基于兴趣边缘优化的壁画影像与激光扫描数据非刚性配准

将壁画影像与激光扫描数据配准,并进行定位和纠正在壁画的数字化保护中有非常重要的意义.本文以激光扫描数据强度信息为中介,提出了一种基于兴趣边缘优化的壁画影像与激光扫描数据的非刚性配准方法:由激光扫描数据生成强度影像,以壁画彩色影像的兴趣边缘和强度影像的梯度场作为配准基元,在影像刚性配准基础上,对每条兴趣边缘进行优化配准,然后以优化后边缘的特征点为控制点,构造影像之间的非刚性变换模型,完成壁画影像与激光扫描数据的配准.实验结果表明本方法在不同数据中都能获得较高的配准精度.     

Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.     

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VFP的艺术类专业报名考试系统的设计与实现

结合高校艺术类专业招生工作实际情况,针对当前艺术类专业报名考试的特点和要求,借助VFP强大的数据管理功能,构建基于VFP的艺术类专业报名考试系统,实现报名信息管理、考场安排、准考证打印和成绩统计的一体化管理,提高了招生工作效率。     

基于群智感知的街景变化检测方法

移动群智感知数据包含的图像和时空情境信息可用于检测街景图像变化,但是群智感知数据通常是低质和不规范的。为了准确检测街景发生的变化,主要解决由拍摄视角差异引起的数据低质问题。首先,针对大视差问题采用图像配准方法初步对齐图像并提取出配准特征点;然后,基于配准特征点分布从图像中提取感兴趣区域;随后,针对差值图像的误检内容,提出基于面积和多特征点的筛选法去除误检区域;最后,结合边缘检测和超像素分割算法提取完整的变化对象。与MDFNet方法进行比较,实验结果显示：当街景发生变化时,该方法的F1-measure值为55.8%,增长6%,错误率为10.8%,降低24%;当街景无变化时,该方法的错误率为2.8%,下降28%。     

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.     

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.     

一种新的基于梯度方向直方图的图像配准方法*

构建了一种新的局部梯度方向直方图,同时定义了特征点的主方向,从而提出了一种具有旋转不变性的图像配准算法.首先采用高斯加权求模技术对特征点邻域内的像素的梯度作直方图统计,确定出具有旋转不变性和抗噪性的特征点主方向;然后用主方向作角度直方图统计,确定待配准图像之间的旋转角度.根据得到的特征点信息及旋转角度定义了特征点对互信息匹配准则,这样使得新配准算法对于图像间旋转角度的范围没有限制,获得了良好的配准效果.     

基于CCA的人耳和侧面人脸特征融合的身份识别*

鉴于人耳和人脸特殊的生理位置关系,从非打扰识别的角度出发,提出仅采集侧面人脸图像,利用典型相关分析的思想提取人耳和侧面人脸的关联特征,进行人耳和侧面人脸在特征层的融合.实验结果表明,此方法与单一的人耳或侧面人脸特征识别比较,识别率得到提高.     

基于区域生长的多源遥感图像配准

多源遥感图像由于成像设备、所用光谱、拍摄时间等因素的不同,给配准带来极大的困难.尽管已经提出了多种匹配方法,但已有方法一般只能适用于特定的应用环境,开发出更加稳定和适用的配准算法仍然是一个极具挑战性的研究课题.提出一种基于区域生长的配准方法,首先,提取改进后的尺度不变特征,通过全局匹配确定种子点和种子区域并完成变换模型的初始化;然后,运用迭代区域生长和双向匹配策略,得到整个图像的可靠匹配点,从而实现多源遥感图像之间的配准.实验表明,该方法提取的匹配点的数量和正确率均远高于已有方法,能够对存在严重灰度差异的多源遥感图像实现高精度的配准,充分证明了该方法的鲁棒性和适用性.