A cavity depth and volume estimation technique to support data collection in an image mining oriented geoprocessing system

Spatially defined information is essential to support decision making in several areas, such as in therapeutic procedures to reduce or healing lesions in human body. So localization together with images and many other data can constitute a database to feed an Image Mining oriented Geoprocessing. Besides that low cost additional infrastructures are attractive to install large-scale use of image capturing and interpretation for health assistance. In such context geometric features of cavities can be remotely captured to monitor patients with wounds called pressure ulcers (PU). An image with linear shadows on the cavity of a model is generated in order to enable the measurement of its deformation caused by depth. This yields maximum depth and volume in an experimental model that are compared with measurements made previously in a conventional manner. Partially satisfactory results suggest improvements in image capturing device and computational procedures.     

Model of a Composite Energy Storage System for Urban Rail Trains

Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtain more information. Channel attention (cSE) is introduced in upsampling to enhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.     

An algorithm for detection and phase estimation of protective elements periodic lattice on document image

Various periodic security elements, such as holograms, watermarks, and guilloches, are applied to documents in order to protect against counterfeiting. These elements can be detected and used to automatically check the authenticity of a document and to identify its type. They also make it possible to use special OCR system parameters in areas of security elements. This paper is devoted to developing methods for the detection and localization of periodic background patterns based on two-dimensional discrete Fourier transform. The model of a document image with a periodic background structure is considered. Algorithms for the detection and localization of background structures that follow from the model are discussed. The behavior and accuracy characteristics of the algorithms are tested on samples of Russian passport images. Their tolerance to errors in document boundary detection are experimentally analyzed. Modified detection and localization algorithms that improve the separating detection capability and reduce localization error twofold are proposed such as masking and replacement of noisy parts of document images, background spectrum suppression, and estimation of phase components of a single periodic element.     

小波域基于差分统计量直方图平移的图像鲁棒可逆信息隐藏算法

随着互联网技术的飞速发展和5G时代的到来,数字多媒体的交流和传播变得越来越便捷。为了解决网络环境下,数字图像的版权保护、完整性认证和篡改定位等问题,本文提出了一种小波域基于差分统计量直方图平移的图像鲁棒可逆信息隐藏算法,其主要思想为：1）对载体图像进行Haar小波变换;2）对变换后所得到的低频子带进行分块并计算分块的差分统计量以构造差分统计量的直方图;3）通过平移直方图将秘密信息嵌入到图像的低频子带中,并通过Haar小波逆变换得到带秘密信息的图像;4）在接收方,通过计算低频子带的分块差分统计量可以将秘密信息准确地提取出来,并且通过执行直方图平移的逆操作可以无损地还原出原始的低频子带,从而无失真地恢复出原始载体图像;5）带秘密信息的图像在遭到一定程度的图像处理操作的攻击后,被嵌入的秘密信息仍然可以被有效地提取出来,例如压缩因子为30的JPEG压缩和标准差为30的加性高斯噪声。与现有的几种鲁棒可逆信息隐藏算法相比,本文算法有更强的鲁棒性。实验结果验证了本文算法的有效性。     

Secure authentication watermarking for localization against the Holliman–Memon attack

Authentication watermarking schemes using block-wise watermarks for tamper localization are vulnerable to the Holliman–Memon attack. In this paper, we propose a novel method based on the Wong’s localization scheme (Proceedings of the IS&T PIC, Portland) to resist this attack. A unique image index scheme is used for computing the authentication signature that is embedded in the least significant bit-plane of the block. The informed detector estimates the correct image index by using the side information about the watermarked image. The image index estimation from the fake image can definitely be an alternative to keeping a directory of image indices. So it is not necessary to manage the database of image indices for the verification purpose. The authenticity measure is defined to quantify the attack severity by taking the connectivity among possible authentic blocks into consideration. There are more blocks verified as authentic when this measure is high for a fake image constructed using this attack. As such, the blocks for a fake image can be chosen from a reduced number of database images. The blocks from any such image are to be connected with each other to maximize the authenticity measure. Thus, the attacker’s task to generate a fake image of reasonable perceptual quality becomes increasingly difficult. With the proposed method there is no loss or ambiguity in localization after the Holliman–Memon attack and content tampering in an image. The localization accuracy in the proposed method is demonstrated by the simulation results and is equal to the chosen block size, similar to the Wong’s scheme.     

Robot Localization in Indoor Environments Using Radio Frequency Identification Technology and Stereo Vision

In this paper, a novel method of obstacle recognition and localization for mobile robots using radio frequency identification (RFID) technology and stereo vision is proposed. It is inexpensive, flexible and easy to use in practical environments. As information about the obstacles or environment can be written in ID tags, the proposed method can detect the obstacles easily and quickly compared with other methods. RF is not so stable, so the Bayes rule was introduced to calculate the probability where the ID tag exists in order to improve the accuracy of localizing obstacles with the ID tags. Then the stereo camera starts to process the image of the region of interest where the obstacle exists. The proposed method does not need to process all the image and easily gets some information about obstacles such as size, and color, and thus decreases the processing computation. Research on RFID technology integrating stereo vision to localize an indoor mobile robot has also been performed. This paper introduces the architecture of the proposed method and some experimental results.     

