基于文字和图像信息提取视频关键帧

关键帧提取是基于内容检索的一个重要的组成部分,所提取的关键帧的质量的好坏,直接影响检索的结果。该文介绍了目前几种较为典型的算法,并在对其进行分析比较的基础上,针对新闻视频提出了基于文字和图像信息提取关键帧的算法,取得了很好的效果,最后在结论中提出了综合运用音频、图像、文字和运动信息提取关键帧的层次化算法思想。

基于分数阶微分的高斯噪声图像信息提取算法

考虑分数阶微分对信号处理的性质与特点,提出一种基于分数阶微分的信息提取算法。首先针对各种系数高斯噪声的图像进行信息提取,并与Roberts、Prewitt、Sobel整数阶经典算子对图像信息提取的结果进行分析比较。实验证明,利用分数阶微分不但能提取整数阶算子的高频边缘信息,还能提取平滑区域的纹理信息,并对高斯噪声具有一定程度的抗噪性。

基于紫外图像信息提取的离散液滴参数对复合绝缘子沿面放电特征的影响

为提高污湿环境下输变电设备外绝缘运行可靠性,本文针对绝缘子运行期间离散液滴的形成和诱导的沿面放电闪络现象,基于图像处理、特征提取和定量指标的紫外图像信息提取技术对不同的液滴静态参数(位置、体积和电导率)下的沿面闪络放电过程进行分析。得到了液滴诱发绝缘子放电的变化过程以及绝缘子起始放电电压和闪络电压与液滴的位置、体积和电导率之间的变化规律。研究结果有助于进一步分析表面液滴在污闪过程中的作用机理,对电力系统的安全稳定运行具有重要意义。

不同IHS变换公式在植被信息提取中的比较研究

多源遥感图像融合对于植被信息提取具有重要意义,该文对常用的球体、柱体、三角形和单六角锥4种IHS变换融合方法进行了比较研究,分别从融和图像的信息量、标准差等统计特征、植被光谱特征曲线、类别可分性、植被指数4方面做了分析.研究结果表明,球体变换融和图像的信息量、标准差和光谱扭曲值等统计指标从整体上优于其他变换.球体变换可将不同类型的灰度平均值间的距离进行拉伸.类别可分性指标的分析表明,球体变换可将难以区分的阔叶林与针叶林、阔叶林与草地间的距离加大。

千将坪滑坡特征信息遥感提取及滑坡成因机理分析

2003年7月13日,三峡库区湖北省秭归县沙镇溪镇千将坪村发生特大型滑坡灾害后,利用遥感图像信息提取技术,结合滑坡前后的高分辨率的遥感数据、地形图资料和野外所测的GPS控制点,制作出研究区滑坡前后的数字高程模型(DEM),在获取滑坡前后高分辨率卫星数据的基本上,采用合理的图像处理技术,获得了滑坡前后正射影像图。采用人机交互解译滑坡,详细地对比解译滑坡前后的图像,分别对滑体的滑动方向、距离和堆积体面积进行了定量计算,估算出其体积约为1 518万m3,在对比千将坪滑坡附近地区多年降雨和三峡库区一期蓄水的基础上,综合地质学、灾害学原理和地理空间信息技术等科学分析了滑坡的成因机理,认为千将坪“7.13”滑坡为古滑坡的复活,三峡库区蓄水和降雨综合作用是促使滑坡复活的主要诱发因素。

约束优化进化的夜间图像时频复合加权提取

研究了一种约束优化进化的夜间图像时频复合加权提取技术。对多帧夜间图像的时域与频域同时加权处理是一种深度检索夜间图像信息的先进技术。夜间图像质量低,传统的夜间图像信息检索技术采用单独的时域检索方法,无法对多帧夜间图像的频谱特性进行分析和信息检索。为此,提出一种约束优化进化的夜间图像时频复合加权提取技术。首先在频域和时域同时对多帧原始图像进行处理,提取多帧图像之间的相关信息;然后做加权处理,形成新的图像特征;在此基础上,通过约束优化进化算法对图像信息检索的结果不断进行循环优化,最终达到较好的效果。采用一组夜间图像进行了实验测试,结果显示,采用约束优化进化的夜间图像时频复合加权提取,多帧图像之间的时域和频域特征得到了很好的加权利用,最终实现了图像信息的深度提取。该方法在图像检索领域具有很好的应用价值。

