基于人脸检测和颜色分析的红眼自动消除

用闪光灯进行人物摄影时由于眼底血管的反光会产生红眼现象,小型照相机的闪光灯和镜头距离很近,这一问题更为突出。为了自动消除用闪光灯进行人物摄影时产生的红眼现象,在对基于颜色空间的常用红眼检测技术进行研究的基础上,对现有较为复杂的检测方法进行了修正,提出了一种基于人脸检测的红眼自动消除方法,并在分析的基础上得出一组先验参数用于识别和处理,从而大大降低了人眼定位的复杂度。实验表明,该方法能快速有效地消除普通数字照片中的红眼现象。     

红眼照片自动检测和修复方法

针对家庭数字照片出现的红眼问题给出了解决方案。过程如下：(1)找出红眼的HSI色彩模型；(2)自动检测出所有可能红眼区域；(3)自动过滤无效区域，找出真正红眼区域；(4)利用红眼周围的颜色特征和红眼的颜色特征进行减色修复；(5)在修复的结果上进行后续处理，使之看起来更真实。本方法已经用Visual C 在Windows2000环境下实现，程序运行速度快，对家庭照片的红眼修改效果好。     

基于红眼效应的人眼探测

传统的立体显示系统中,观看者需要配戴头盔、眼睛等辅助设备才能欣赏到立体效果,但是这些辅助设备也给观看者带来诸多不便。相比之下,自由式立体显示系统的优点在于观看者能够观看到立体效果的同时,并不需要配戴头盔、眼睛等辅助设备。为了能自由地观看立体效果,需要根据观看者的视点调整系统,使得立体图像能够分别进入观看者的左右眼,因此,人眼位置的探测是立体显示技术的重要组成部分。文章提出了一种基于红眼效应的人眼探测方法。算法利用瞳孔在近轴和远轴红外光分别照射下的不同状态,得到眼睛的候选区域。在对候选区域进行判别的时候,算法介绍并比较了主成分分析以及基于概率的主成分分析法。实验结果表明基于红眼效应的探测方法具有较高的准确率和实时性。     

彩色图像中多姿态的人脸快速检测及定位

针对复杂背景下的彩色图像,提出了一种基于肤色模型、数学形态学处理、似眼物检测以及眼睛与嘴巴构成三角形特征的人脸检测算法。该方法首先根据肤色模型检测出肤色区域;快速提取肤色区域的边界并分割成一系列肤色块;用数学形态学的膨胀和腐蚀方法分别对各个肤色块进行处理,消除边界毛刺和回旋,填补空洞,同时也避免了不同肤色块的连通;用简单的圆形模板检测出似眼物,然后根据眼睛与嘴巴构成三角形的特征判断是否为人脸。     

基于相位相关的图像区域复制篡改检测与定位

针对区域复制篡改检测算法检测效率低问题,提出一种基于相位相关的检测与定位算法。算法首先对图像进行滑窗式分块,滑动步长为多个像素,通过对图像块灰度均值排序筛选出可能匹配的图像块; 然后,利用相位相关的互功率谱脉冲函数峰值确定两个图像块是否匹配以及二者的相对位移; 最后,通过统计匹配块对偏移距离方法消除误匹配,并对篡改区域进行标记。实验结果表明,该算法不仅检测效率高,而且对有损压缩、平滑滤波、噪声等常见的后处理操作具有鲁棒性。     

基于SURF的连续帧图像配准及高光去除

物体上的高光直接影响工业检测、模式识别和计算机视觉等领域中后续处理的算法性能。如何检测和消除图像中的高光区域一直是个热点问题。这里介绍了一种基于SURF的连续帧图像配准及高光去除的方法。首先,利用SURF特征检测及其特征描述方法,对连续帧图像进行自动配准;其次,在连续帧图像配准后,对图像进行融合;最后,输出去除高光的图像。实验表明:该方法用于消除或消弱高光区域有比较好的效果,有一定的理论和应用价值。     

复杂环境下的运动车辆检测

针对目前采用的车辆检测方法的优缺点,提出了一种新的车辆区域检测方法,能够消除阴影干扰。该算法首先运用选择性背景更新法进行背景相减,获取感兴趣区域,然后提出基于图的区域分割算法,对感兴趣区域进行再分割。该方法充分考虑了视频图像全局和局部的空间信息,根据分割区域的大小自动自适应地调节对图像局部细节的忽略程度,从而获取局部区域像素信息较为一致的分割块。最后基于分割过程中所具有的马尔科夫属性,运用条件随机域的方法建立分割后验概率分布,求取最大后验概率确定标号,并对具有相同标号的相邻分割进行合并。     

基于车载激光雷达的机车可通行区域检测

可通行区域检测是自动驾驶环境感知的重要内容之一.为了提取自动驾驶车辆前方可通行区域信息,文章提出一种基于车载激光雷达的机车可通行区域实时检测算法.该算法首先对激光雷达采集的三维点云进行数据预处理以提高点云质量;然后通过局部区域搜索算法提取轨道点云,并采用随机采样一致性算法拟合轨道曲线;接着使用匹配规则对轨道进行配对,以...     