Development and applications of a software for Functional Image Registration (FIRE)

Image registration with anatomical modalities, such as CT and MRI, facilitates the anatomical identification and localization in the interpretation of nuclear medicine images that lack anatomical information. The implementation of Functional Image Registration (FIRE), an operating system (OS) and platform independent multimodal image registration software is reported. In order to register the images without an operator's interaction, several automatic algorithms were implemented. These include principal axes matching and maximization of the mutual information methods. The user interface was designed to support the manual registration of the images. Fused images were composed by overlaying one image with the other one transparently, in which the opacity of the overlaid image was interactively controlled. FIRE was successfully applied to many clinical cases for which automatic and/or manual registration was required. An OS and platform independent program for image registration developed in this study will be useful for the clinical application of image registration techniques.     

Whisper, don't scream: grids and transparency

Visual elements such as grids, labels, and contour lines act as reference structures that support the primary information being presented. Such structures need to be usefully visible, but not so obtrusive that they clutter the presentation. Visual designers know how to carefully manage transparency and layering in an image to balance these elements. We want the presentation of these structures in complex, dynamic, computer-generated visualizations to reflect the same subtlety and comfort of good design. Our goal is to determine the physical, perceptual, and cognitive characteristics of such structures in a way that enables automatic presentation. Our approach to this problem does not try to characterize "ideal” or "best,” but instead seeks boundary conditions that define a range of visible yet subtle legibility. All presentations that are clearly bad lie outside of this range, and can easily be avoided. In this paper, we report three experiments investigating the effects of grid color and spacing on these boundary conditions, defined by manipulating the transparency (alpha) of thin rectangular grids over scatter plots. Our results show that while there is some variation due to user preference and image properties, bounding alpha allows us to reliably predict a range of usable yet unobtrusive grids over a wide variety of conditions.     

面向屏幕拍摄的端到端鲁棒图像水印算法

目的 在抗屏摄鲁棒图像水印算法的研究中,如何在保证含水印图像视觉质量的同时提高算法的鲁棒性是存在的主要挑战。为此,提出一种基于深度学习的端到端网络框架以用于鲁棒水印的嵌入与提取。方法 在该网络框架中,本文设计了包含摩尔纹在内的噪声层用以模拟真实屏摄噪声造成的失真,并通过网络训练来学习到抵抗屏摄噪声的能力,增强网络生成的含水印图像的鲁棒性;同时引入了最小可察觉失真（just noticeable distortion,JND）损失函数,旨在通过监督图像的JND系数图与含有水印信息的残差图之间的感知差异来自适应控制鲁棒水印的嵌入强度,以提高生成的含水印图像的视觉质量。此外,还提出了两种图像区域自动定位方法,分别用于解决：拍摄图像中前景与背景分割即含水印图像区域的定位矫正问题,以及含水印图像经过数字裁剪攻击后的解码问题。结果 实验结果表明,引入JND损失函数后嵌入水印图像的视觉质量得到了提高,平均的峰值信噪比（peak signalto-noise ratio,PSNR）、结构相似性（structural similarity,SSIM）可分别达到30.937 1 dB和0.942 4。加入摩尔纹的噪声模拟层后,所提算法的误码率可下降1%～3%,具有抵抗屏摄噪声的能力。另外,将图像的R通道嵌入用于抗裁剪的模板,使得算法可有效抵抗较大程度的数字裁剪攻击。本文算法的计算复杂度较低,对单幅图像进行嵌入时,定位与提取操作的总耗时小于0.1 s,可满足实际应用场景的实时性需求。结论 本文算法的嵌入容量和生成的含水印图像视觉质量较为理想,且在不同拍摄距离、角度以及不同拍摄和显示设备条件下的鲁棒性优于已报道的主流算法。     

Content-based image database system for epilepsy

We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy. The system consists of several modules including a database backbone, brain structure identification and localization, segmentation, registration, visual feature extraction, clustering/classification and query modules. Our newly developed anatomical landmark localization and brain structure identification method facilitates navigation through an image data and extracts useful information for segmentation, registration and query modules. The database stores T1-, T2-weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data. We confine the visual feature extractors within anatomical structures to support semantically rich content-based procedures. The proposed system serves as a research tool to evaluate a vast number of hypotheses regarding the condition such as resection of the hippocampus with a relatively small volume and high average signal intensity on FLAIR. Once the database is populated, using data mining tools, partially invisible correlations between different modalities of data, modeled in database schema, can be discovered. The design and implementation aspects of the proposed system are the main focus of this paper.     

