基于感兴趣区域的图像检索方法

为了弥补全局特征在描述图像内容上的不足，近年来许多研究者提出了基于区域的图像表示和检索方法。本文提出了一种基于感兴趣区域的图像检索方法，根据认知心理学中选取注视焦点的模型来提取感兴趣区域，利用感兴趣区域的视觉特征和位置关系进行图像检索。该方法既利用了区域的局部特征，又考虑了区域在重要程度上的差别。在60000幅通用图像库上的实验结果表明，这种方法的检索效率要好于现有的基于区域的方法，而义对查询图像的噪声等扰动具有鲁棒性。     

基于人眼视觉特性的医学图像处理方法

在数字化X光影像仪中,医学图像经处理后供医生进行诊断用。由于图像信息是通过人的视觉来获取的,所以在图像处理中,应充分考虑人眼的视觉特性。从人眼视觉的生理特性和人的心理特性出发,对图像的灰度分辨能力和图像结构的敏感性两方面进行了探讨和测试。根据测试结果总结了视觉的有关规律,建立了相应的数学模型和具体实现方法,并实际应用于X光图像处理系统中,提高了图像质量,使人眼理寻获得所需信息,增加了医生诊断的准确性。     

基于感兴趣区域的彩色三维物体快速喷绘方法

目的 针对目前三维空间喷绘效率低及个性化不足问题,提出一种基于感兴趣区域再分割的三维物体快速喷绘方法.方法 首先利用感兴趣区域算法提取出彩色图像中最显著部分,再通过一种新灰度图再分割算法进行二次图像处理,得到具有不同显著性区域等级的图像,将带有区域分级图像和原图像进行合并,得到具有区域分级的彩色图像.然后通过三维纹理映射方法,将后得到的彩色图像映射到目标三维模型,三维模型的表面就带上所需的纹理信息.再利用一种新的多喷头控制技术,合理地安排不同的喷头处理不同的区域.最终通过信号传输,将打印文件传输到机械臂,机械臂进行工作喷绘.结果 该三维喷绘方法对半径为100 mm球体的工作总时间为100.34 s,具有较高的工作效率.结论 相较于传统的三维喷绘,基于传统的感兴趣区域的喷绘所提出的基于感兴趣区域再分割算法,在保证喷绘精度的前提下显著提高了喷绘效率,在性能指标上总体优于传统的感兴趣算法.     

二维多光束干涉图特征信息的提取

较详细地阐述了一套新的运用于激光波长测量中对图像数据中特征信息的提取方法。由于是二维干涉图进行处理。所以处理过程基本上分为图像预处理阶段的图形增强和二值化、条纹图形细线化的预处理及细线化,以及图形标注和数据处理,与其它方法相比,采用这种处理方法,测量精度高,在测量速度方面也能满足实时处理的要求。     

Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image classification will then be implemented by combining a supervised learning model with a support vector machine. The proposed framework is developed based on MATLAB with a graphical user interface. According to the experimental results, the proposed framework can achieve 98.39% accuracy for classifying diseased and healthy leaves. Moreover, the framework can achieve an accuracy of 98.07% for classifying diseases on pomegranate leaves.     

指纹图像预处理方法的研究

针对指纹图像降质带来的困难，提出基于指纹结构特征的某些合理假设，在此基础上设计要应的处理算法，提出了增强指纹图像对比度、提取指纹有效区域、根据方向信息分割图像以及去除图像中气泡噪声的算法等。实施证明，这些算法处理效果好、运行速度快、编程简单，有效地解决了降质图像的预处理问题。     

具有分割健壮性的超声图像分类方法探索

在早期癌症的发病表现中,肿块和微小钙化点是重要的特征和诊断依据.然而,病变可疑区域与正常组织区域之间的差别很小,并且肿块的边界和内部结构在超声图像上形态不一,与周围组织的差别也很细微,很难用人眼检查到,这样使得早期确诊变得比较困难.本文采用一种基于模糊数学理论的乳腺超声图像增强算法,经过增强处理之后,原先由于图像的模糊而隐藏的信息能够清晰的观察到,这也后后面的肿瘤的分割与分类打好基础.     