基于最近邻链的车牌检测算法

根据车牌字符的几何特征和空间排列规则,提出了一种基于最近邻链的自适应车牌检测算法. 首先,采用自适应阈值分割算法消除光照变化的影响,并采用连通体分析方法消除部分干扰目标; 其次,根据车牌字符连通体的区域特征,将宽度和高度都相近的连通体构造为最近邻连通体对, 并将最近邻对连接为最近邻链,从而检测出所有可能的车牌区域; 最后,利用两组不同长度的方波模板分别对车牌的水平和竖直投影进行匹配, 它能够验证候选车牌区域的有效性,并求解所有车牌字符的最佳切分位置. 实验表明,该算法能够自适应地处理光照不均匀、尺度变化、透视失真、背景干扰以及质量退化等因素的影响, 可以有效地检测出复杂背景中的车牌区域.     

基于层次约束的2维动画关键帧插值算法

2D动画制作是一个劳动密集型的过程,如何自动进行关键帧插值是研究的难点。关键帧插值的核心环节是2D形体渐变,现有2D形体渐变算法大多针对闭合的单一多边形,在应用到包含多个区域的复杂形体时,可能出现区域之间的重叠。本文算法获取输入关键帧的封闭区域,并迭代计算区域间的匹配关系;对每个子区域建立相对于父区域的局部坐标系,施加层次间的约束关系进行插值,以消除父子区域插值过程中“运动”的不匹配现象。实验结果表明,该算法合成结果自然光滑,具有一定的实用价值。     

Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision

This work presents a new algorithm designed to detect clouds in satellite visible and infrared (IR) imagery of ice sheets. The approach identifies possible cloud pixels through the use of the normalized difference snow index (NDSI). Possible cloud pixels are grown into regions and edges are determined. Possible cloud edges are then matched with possible cloud shadow regions using knowledge of the solar illumination azimuth. A scoring index quantifies the quality of each match resulting in a classified image. The best value of the NDSI threshold is shown to vary significantly, forcing the algorithm to be iterated through many threshold values. Computational efficiency is achieved by using sub-sampled images with only minor degradation in cloud-detection performance. The algorithm detects all clouds in each of eight test Landsat-7 images and makes no incorrect cloud classifications.     

Generalized Perpendicular Bisector and exhaustive discrete circle recognition

This paper presents a generalization of the notion of circumcenter as the intersection of perpendicular bisectors. We define Generalized Perpendicular Bisectors between two regions as an area where each point is the center of at least one circle crossing both regions. This allows us to determine all the possible discrete circle centers that cross a given set of pixels. The possible radii can then easily be determined. This exhaustive digital circle parameter computation is adapted to various types of circles/digitization schemes such as Naïve, Pythagorean and standard/supercover circles.     

A novel segmentation algorithm for nucleus in white blood cells based on low-rank representation

White blood cells (WBCs) segmentation is a challenging problem in the study of automated morphological systems, due to both the complex nature of the cells and the uncertainty that is present in video microscopy. This paper investigates how to boost the effects of region-based nucleus segmentation in WBCs by means of optimal thresholding and low-rank representation. The main idea is firstly using optimal thresholding to obtain the possible uniform WBC regions in the input image. After that, a manifold-based low-rank representation technique is employed to infer a unified affinity matrix that implicitly encodes the segmentation of the pixels of possible WBC regions. This is achieved by separating the low-rank affinities from the feature matrix into a pair of sparse and low-rank matrices. The experiments show that the proposed method is possible to produce better segmentation results compared with existing approaches.

     

Edge and region analysis for digital image data

In this paper we provide a unified view of edge and region analysis. Our framework is based on the sloped-facet model which assumes that regions of image segments are maximal areas which are sloped planes. Edge strength between two adjacent pixels is measured by the F statistic used to test the significance of the difference between the parameters of the best-fitting sloped neighborhoods containing each of the given pixels. Edges are declared to exist at locations of local maxima in theF-statistic edge strength picture. We show that this statistically optimum procedure in essence scales the edge strength statistic of many popular edge operators by an estimate of the image noise. Such a scaling makes optimum detection possible by a fixed threshold procedure.     