Analyzing scenery images by monotonic tree

Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Research efforts are needed to bridge the gap between high-level semantics and low-level features. In this paper, we present a novel approach to support semantics-based image retrieval. Our approach is based on the monotonic tree, a derivation of the contour tree for use with discrete data. The structural elements of an image are modeled as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such properties as color, spatial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme is applied to the analysis and retrieval of scenery images. Comparisons of experimental results of this approach with conventional techniques using low-level features demonstrate the effectiveness of our approach.     

基于最大熵原理的多阈值自动选取新方法

基于信息论中最大熵原理 ,提出了一种等概率场下的自动选取阈值的新方法 .该方法是首先通过图象灰度直方图信息 ,并利用 Shannon熵中等概率场具有最大熵的基本性质来确定阈值 ,然后将图象划分为等概率的子块 ,进而给出了该算法的理论推导和算法的具体实现步骤 .与通常的基于熵理论自动获取图象阈值的方法相比较 ,该方法直观、简便、求解稳定 ,且易于实现 .同时该方法克服了常用方法在阈值求取时 ,出现的诸如计算量大、计算效率低等不足等问题 ,故能够迅速地获得图象的阈值 .对比实验的结果 ,也说明了该方法的快速性、有效性、可靠性和稳定性     

多总线多DSP实时图像处理操作系统的设计与实现

该文针对多总线多 DSP实时图像识别系统 ,设计并实现了一个并行操作系统 .它包括嵌入到 DSP芯片上的操作系统和运行在 PC机上的协议软件两部分 .协议软件提供一个人机界面 ,接收算法的分解信息 ,并将其按一定的数据结构组织 ,再将所有的子任务及其分解信息连接成一个作业 .DSP上的操作系统支持作业从上位机上加载 ,或通过 EPROM加载 .操作系统支持 VXI总线标准 ,并提供了数据通信、任务分配和并发进程管理等功能 .它根据任务分解信息 ,分配硬件资源 ,构造数据流向 ,建立子任务相互间的同步关系 ,完成与上位机的联络并输出结果 .实验结果表明 ,该文设计的硬件及其操作系统能够适应不同并行结构的需要 ,并得到满意的图像并行处理效果 .     

Recovering ball motion from a single motion-blurred image

Motion blur often affects the ball image in photographs and video frames in many sports such as tennis, table tennis, squash and golf. In this work, we operate on a single calibrated image depicting a moving ball over a known background, and show that motion-blurred ball images, usually unwelcome in computer vision, bear more information than a sharp image. We provide techniques for extracting such information ranging from low-level image processing to 3D reconstruction, and present a number of experiments and possible applications, such as ball localization with speed and direction measurement from a single image, and ball trajectory reconstruction from a single long-exposure photograph.     

基于HTML5的网页地图设计和应用

网络地理信息系统（WebGIS）综合应用网络与网页服务,应用ArcGIS软件相对标准地图完成加工设计,系统需要的地理数据库和地理数据处理过程进行分类概述。超文本标识语言（HTML）第5版加强和提高网络和网页软件应用开发的技术标准,优化网页结构基础元素,也是网页地图设计的基础编程语言。运用HTML和JAVASCRIPT对网页地图模块进行程序设计,并测试网页应用功能和模块操作应用。深入分析网页地图命令工具和应用功能,表明网页服务开发地理信息的可行性。     

天基光学传感器姿态实时校正算法

卫星在轨运行过程中,载荷姿态不可避免受到平台抖动、传感器热形变等安装误差因素一定程度的影响,导致图像定位精度下降。针对该问题,通过建立相应的安装误差状态转移模型和基于特征点的观测模型、采用扩展卡尔曼滤波（ EKF）,实现对安装误差的实时估计。采用基于特征点的天基传感器姿态实时校正方法,数学仿真表明：该方法能够实现姿态实时校正,且性能稳定,可有效提高图像定位精度。     

一种自适应半脆弱数字水印算法

结合人眼视觉特性及数字图像的自相关性,提出一种具有自适应性的半脆弱数字水印算法。该算法采用改进的低通滤波器,对于中低频图像信息,通过动态的自适应调节中间像素灰度,改变两端的灰度差分来实现水印的嵌入。实验表明该算法对所有等级的JPEG压缩均具有很好的鲁棒性,并且对图像擦除、加噪、挤压等恶意篡改具有脆弱性、敏感性以及准确的检测定位能力。     

常用边缘检测技术的对比

边缘检测是图像处理和计算机视觉中的基本问题,边缘检测的目的是标识数字图像中亮度变化明显的点.经典的边缘检测算法如canny算子等是通过计算图像中局部小区域的差分来实现边缘检测的.这类算子对噪声非常敏感,并且常常会在检测边缘的同时加强噪声.多尺度形态学边缘检测利用不同的结构元素去作用图像,通过形态腐性和形态膨胀操作,获得了效果很好的图像边缘检测算法.单尺度形态学梯度算子也能很好提检测图像边缘,但结构元素的选取对输出结果影响较大.通过使用多尺度形态学梯度算子,可以弥补结构元素的大小问题.仿真结果表明,该算法能得到较为理想的图像边缘信息,其抗噪声性能明显优于经典的算子检测算法.