一种快速检测光滑半球表面缺陷的方法

针对具有镜面反射特性的光滑球面,提出了一种利用反光带的镜像变形来对表面缺陷进行快速检测的新方法。这种方法通过漫反射反光带增强图像上的缺陷信息,克服了背景噪声在图像处理中会造成较大误差的缺点,也降低了照明系统的设计难度。根据缺陷引起的反光带镜像变形获取缺陷特征,使图像处理软件的工作量大大减少。通过实验,给出了缺陷尺寸和扫描间隔等因素对检测结果影响的分布曲线,为合理提取缺陷尺寸提供了参考。实验表明,该方法的鲁棒性好、效率高,能识别0.5mm以上的全部缺陷,定位误差在 1之内。     

A new semi-blind watermarking algorithm using ordered Hadamard transform

An efficient and fast watermarking algorithm based on ordered Hadamard transform (HT) is proposed. Since it uses a few information of watermark image in secret key, it is categorised as a semi-blind method. In our algorithm, both host and watermark images are decomposed into non-overlapping blocks. Next, they are mapped into transform domain as Hadamard coefficients. Then, several high frequency AC components of host image are substituted with watermark coefficients without using any strength factor. Not employing this factor guarantees the embedded image to be retrieved with primary quality. Moreover, using HT offers several advantages of fastness, higher image fidelity, greater reliability of watermark detection and higher data hiding capacity. The experimental results show that our method keeps the quality of host image and is robust to common attacks. Compared to frequency domain and hybrid methods, it has advantages of shorter processing time, acceptable robustness and ease of hardware implementation.     

利用粗糙集和属性直方图的图像增强方法

利用粗糙集理论进行图像增强,子图的划分是关键。属性直方图是对直方图概念的推广,是一种由先验知识约束的直方图;将它用于子图的划分,在此基础上提出了一种基于粗糙集理论和属性直方图的图像增强方法。该方法利用属性直方图的 Otsu 算法确定灰度阈值,根据灰度阈值利用不可分辨关系,将图像划分为背景子图、目标子图和噪声子图,对去噪后背景子图和目标子图进行增强变换,并将它们合并得到增强图像。将该方法用于一种海底小目标图像增强。实验结果表明该方法处理增益为 11dB,明显地增强了图像,且不损害图像的边缘。该方法适用于图像有某种先验知识的场合。     

Digital Forensics for Recoloring via Convolutional Neural Network

As a common medium in our daily life, images are important for most people to gather information. There are also people who edit or even tamper images to deliberately deliver false information under different purposes. Thus, in digital forensics, it is necessary to understand the manipulating history of images. That requires to verify all possible manipulations applied to images. Among all the image editing manipulations, recoloring is widely used to adjust or repaint the colors in images. The color information is an important visual information that image can deliver. Thus, it is necessary to guarantee the correctness of color in digital forensics. On the other hand, many image retouching or editing applications or software are equipped with recoloring function. This enables ordinary people without expertise of image processing to apply recoloring for images. Hence, in order to secure the color information of images, in this paper, a recoloring detection method is proposed. The method is based on convolutional neural network which is quite popular in recent years. Unlike the traditional linear classifier, the proposed method can be employed for binary classification as well as multiple labels classification. The classification performance of different structure for the proposed architecture is also investigated in this paper.     

A Robust Zero-Watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain

Remote medical diagnosis can be realized by using the Internet, but when transmitting medical images of patients through the Internet, personal information of patients may be leaked. Aim at the security of medical information system and the protection of medical images, a novel robust zero-watermarking based on SIFT-DCT (Scale Invariant Feature Transform-Discrete Cosine Transform) for medical images in the encrypted domain is proposed. Firstly, the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images. Then, the SIFT-DCT is used to extract the feature sequences of encrypted medical images. Next, zero-watermarking technology is used to ensure that the region of interest of medical images are not changed. Finally, the robust of the algorithm is evaluated by the correlation coefficient between the original watermark and the attacked watermark. A series of attack experiments are carried out on this method, and the results show that the algorithm is not only secure, but also robust to both traditional and geometric attacks, especially in clipping attacks.     

基于分级位平面偏移的多感兴趣区图像编码

被JPEG2000推荐的Maxshift方式能够高效完成单一感兴趣区(ROI)图像编码,但无法支持不同兴趣度下的多ROI编码.本文提出一种采用分级位平面偏移框架的多ROI编码方式-CBShift.新方法将所有ROI与背景位平面按重要性不同分为重要ROI与背号位平面区(MSRB)、一般重要ROI与背景位平面区(GSRB)及不重要背景区(LSB).当图像中含有多个ROI时,通过调节每个ROI在GSRB区的位平面偏移量支持不同兴趣度下的多ROI编码.实验显示,新方式不仅具有Maxshift方式的优点,而且支持不同兴趣度下无需ROI形状信息的多ROI编码.     

