to strong stability of image＇s singular value characteristics, we calculate the distance of the singular values and structural characteristic similarity of the absolute difference maps, and utilize the statistical value of the global error to evaluate stereoscopic perception. Finally, we combine two models to describe the stereoscopic image quality. Experimental results show that the correlation coefficients of the proposed assessment method and the human subjective perception are above 0.93, and the mean square errors are all less than 6.2, under JPEG, JP2K compression, Gaussian blurring, Gaussian white noise, H.264 coding distortion, and hybrid cross distortion. It indicates that the proposed stereoscopic objective method is consistent with human visual properties and also of availability.     

A Novel Algorithm of Water Region Detection in SAR Image Based on Bag of Visual Words and Local Pattern Histogram

Water region detection based on SAR images is a difficult problem for its computing complexity.This paper proposes a novel water region detection method in SAR image of complex scenery.The algorithm takes advantages of Bag of visual words (BOV) to precisely describe the homogeneous region in complex scenery.Local pattern histogram (LPH) and single-class Support vector machine (SVM) are adopted to determine the edge information of water region precisely.The feature extraction is calculated block by block,which reduces computing workload and interference from noise.The experiments based on SAR images of real complex scenery show that the proposed method achieves higher accuracy and robustness.     

车型识别系统中利用阴影特点快速定位车体

阴影在车型识别系统中对于车体的准确定位产生很大的影响，为了能准确地定位车体，提出一种快速定位阴影与车体之间边界的算法即根据平行投影知识和原理的方法不定位阴影与车体之间的边界，同时利用该算法也能找出货车本身所具有的特征和华车的车头和车尾的定位，利用所寻找的特征点也可以进一步判断车所属的大致类型。利用该算法进行了大量的实验，从实验结果来看，该算法具有较好的稳定性和有效性。从该算法的计算时间来看也满足实时系统的需要。     

一种基于数学形态学和投影的车牌定位方法

车牌定位是车牌识别系统的关键步骤,定位的准确与否直接影响车牌识别的结果。为了能在复杂背景和不同光照条件下快速、准确定位车牌位置,本文根据车牌及拍摄效果等特点及因素,提出了一种结合数学形态学和水平、垂直投影法的定位方法。首先对图像进行预处理,利用数学形态学将预处理过的图像进行处理,滤去一部分伪车牌区域,然后利用水平和垂直扫描进一步精确定位车牌,最后结合车牌的先验知识最终确定车牌区域。实验结果表明,该方法定位率高,速度快,具有良好的鲁棒性。     

基于视觉感知模型修正的彩色图像边缘检测

在彩色图像边缘检测过程中,准确检测低照度区域的细节边缘是一个难题.提出了一种基于视觉感知模型修正的彩色图像边缘检测方法.由于视觉感知彩色空间中的色调分量具有圆形特性,导致伪边缘问题,利用色调距离计算色调图像的梯度以去除伪边缘,并根据饱和度的非线性函数控制色调分量的梯度,最后利用修正的视觉感知模型计算矢量梯度以获取彩色图像的边缘.实验结果表明,新的彩色边缘检测方法较现有方法能提高彩色图像的边缘检测精度,尤其对于低照度区域的细节边缘具有较好的响应.     

计算机多尺度辅助定位车牌算法

违法占道拍摄出的单帧车辆图像具有数据量大、时效性强,检测环境复杂等特点.对其检测需要花费大量的人力与物力.并且人们在定位过程中,无法避免因经验、疲劳等方面的干扰,导致遗漏和错误定位.为此本文从视觉感知角度提出计算机多尺度辅助定位车牌算法.模拟视觉感知原理,从车辆特征、纹理特征、颜色特征尺度,逐次聚焦至车牌所在区域.提出了完整的单帧图像车牌定位流程.并且提出基于边界对的车牌区域准确定位算法.通过对实拍的交通图像实验,表明本算法对于正对的车辆有较高的准确率,符合人类视觉感知的过程可实时的对图像进行车牌检测,可同时检测单幅图片的多个车牌.但对于光线过暗、过强或者颜色失真的情况,仍需要进一步的研究.     

基于相位信息与边缘投影的车体定位新算法

提出一种基于相位信息与边缘投影的车体定位新算法,先将RGB颜色模型与HSV颜色模型相结合,利用相位一致性检测出汽车图像中的车体边缘,再通过投影处理得到候选车体区域,然后对候选区域进行判别,最终得到车体的精确位置.实验结果表明,该算法鲁棒性强,受图像亮度、对比度和阴影的影响小,而且适用于包含多部车辆的图像,定位准确率高于其他常用算法.     

基于多尺度小波边缘检测的车牌定位研究

车辆牌照的准确定位是车牌识别系统中的关键步骤,利用车牌区域丰富的边缘和纹理信息以及车牌自身的特征,提出一种基于多尺度小波边缘检测的车牌定位方法.该方法能够更好地解决在复杂背景和复杂光照下的车牌定位.首先用图像增强和多尺度小波算子提取出车牌图像的边缘,然后利用数学形态学和连通区域标记的方法对车牌进行初步特征提取去除伪车牌区域,最后采用水平垂直投影法进行车牌的精确定位.实验结果表明,该方法能够实现车牌的快速准确定位,对复杂背景下的车牌具有很好的鲁棒性和实时性.     

基于纹理特征和颜色匹配的车牌定位方法

在分析车牌定位现有算法的基础上,根据车牌的特点,提出一种新的综合利用车牌纹理特征和边缘颜色对的车牌定位方法.首先根据车牌的纹理特征和结构特点进行粗定位,确定车牌的候选区域,然后对候选车牌区域进行边缘颜色对的检测,根据车牌背景与字符有固定颜色搭配的特点,确定车牌区域.实验结果表明,该算法能有效地对车牌进行定位,提高了车牌定位的可靠性.     

基于积分投影和Hough变换圆检测的人眼精确定位方法研究

眼睛定位是人脸识别的非常重要的一个环节,后续的图像几何归一化、图像矫正和特征提取都依赖于眼睛的位置。提出一种人眼快速定位的新方法。首先用积分投影法检测到眼睛瞳孔的大致位置;然后用Sobel边缘检测算子提取图像边缘信息;再利用基于圆的Hough变换快速定位出人眼,实验证明了本方法的有效性。     

一种基于图像处理和投影的车牌定位方法

为了提高车牌的定位率和准确性,通过对车牌特征的研究,提出一种采用颜色特征、图像处理和投影结合的定位方法。首先利用车牌的颜色特征将车牌可能区域从整幅图像中确定,再利用水平移差扫描,边缘检测对图像进行处理,并利用投影以及车牌的长宽比例相结合的方法,确定车牌具体位置。结果表明,此方法对蓝底白字车牌定位较准确,并且具有一定的鲁棒性和实时性。     

基于视觉感知和边缘保持的光照不变人脸识别

提出一种具有视觉感知特性和边缘保持特性的光照不变人脸识别方法.方法在各向异性扩散算法基础上引入视觉感知机制,提出具有视觉感知特性的图像梯度替代传统的图像空间梯度,使算法更符合人类视觉系统特性;同时,考虑到传统各向异性扩散算法采用的传递系数受参数影响较大,易产生明显的边缘锐化现象,提出一种新的传递系数,该系数不受参数影响,能够始终保持良好的边缘保持特性.新方法所获的光照不变人脸图像保持了良好的边缘,并极大程度上消除了光晕和白斑现象.在EYaleB和CMU PIE人脸图像库上的实验验证了该方法的有效性.