基于可逆信息隐藏算法的图像信息处理系统设计

传统的图像信息处理系统存在着载体图像恢复性能差的缺陷,为了解决上述问题,提出基于可逆信息隐藏算法的图像信息处理系统。系统硬件设计包括主控芯片选型、并行处理结构设计以及以太网通信电路设计;软件设计包括图像信息隐藏模块、图像信息提取模块与载体图像恢复模块。通过系统硬件与软件的设计实现了基于可逆信息隐藏算法的图像信息处理系统的运行。实验结果表明,与传统的图像信息处理系统相比,设计的图像信息处理系统极大地提升了载体图像恢复性能,具备更好的处理效果。

路面裂缝信息自动提取研究综述

路面裂缝是路面早期病害的主要形式,获取准确和完整的路面裂缝信息对于制定路面维护计划和预算方案至关重要,但准确、全面的对路面裂缝信息提取仍是一个挑战。因此,本文对路面裂缝信息自动提取进行文献综述,从裂缝图像采集,裂缝图像预处理,裂缝图像信息提取三个方面,分析现有技术的优缺点和发展趋势,为路面裂缝信息自动提取研究提供参考。

计算机视觉信息处理技术在苹果自动分级中的应用

苹果在水果消耗中占有较大份额,对其进行分级销售可提高经济效益。在以往的苹果分级中,大都采用人工方法进行,只考虑大小、色泽方面的影响,导致分级精度低和人工消耗大。计算机系统现今已被广泛应用在精细农业中,如水果和蔬菜的自动收获及农产品的分级。为此,利用计算机视觉系统采集提取苹果图像,采用边缘检测、图像改善、图像二值化等图像数据处理方法对采集的图像前处理,设定等级区分参数,再依据特征参数对苹果进行自动分级。采用机器视觉进行苹果等级分离,提高了苹果分级的正确率,节省了劳动力,可以广泛地推广应用。

“8·7”甘肃舟曲县特大泥石流灾害遥感解译与评价研究

从泥石流形成的地形、地质和降雨条件入手,分析了本次泥石流灾害的特征与成因,并利用灾害发生前后的高分辨率影像数据、数字高程模型,通过数字影像正射纠正、数字制图、人机交互式解译等开展应急救灾工作,提取了灾区房屋毁损、道路破坏、河流堵塞、地质灾害等重要信息,在第一时间为前线救灾治理工作提供了丰富而准确的调查数据。同时对三眼峪沟泥石流治理工程进行了遥感解译评价,从灾后影像图分析,治理工程减少了洪峰流量和泥沙冲出量,对减轻本次灾害的严重程度有一定作用。

词袋特征支持下的无人机影像滑坡解译模型

川西地区滑坡灾害频发,给当地人民生命、财产安全带来了极大威胁。如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译的关键。针对该问题,首先,对比了多种影像特征提取方法,然后将词袋特征和支持向量机引入到构建滑坡灾害解译模型中,提出了一种基于词袋特征的无人机影像滑坡灾害解译模型。最后,选取512汶川地震及420芦山地震系列无人机影像构建了滑坡灾害样本库,基于该样本库进行了实验,分类准确度达到76%,相比于传统目视解译在效率上有明显优势。分析表明,所提出方法可用于高分影像滑坡自动解译。此外,该模型可用于大面积高分影像快速山地滑坡灾害定位,可实现滑坡灾害的快速检测,对于灾后应急具有一定的指导作用。

现代科技发展对实验力学带来的挑战和机遇——中国科协青年科学家第75次论坛会议简介

由清华大学、东南大学、天津大学、国防科技大学共同发起和中国科协主办的第75次青年科学家论坛(现代科技发展对实验力学带来的挑战和机遇)于2003年8月4日至5日在北京中国科学会堂召开.

