一种多传感器图像的彩色融合系统

针对像素及多传感器图像的融合问题,设计了一种多传感器图像的彩色融合系统。该系统首先采用Laplacian金字塔变换算法得到灰度融合图像,然后产生共有图像、差异图像和细节增强图像,最后将灰度融合图像、细节增强图像,分别送入RGB通道得到彩色融合图像。实验结果表明,本系统可以有效地对多传感器图像进行彩色融合。根据熵和颜色丰富度两个客观评价指标,本系统的彩色融合图像更清晰,更容易识别目标,包含更多的细节信息,图像信息也更加丰富。     

一种有效的水下图像分割算法

水下图像具有信噪比低、边缘模糊等特点，直接使用传统分割方法对水下图像进行处理，效果较差，存在边缘扩大、轮廓变形等缺点。为提出新的抗噪性能好、能够克服水下成像过程中非线性影响的算法，文中在模糊理论的基础上，结合用熵的概念，提出了一种能根据图像自身特点自适应选择变换参数、使图像分割效果达到最佳的算法。通过对水下图像处理实验证明，该算法对简单背景的水下图像分割是有效的。和传统分割方法相比，该算法具有更强的自适应性和抗噪性能。     

一种矿井图像增强算法

针对煤矿井下视频监控系统采集图像对比度低、光照不均、伴有大量噪声等问题,提出一种基于加权引导滤波同步去噪的单尺度Retinex算法对矿井图像进行增强。该算法首先采用加权引导滤波代替单尺度Retinex算法的高斯滤波对图像的低频分量进行照度估计,然后采用加权引导滤波对图像的高频分量进行去噪处理,最后由对数域转换到实数域得到增强后的图像。通过主观视觉效果和客观质量评价对该算法进行验证,结果表明该算法较传统图像增强算法可获得更好的图像视觉效果,且图像处理速度更快。     

结合NSCT和图像形态学的路面裂缝检测

路面图像的复杂性及裂缝信息的弱信号性导致对路面裂缝进行检测非常困难,为此提出一种基于非下采样contourlet变换(NSCT)和图像形态学的路面裂缝检测算法.首先对图像进行NSCT得到不同尺度、不同方向上的变换系数,在NSCT域中根据变换系数自适应地确定阈值,并应用广义非线性增益函数来增强较弱细节的局部对比度;然后对增强处理后的变换系数进行反变换;最后用图像形态学方法和中值滤波实现裂缝检测及孤立噪声点去除.通过对实际的高速路面图像测试表明,与直方图增强、小波变换及contourlet变换相比,该算法能更有效地增强弱对比度的细小裂缝,克服了常规算法易受离散噪声点以及光照条件等干扰的问题,具有较强的鲁棒性且高效实用.     

基于GPRS无线传输的便携式图像监控系统

考虑到监控系统的方便与实用，提出了一种基于嵌入式单板电脑所开发的便携式图像监控系统。该系统以普通的USB摄像头作为图像采集设备，对比目前以CCD摄像头和采集卡作为采集设备的监控系统，体积大大缩小。通过一种有效的图像异常检测算法，对实时图像进行检测，有异常发生时，通过GPRS模块进行无线传输，将异常图像发送到远端，达到远程监控的目的，实验结果表明该系统实时性好，异常报警准确率高，实用性强。     

一种实用的图像加密算法

秘密图像信息网络传输的前提是对它进行加密处理。讨论了混沌系统和Blum—Blum—Shub发生器的特性，利用它们生成位置置乱矩阵和系数扰动矩阵，通过它们对图像的小波变换系数进行修改实现图像的加密。通过实验证明，该算法是一个有效的、实用的图像加密算法。     

一种新颖的基于遗传算法的正则化图像插值方法

传统的图像插值方法，包括零阶插值或最近邻插值、双线性插值、立方样条插值等，是先经补零疏化、后经内插滤波实现的。由于这些内插滤波器不能完成理想的低通滤波功能，传统插值图像会增加一定的虚假内容，即导致方块效应、模糊等。另外．由于内插滤波器是确定的，因而这些插值算法就缺乏利用图像本身信息的机制。为了提高插值图像的质量和增强图像的分辨率，首次提出了一种基于遗传算法的正则化图像插值方法。在该遗传算法中，编码采用实值方式，变异采用“引导”方式，适应度评价函数的正则化项采用图像质量评价的一些客观标准。最后，还分析了在遗传算法中怎样直接调整正则化系数，并将实验获得的该正则化系数应用于相同实验条件下的迭代正则化插值算法中，取得了良好效果。实验证明，该方法实用、可行。     

全景图像生成算法的研究与实现

针对虚拟现实中的柱面全景图生成问题提出了一种简单实用的算法。该算法通过对图像重叠部分的相交特征线段进行提取和匹配来实现相邻图像的拼接。在此基础上编制了全景图生成工具VR-Maker，并取得了良好的实验结果。     

基于BIRCH聚类加速的彩色图像增强算法

针对现存的大多图像增强算法增强的图像可见性丢失问题,提出了一种基于BIRCH聚类加速的彩色图像增强算法;首先,通过BIRCH聚类加速确定数据库中与输入图像直方图相似度最高的图像来提取图像特征;然后,选择最小欧氏距离的特征值进行图像融合以获取目标图像;最后,增强图像通过目标图像直方图规范化和后期处理获得;大量图像融合实验结果验证了算法的有效性,该算法扩展了图像增强的类别,解决了增强过程中可能出现的可见性丢失问题,使图像增强的适应性更强;另外,EM、CII和SSIM评估指标的结果表明该算法明显改善了增强效果。     

