Fruit shape detection by level set

A novel approach for fruit shape detection in RGB space was proposed,which was based on fast level set and Chan-Vese model named as Modified Chan-Vese model(MCV) . This new algorithm is fast and suitable for fruit sorting because it does not need re-initializing. MCV has three advantages compared to the traditional methods. First,it provides a unified frame-work for detecting fruit shape boundary,and does not need any preprocessing even though the raw image is noisy or blurred. Second,if the fruit has different colors at the edges,it can detect perfect boundary. Third,it processed directly in color space without any transformations that may lose much information. The proposed method has been applied to fruit shape detection with promising result.     

A color based face detection system using multiple templates

A color based system using multiple templates was developed and implemented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the frontal human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution of human skin in chromatic color space in order to get a chroma chart showing likelihoods of skin colors. This chroma chart is used to generate, from the original color image, a gray scale image whose gray value at a pixel shows its likelihood of representing the skin. The algorithm uses an adaptive thresholding process to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates matching is used to determine if a given skin region represents a frontal human face or not. Test of the system with more than 400 color images showed that the resulting detection rate was 83%, which is better than most color-based face detection systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800 MHz) PC. Project (No. 60203013) supported by National Natural Science Foundation of China     

Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations

This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery, based on principal component analysis (PCA) and intensity-hue-saturation (IHS) transformations. The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component, respectively. The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation, and undertaking the inverse IHS transformation. The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image. On the basis of the visual and statistical analysis results of the experimental study, we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion. It has potential advantages in image mapping optimization, object recognition, and weak information sharpening.     

颜色和形状图像检索在邢台市智能交通的应用

给出颜色空间模型,阐述颜色特征的检索方法,并对直方图相交方法和其他方法的利弊进行了对比分析;在基于颜色特征的检索基础上,针对图像检索的另一个重要特征形状,提出了对轮廓特征的索引和对区域特征的索引;基于颜色特征和形状特征为邢台市智能交通系统设计了一个图像检索系统。     

基于MSURF算法的PCB图像配准

针对PCB板缺陷检测中用传统SURF算法进行图像匹配精度不高的问题,提出一种生产工序中运动平台机械误差先验信息与SURF特征提取相结合的MSURF配准算法。通过提取计算SURF特征点,求出对应特征点对的距离;在PCB运动平台机械误差分析的基础上,依据先验阈值边界条件筛除异常匹配特征点对;求出两幅图像满足最小二乘拟合准则的映射关系,并将其用于图像配准。在机械运动误差0.05~0.10 mm范围内对42 mm×42 mm的PCB图像配准实验,结果表明:提出的图像配准方法速度快、精度高,适用于产线PCB缺陷检测。     

基于HSI空间和颜色纯度的多尺度遥感图像分割算法

提出了一种新的考虑到HIS颜色空间和颜色纯度的多尺度遥感影像分割方法.使用HIS颜色空间来取得像素的颜色信息.当饱和度和亮度值较低时,色调值会非常敏感,用颜色纯度作色调值的加权值.不但考虑了像素的属性,还考虑了像素集合的属性.图像先被根据特定尺度分割成不同的区域对象,各个区域对象的平均值和方差值作为区域对象的属性.用区域对象特性和相对位置信息把小的对象合并到大的对象中.     

一种利用SURF特征自标定相机焦距的方法

鉴于SURF算法能快速而稳定地提取图像特征,提出一种基于SURF特征进行快速标定相机焦距的方法.相机模型采用单参数模型（即只有焦距未知）,先使用SURF算法得到图像匹配点对,这些匹配点对用于计算基础矩阵,然后根据Kruppa方程推导出一个二次方程,进而求解出焦距.实验结果验证了此方法的有效性和正确性.     

基于R-Y通道冗余去除钢筋层阴影方法

将RGB颜色空间下的图像转到YUV颜色空间,分离出R、Y两个通道,利用图像在R、Y两个通道内的阴影区域灰度值接近且钢筋区域亮度差距明显的特性,提出一种基于R-Y通道冗余去除的算法,以消除阴影。对比几种常用的阴影消除方法,该方法可以较好地实现在工厂环境下钢筋层空间重影去除。     

基于相位相关法与改进SURF算法的图像拼接方法

为避免相邻图像非重叠区域特征点被检测和提取,加速图像配准,提出一种基于相位相关法与改进SURF算法的快速图像拼接方法。该方法采用相位相关算法确定待拼接图像的重叠区域,限定SURF特征点检测、提取范围,用改进的SURF算法进行特征点匹配|然后根据MSAC算法剔除误配后的特征点匹配对,求取单应性矩阵,实现图像之间的快速配准|最后采用多波段融合算法对配准后的图像进行处理以消除拼接缝。实验结果表明,与传统算法相比,该方法可提高特征点匹配正确率,加速图像配准过程,完成拼接图像间的平滑过渡。     

基于改进 SURF 的快速图像匹配算法

SURF 是在 SIFT 基础上提出的一种图像特征点提取算法。针对传统算法误匹配点多和计算量大等问题,提出一种基于改进 SURF 的快速图像匹配算法。该算法通过引入对角降维与角度删减方法,分别对 SURF算法中特征点描述子进行降维和误匹配点剔除,以提升匹配速度和精确度。实验结果表明,与传统算法相比,该算法提高了 1%~10%的匹配正确率,以及 8%~30%的效率。     

Depth-aided inpainting for disocclusion restoration of multi-view images using depth-image-based rendering

A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.     