Fast Texture Segmentation Based on Semi-Local Region Descriptor and Active Contour

In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the problem of textures where neither the gray-level information nor the boundary information is adequate for object extraction. This is often the case of natural images composed of both homogeneous and textured regions. Because these images cannot be in general directly processed by the gray-level information, we propose a new texture descriptor which intrinsically defines the geometry of textures using semi-local image information and tools from differential geometry. Then, we use the popular Kullback-Leibler distance to design an active contour model which distinguishes the background and textures of interest. The existence of a minimizing solution to the proposed segmentation model is proven. Finally, a texture segmentation algorithm based on the Split-Bregrnan method is introduced to extract meaningful objects in a fast way. Promising synthetic and real-world results for gray-scale and color images are presented.

Case study on the extraction of land cover information from the SAR image of a coal mining area

In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.

A method of automatic shape depiction and information extraction for oceanic eddies in SAR images

Synthetic aperture radar （SAR） provides a large amount of image data for the observation and research of oceanic eddies. The use of SAR images to automatically depict the shape of eddies and extract the eddy information is of great significance to the study of the oceanic eddies and the application of SAR eddy images. In this paper, a method of automatic shape depiction and information extraction for oceanic eddies in SAR images is proposed, which is for the research of spiral eddies. Firstly, the skeleton image is got by the skeletonization of SAR image. Secondly, the logarithmic spirals detected in the skeleton image are drawn on the SAR image to depict the shape of oceanic eddies. Finally, the eddy information is extracted based on the results of shape depiction. The sentinel 1 SAR eddy images in the Black Sea area were used for the experiment in this paper. The experimental results show that the proposed method can automatically depict the shape of eddies and extract the eddy information. The shape depiction results are consistent with the actual shape of the eddies, and the extracted eddy information is consistent with the reference information extracted by manual operation. As a result, the validity of the method is verified.

A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

Classification Method Research to Remote Sensing Images

With rapid development of remote sensing technology, the resolution of remote sensing images is increasingly improved; then people can extract more useful data and information from these images. Thus, an important information extraction method from remote sensing images - image classification, becomes more and more important. Based on phenopthase and band composition characteristics, this paper firstly discusses the important role of background parameters in remote sensing images classification; then based on geographical infomation system technology, the computerized automatic classification to high-medium-low-yield croplands in Dingxiang County of Shanxi Province in rotate sensing images has been carried out by using eompound layers classification method of multi-thematic information; compared the classification result to the visual interpretation results, the accuracy increases from 70% to above 90%.

Extracting Eco-hydrological Information of Inland Wetland from L-band Synthetic Aperture Radar Image in Honghe National Nature Reserve, Northeast China

Taking a typical inland wetland of Honghe National Nature Reserve (HNNR), Northeast China, as the study area, this paper studied the application of L-band Synthetic Aperture Radar (SAR) image in extracting eco-hydrological information of inland wetland. Landsat-5 TM and ALOS PALSAR HH backscatter images were first fused by using the wavelet-IHS method. Based on the fused image data, the classification method of support vector machines was used to map the wetland in the study area. The overall mapping accuracy is 77.5%. Then, the wet and dry aboveground biomass estimation models, including statistical models and a Rice Cloudy model, were established. Optimal parameters for the Rice Cloudy model were calculated in MATLAB by using the least squares method. Based on the validation results, it was found that the Rice Cloudy model produced higher accuracy for both wet and dry aboveground biomass estimation compared to the statistical models. Finally, subcanopy water boundary information was extracted from the HH backscatter image by threshold method. Compared to the actual water borderline result, the extracted result from L-band SAR image is reliable. In this paper, the HH-HV phase difference was proved to be valueless for extracting subcanopy water boundary information.

Dynamic mode decomposition of hairpin vortices generated by a hemisphere protuberance

We present dynamic mode decomposition (DMD) for studying the hairpin vortices generated by hemisphere protuberance measured by two-dimensional (2D) time-resolved (TR) particle image velocimetry (PIV) in a water channel. The hairpins dynamic information is extracted by identifying their dominant frequencies and associated spatial structures. For this quasi-periodic data system, the resulting main Dynamic modes illustrate the different spatial structures associated with the wake vortex region and the near-wall region. By comparisons with proper orthogonal decomposition (POD), it can be concluded that the dynamic mode concentrates on a certain frequency component more effectively than the mode determined by POD. During the analysis, DMD has proven itself a robust and reliable algorithm to extract spatial-temporal coherent structures.

An adaptive tensor voting algorithm combined with texture spectrum

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.