Pixel-selected ray tracing

An acceleration method based on an idea that T. Whitted (Commun. ACM, vol.23, no.6 pp.343-349, June 1980) presented on ray tracing is discussed. He proposed making antialiased images by hierarchical adaptive oversampling. The present authors use hierarchical adaptive undersampling to reduce the number of pixels whose intensity must be calculated by ray tracing. To implement pixel-selected ray tracing (PSRT), homogeneous regions in images must first be found. Generally, adaptive undersampling can result in some image-quality defects, because small objects and parts of thin or wedge-shaped objects may disappear when they are located between the initially sampled pixels. PSRT has an improved algorithm that uses pixels with the correct object information from among the sampled pixels to find pixels with erroneous color and correct them. Moreover, PRST uses ray-object intersection trees for precise classification of the homogeneity of regions and for fast intensity calculation in homogeneous regions. Experimental results are presented. They show that PSRT is two to nine times faster than standard ray tracing     

Color Image Segmentation for Multimedia Applications

Image segmentation is crucial for multimedia applications. Multimedia databases utilize segmentation for the storage and indexing of images and video. Image segmentation is used for object tracking in the new MPEG-7 video compression standard. It is also used in video conferencing for compression and coding purposes. These are only some of the multimedia applications in image segmentation. It is usually the first task of any image analysis process, and thus, subsequent tasks rely heavily on the quality of segmentation. The proposed method of color image segmentation is very effective in segmenting a multimedia-type image into regions. Pixels are first classified as either chromatic or achromatic depending on their HSI color values. Next, a seed determination algorithm finds seed pixels that are in the center of regions. These seed pixels are used in the region growing step to grow regions by comparing these seed pixels to neighboring pixels using the cylindrical distance metric. Merging regions that are similar in color is a final means used for segmenting the image into even smaller regions.     

Segmentation of remotely-sensed images by a split-and-merge process+

Abstract

This paper describes the application of an image segmentation technique to remotely-sensed terrain images used for environmental monitoring. The segmentation is a preprocessing operation which is applied prior to image classification in order to improve classification accuracy from that achievable by classifying pixels individually on the basis of their spectral signatures. The method uses a split-and-merge technique to segment images into regions of homogeneous tone and texture wherever this is possible. The split-and-merge technique employs a hierarchical quadtree data structure. Texture is measured using easily computed grey value difference statistics. The homogeneity criteria employed in region merging are dependent on local statistics. The segmented image is classified using a region classifier for regions and the normal per-pixel classifier for single pixels in areas of inhomogeneity. The technique is illustrated by example classifications of aerial Multispectral Scanner data from two test sites. A quantitative analysis of the performance shows that an increased classification accuracy is achieved.     

改进的多尺度Retinex算法及其应用

在经典的多尺度Retinex算法中对Retinex输出采用一个常数增益,使图像在平滑区域和高对比度边缘出现过增强,导致噪声放大和边缘晕环。针对该问题,提出改进MSR算法,对Retinex输出采用自适应空间变化增益,平滑区域和高对比度边缘增益小,细节区域增益大,并且小尺度Retinex输出不同区域增益差大,而大尺度Retinex输出不同区域增益差小,从而使图像细节更清晰,同时场景轮廓和颜色呈现更自然。将该算法用于受到严重退化的雾天图像,能取得较好的图像去雾效果。     

Segmentation of single- or multiple-touching handwritten numeral string using background and foreground analysis

An approach of segmenting a single- or multiple-touching handwritten numeral string (two-digits) is proposed. Most algorithms for segmenting connected digits mainly focus on the analysis of foreground pixels. Some concentrated on the analysis of background pixels only and others are based on a recognizer. We combine background and foreground analysis to segment single- or multiple-touching handwritten numeral strings. Thinning of both foreground and background regions are first processed on the image of connected numeral strings and the feature points on foreground and background skeletons are extracted. Several possible segmentation paths are then constructed and useless strokes are removed. Finally, the parameters of geometric properties of each possible segmentation paths are determined and these parameters are analyzed by the mixture Gaussian probability function to decide the best segmentation path or reject it. Experimental results on NIST special database 19 (an update of NIST special database 3) and some other images collected by ourselves show that our algorithm can get a correct rate of 96 percent with rejection rate of 7.8 percent, which compares favorably with those reported in the literature.