网络环境下实时图像信息处理传输系统

基于 MPEG-IV 编解码技术和 RTP/RTCP 网络传输协议,设计出一种以高速 DSPTMS320C6205 为核心处理器的实时图像信息处理传输系统(IPTS)。系统的总体构架采用Client/Server 模式,由现场视频信息采集前端、信息中心和视频信息传输网络构成。该系统融合了先进图像处理技术、信息技术、数据通信传输技术及自动控制技术等,能实时地采集和处理图像信息,并进行网络传输和控制,是一种范围大、方位全、实时、准确和高效信息综合处理系统。     

WISECODE: wise image segmentation based on community detection

Image segmentation is one of the fundamental problems in image processing and computer vision, since it is the first step in many image analysis systems. This paper presents a new perspective to image segmentation, namely, segmenting input images by applying efficient community detection algorithms common in social and complex networks. First, a common segmentation algorithm is used to fragment the image into small initial regions. A weighted network is then constructed. Each initial region is mapped to a vertex, and all these vertices are connected to each other. The similarity between two regions is calculated from colour information. This similarity is then used to assign weights to the edges. Afterwards, a community detection algorithm is applied, and communities are extracted such that the highest modularity measure is achieved. Finally, a post-processing algorithm merges very small regions with the greater ones, further enhancing the final result. One of the most striking features of the proposed method, is the ability to segment the input image without the need to specify a predefined number of segments manually. This remarkable feature results from the optimal modularity value, which is utilised by this method. It is also able to segment the input image into a user defined number of segments. Extensive experiments have been performed, and the results show that the proposed scheme can reliably segment the input colour image into good subjective criteria.     

High speed preprocessing system

In systems employing tracking, the area of interest is recognized using a high resolution camera and is handed over to the low resolution receiver. The images seen by the low resolution receiver and by the operator through the high resolution camera are different in spatial resolution. In order to establish the correlation between these two images, the high-resolution camera image needs to be preprocessed and made similar to the low-resolution receiver image. This paper discusses the implementation of a suitable preprocessing technique, emphasis being given to develop a system both in hardware and software to reduce processing time. By applying different software/hardware techniques, the execution time has been brought down from a few seconds to a few milli-seconds for a typical set of conditions. The hardware is designed around i486 processors and software is developed in PL/M. The system is tested to match the images obtained by two different sensors of the same scene. The hardware and software have been evaluated with different sets of images.     

生物医学图像信息技术相关研究

生物医学图像信息技术是一门计算机技术与生物医学相结合的综合性学科,其隶属于医学信息学。生物医学图像信息技术的研究对象为生物体,其主要根据不同光源在不同设备中所显示的图像差异,结合现代信息处理技术,实现对图像信息的收集整理、分析、储存、检索、利用和传播。本文主要对现代生物医学图像信息技术的应用领域及发展情况进行了综述。     

A web-based digital analysis interface for image quality assessment

Image processing and transmission systems may introduce some amount of distortion or artefacts in a digital image. This fact usually leads to a visual or statistical image quality assessment (IQA) required in many applications and research studies in order to analyse a product image in terms of deteriorations as well as effects of the processing. There are numerous IQA criteria presented in the literature separately that makes it really difficult both for use in comparative analyses and for educational purposes. In this presented work, a user friendly web-based digital educational interface for full-reference or no-reference image quality assessment using MATLAB builder NE has been developed. In addition to this, developed system performs user-defined optional deteriorations (median noise, Gaussian blur, motion blur, high-pass filter and jpeg compression) on the image and assesses deterioration of the image. It is an extremely easy, fast and economical way of analysing digital images, especially designed for researchers, graduate and postgraduate students who work on digital image processing. Using this web-based tool well contributes to teaching all of the IQA methods and quality effects of systematic distortions on the image as well as establishes a scientific benchmark for researchers.     

基于二次曲面拟合的亚象素图象匹配算法

在图象测量系统中，目标的精确定位是一个关键问题，也是应用其它图像处理技术的基础。传统的图象匹配算法只能在象素极定位，本文基于相关函数的二次曲面拟合提出了一种亚象素精度的匹配算法，它对于无噪声图象匹配的绝对误差小于０．０１象素。模拟实验表明，在有噪声的情况下该算法仍具有较小的偏差。     

LIP多重微区域分解算法扩展数字图像动态范围

CCD/CMOS 图像传感器的饱和电流和背景噪音使得一次曝光很难超过 60-80dB 的动态范围。用 LIP(Logarithmic image processing)模式将图像转换为灰色调函数,再将基于 LIP 模式的非线性多重微区域图像分解算法进行改进,可以适应不同亮度的单幅图像处理,提高了可分辨信息量和图像对比度,同时抑止了噪声。质量评价显示,该算法能将 MOS 等级四的源图像提高到等级一。