一种新颖的数字图像篡改检测方法

检测数字图像复制—粘贴型篡改是目前研究热点之一。多数复制—粘贴篡改检测方案只对刚性平移篡改具有较好的检测精度,无法有效应对更复杂的几何变换和常规信号攻击。为优化数字图像复制—粘贴篡改检测效率和精度,首次提出一种使用极坐标复指数变换（PCET）与一致性敏感哈希（CSH）的高效检测算法。首先,计算滑动窗口 PCET 系数,将其作为不变的图像局部特征|然后,根据这些特征,利用 CSH 算法快速、精确地匹配大量密集分块|最后,使用基于密集线性滤波的后处理算法消除匹配结果中的错误匹配并定位重复区域,得到最终检测结果。根据实验结果可知,该算法不仅检测精度平均提高 12.06%,而且处理时间平均缩短315.64s。因此利用对几何变换和常规信号攻击鲁棒的 PCET 系数刻画图像局部特征,并采用基于图像一致性的快速高精度匹配算法 CSH,可有效优化复制—粘贴篡改检测精度和效率。     

A flower image retrieval method based on ROI feature

INTRODUCTION There are about 250000 named species offlowering plants and many plant species that havenot been classified and named. Plant classificationand identification is a very old field. So far, thistime-consuming process has mainly been carriedout by taxonomists and/or botanists. A significantimprovement can be expected if the plant identifi-cation can be carried out by a computer automati-cally or semi-automatically with the aid of imageprocessing and computer vision techniqu…     

基于改进自适应TV模型的全自适应去噪算法

为使保边性更好的去噪模型在邻近图像边缘处得到应用,同时在图像平坦处得到平滑性更好的模型,解决选择单一的正则项系数对图像处理结果造成的不良影响,有效地自适应选取正则项系数,采用基于小波变换的图像分割技术将图像分为边缘区域和平坦区域,通过修改TV模型的数值解法和ROF算法,实现自适应正则项系数算法。实验结果表明,改进的模型和自适应正则项系数选取算法得出的图像,均方误差为56.69,峰值信噪比为31.00,平均相似度为0.84。改进的TV模型在图像去噪效果方面优于原模型,自适应正则项系数算法能够弥补原图像去噪方法的不足。     

A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition.In this paper,we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images.For flower retrieval,we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets,Centroid-Contour Distance(CCD)and Angle Code Histogram(ACH),to characterize the shape features of a flower contour.Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions.Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest(ROD based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard(1991)and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).     

基于颜色和情感语义特征的感性图像检索

针对图像底层视觉特征与高层情感语义间的语义鸿沟,分析色彩与情感的关系,建立情感空间,采用概率神经网络建立图像颜色特征与情感空间的映射,融合图像底层视觉特征和人类情感特征,提出并实现了一种感性图像检索方法。实验结果证明了该方法具有较好的效果,为图像情感语义的研究提供了一种行之有效的解决方案。     

一种改进的多混沌系统彩色数字图像加密方案

提出一种改进的基于Logistic映射和Lorenz映射相结合的彩色图像加密方法。将三维空间的彩色图像转换成二维的灰度图像,分别利用Logistic、Lorenz混沌系统生成的混沌序列对二维图像进行像素位置与像素值的置乱,对置乱后的图像按折叠算法进行处理。Matlab仿真实验结果可知,转换成二维灰度图像后有效地降低了算法的计算强度和空间需求;置乱时实现了不同层次（R、G、B）亮度值的完全混合,取得了较好的加密效果,安全性更高。     

两幂等变换值域与核相等问题研究

幂等变换的值域与核在线性空间的直和分解中有着重要应用.文章对同一线性空间上两不同幂等变换的值域与核相等问题展开讨论,给出了一个两者相等的充要条件,并把该充要条件推广到p次幂等变换上来,同时得到两幂等变换核与值域之间对应相等的充分条件,并在更一般的条件下,给出了两幂等秩线性变换值域与核对应相等的一个必要条件。     

基于特定理论工具的彩色图像分割技术

图像分割是对图像进行分析和理解的基础，彩色图像由于较灰度图像具备更多的视觉信息而越来越受到人们的关注．对彩色特征空间的选取，以及近年来通过结合模糊数学、马尔可夫随机场、神经网络、遗传算法、小波分析等特定理论工具和模型的彩色图像分割方法和策略进行了介绍．     

基于改进细菌觅食优化的彩超图像增强算法

受彩色多普勒超声影像工作设备的限制,彩超图像作为医(疗)学辅助证据一般采用传统的算法(诸如空域增强中的直方图变换或频域增强中的小波变换)进行增强,而直方图变换后的彩超图像失真度过大,小波变换的参数调试更为繁琐。为此,本文提出了一种改进细菌觅食优化的图像增强算法。首先将彩超图像转到HSV颜色空间中,而后利用熵视觉显著性机制对亮度分量进行处理,以便获得显著性点集,将其作为细菌觅食优化初始菌群的候选集合,根据随机佳点理论抽选出合适的菌群点集后,对不完全Beta函数中的两个调节因子进行寻优,确定最优参数,最后将增强后的亮度分量整合色调分量和饱和度分量,并转回到RGB颜色空间中,得到增强后的彩超图像。文中的数据来源于某医院彩超诊断室,经MATLAB仿真测试后,证明所提算法同模因及杜鹃优化算法相比,峰值信噪比平均提高了7.29分贝,且具有良好的主观分辨效果。