基于AROP程序包的类Landsat遥感影像配准与正射纠正试验和精度分析

遥感影像的精确配准和正射纠正是进行图像融合、变化检测、图像镶嵌、定量遥感建模、多时相和多传感器影像协同应用的基础和前提。以美国国家航空和航天管理局下设LEDAPS(Landsat Ecosystem Disturbance Adaptive Processing System)课题组开发的配准与正射纠正程序包AROP(Automated Registration and Orthorectification Package)为例,详细阐述了其配准的原理与程序设计流程,并对其配准的精度进行了分析和评价。试验表明:AROP程序包算法能够找出足够的控制点,且控制点分布较为均匀,配准误差小于0.5个像元。误差特征表现为:扫描误差明显大于航向误差,误差的结果与影像漂移、DEM、坡度存在一定的相关性,高程和坡度是影响配准精度的主要因素之一。该程序包目前能够用于对我国CBERS影像的正射校正以及波段不匹配处理,但是对HJ卫星CCD影像数据配准还有待于进一步研究。     

基于非下采样Contourlet变换的指纹图像超分辨率重建

非下采样Contourlet变换(Nonsubsampled contourlet transform,NSCT)采用非抽样金字塔结构和非抽样方向滤波器组构成,具有Contourlet变换所不具备的平移不变性、较高冗余度等优良特性,而且能够克服伪吉布斯现象。图像经过非下采样Contourlet变换后分解成多尺度、多方向的细节信息,这些细节信息代表了图像不同频带不同方向的特征,这就简化了系数之间的关系。基于学习的超分辨率重建算法具有整体的预测性,将非下采样Contourlet变换和基于学习的算法相结合,在一定程度上提高训练精度。针对指纹图像的实验证明该算法具有良好的性能,重建的图像纹理性细节信息较好,基本保持了原指纹图像的特征点,更接近于原始的高分辨率图像。     

视错觉及其应用

视觉足人类获取外部信息的最重要方式。但由于人类自身的生理、心理因素以及外界光、形、色等因素的干扰,会产生视觉误差,即视错觉。本章系统的分析了视错觉产生原因,按不同的现象和成因把视错觉分成病理视错觉、动态视错觉、几何视错觉、颜色视错觉、轮廓错觉等5类。在此基础上介绍了视错觉的应用,特别介绍了其在图像融合中的应用,并用matlab实现了图像的融合。     

SAS数据挖掘在冷轧酸洗卷质量缺陷分析中的应用

数据挖掘作为一种发现大量数据中潜在信息的数据分析技术,受到各界的密切关注。SAS数据挖掘技术是众多数据挖掘方法中的佼佼者,它在大型企业中得到很好的应用。本文介绍数据挖掘的背景知识,并利用SAS/EM工具,对该技术在冷轧酸洗卷质量缺陷原因分析作了初步尝试。     

基于SQL Server的OLAM的研究及应用

OLAM(On-line Analytical Mining)是当前的热点技术,是融合了联机分析处理(OLAP)和数据挖掘(Data Mining)的一种新的数据挖掘技术。本文主要研究数据仓库中OLAP,On-line Analytical Processing和数据挖掘技术,这两个技术是商业智能中的核心技术和主要内容,在两者的基础上引入OLAM的概念,并阐述其基本原理和核心技术。基于微软的SQL Server平台,为一个商业案例建立数据仓库,利用Analysis Services服务,建立销售分析的多维数据集,利用OLAM的基本模型实现OLAP和聚类挖掘技术的结合,借助两者的各自优势,得到很好的客户聚类分析结果。     

图形图像制作软件中的色彩模式分析

色彩模式是在数字处理过程中处理色彩的基本标准,其是以颜色模型为基础,利用数字定量对颜色进行描述的方法。针对图形图像制作软件中的色彩模式进行分析。     

基于SOM的模糊聚类在Web日志挖掘中的应用

将自组织映射神经网络（SOM）与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。     

单幅图像去雨算法研究现状及展望

图像去雨算法通过对有雨图像进行分析和处理从而去除雨水条纹,恢复干净的背景场景,有助于提升计算机视觉任务识别精度,因此成为当下的研究热点。为系统地了解该领域的研究现状和发展趋势,首先介绍了典型的雨水合成模型,其次从基于模型驱动和基于数据驱动两个方面重点分析了典型图像去雨算法模型和方法;之后比较了去雨图像质量评价指标及雨水数据集;最后,对单幅图像去雨算法未来发展趋势进行了展望。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.