数字CR医学图像自适应增强算法研究

数字CR(Computed Radiography)医学放射图像以其高灰阶分辨率、强大的计算机图像后处理功能、小辐射剂量、无胶片诊断、异地会诊等优势,已成为医学成像技术新的热点。然而在成像过程中,由于人体结构和组织的复杂性以及成像系统中的X线散射、电器噪声等各种不利因素的影响导致图像质量的下降,主要表现为细节模糊、对比度差,要对其进行增强处理以改善其视觉质量,便于医生更准确地诊断。而目前通用的CR图像增强方法对比度和噪声增强过度,丢失细节,为此提出一种基于邻域标准差与均值之比自适应增强算法。算法能根据CR图像的邻域标准差与均值之比来调节增强程度的加权因数k,从而自适应的增强CR图像的边缘细节。实验证明,该算法处理后的CR图像细节丰富,信噪比高,具有良好的视觉效果,是一种有效的适合CR医学放射图像的自适应增强算法。     

A simplified visual inspection system using a personal computer

For a practical visual inspection system, data processing in real time, flexibility for variety of products, and cost performance have been required. This article describes a simplified visual inspection system that has well-balanced performance that meets these requirements. Inspection data processing by software makes the inspection system flexible. Development of a simplified pattern matching method based on limiting the inspection within the area pointed by an user and direct memory access of binary image data performs a high speed inspection function. By using a personal computer as a processor of this system, a cost effective inspection system is realized.     

基于区域划分的快速图像插值算法

图像插值是将低分辨率图像放大后提高视觉效果的有效方法,传统算法中有较简单且算法复杂度小的方法,但插值后的图像常常有锯齿边缘或者效果模糊,因而实际应用并不广泛。为克服以上缺陷,提出了一种先将图像进行区域划分,然后再进行快速图像插值的算法,既保证了算法较低的复杂度,又优化了图像显示效果,适合实际应用。     

Feel the image: The role of emotions in the image-seeking process

Images are characterized by a complex system of attributes. One of the most elusive properties of an image is its emotiveness – the ability to trigger an emotional reaction in the viewer. Prior research has demonstrated that users react to image emotiveness, yet to date there is little theoretical understanding and scarce empirical evidence as to the role played by emotiveness in the image retrieval process. Our study aims to fill this gap and investigates how users perceive the emotional content of the image-seeking task; explicate this emotional content in search keywords; and react to image emotiveness in selecting relevant images. Using a multimethod approach that combines quantitative and qualitative analyses, we performed three experiments where participants searched for images, both within the predefined set of images and in an open Web environment. Our findings suggest that although seekers rarely explicate image emotiveness in search keywords, their decision to select relevant images is largely dependent on their perception of the emotional content of the task and images. We discuss implications for research on emotions in image retrieval, as well as practical implications for designers of search systems.     

基于嵌入式的近红外人脸识别系统设计

设计并实现了一套基于达芬奇平台OMAP3530的近红外人脸识别系统。本系统采用850nm的LED灯提供主动的近红外光源,在OMAP3530和EPM570处理器的协同作用下,实现了可见光图像与近红外图像的实时采集与处理。软件设计基于Codec Engine架构,ARM端负责图像的采集、识别结果的显示和数据库的管理;DSP端专注于人脸识别算法。通过图像采集、人脸检测、特征提取和特征匹配4个步骤实现人脸识别。本系统充分利用了OMAP3530丰富的接口和强大的图像处理能力,并且经过了C与汇编语言的优化。当环境光强发生变化时,系统仍能获得较高的识别精度和较好的实时性。     

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.     

云存储的可视门铃监控系统的设计

针对传统的视频监控系统将数据保存在电脑硬盘上,电脑硬盘损坏导致数据丢失的缺点,设计了一种基于云存储的可视门铃监控系统.系统以S3 C2440A处理器和嵌入式Linux为核心平台,将CMOS摄像头采集的视频信号通过网络传输到PC机进行处理,运用混合高斯背景建模算法实现运动目标的检测,把视频图像上传到百度云存储.局域网内用户可连接PC机获取视频图像,局域网外用户可访问百度云获取视频图像.实验结果表明,该系统具有安全性高、实时性好等优点,具有广阔的应用前景.     

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.     

Depth-guided asymmetric CycleGAN for rain synthesis and image deraining

Based on supervised learning, most of the existing single image deraining networks are trained on paired images including one clean image and one rain image. Since it is difficult to obtain a sufficient number of paired images, most of the rain images are manually synthesized from the clean ones. However, it costs huge time and effort, and requires professional experience to mimic the real rain images well. Moreover, the superior performance of these deraining networks trained on manually synthetic rain images is hard to be maintained when tested on real rain images. In this work, to obtain more realistic rain images for training supervised deraining networks, the depth-guided asymmetric CycleGAN (DA-CycleGAN) is proposed to translate clean images to their rainy counterparts automatically. Due to the cycle consistency strategy, DA-CycleGAN can also implement the single image deraining task unsupervised while synthesizing rain on clean images. Since rain streaks and rain mist vary with depth from the camera, DA-CycleGAN adopts depth information as an aid for rain synthesis and deraining. Furthermore, we design generators with different architectures for these two processes due to the information asymmetry in rain synthesis and deraining. Extensive experiments indicate that the DA-CycleGAN can synthesize more lifelike rain images and provide commensurate deraining performance compared with the state-of-the-art deraining methods.

     

A variational framework for multiregion pairwise-similarity-based image segmentation

Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley Segmentation Data Set by comparing its performance with other segmentation techniques.