Research on Key Technologies of Automatic Registration in Low-altitude High Resolution Remote sensing Images

无人机(UnmannedAerialVehicle，UAV)遥感系统作为一种新兴的遥感技术，因其具有机动性强、实时性好、成本低、可提供厘米级超高分辨率数据等优点，已逐渐成为计算机视觉、图像处理、摄影测量与遥感等领域的研究热点。在无人机航拍图像的处理技术中，图像配准是关键环节，其研究成果可应用于多时遥感变化检测、建筑物3维重建以及多源遥感图像融合等领域。因此，开展低空航拍图像配准技术的研究具有重要的理论意义和广泛应用前景。 本文在综述图像配准技术研究现状的基础上，针对低空遥感图像中的景物透视变形、视差突变以及复杂变换结构估计等问题，开展了图像透视不变特征提取与匹配、特征匹配优化算法、图像变换结...Unmanned aerial vehicle (UAV) remote sensing system is a new technique for acquiring remote sensing data. Since UAV has the advantage of mobility, real-time, low cost and acuqiring centimeter-level ultra-high resolution data, new technologies relate to UAV data processing become hot topics in the fields of computer vision, image processing, photogrammetry and remote sensing. Noteworthy that image ...学位：理学博士院系专业：信息科学与技术学院_人工智能基础学号：3152010015405

A Survey on Pedestrian Detection

行人检测是计算机视觉中的研究热点和难点,本文对2005-2011这段时间内的行人检测技术中最核心的两个问题—特征提取、分类器与定位—的研究现状进行综述.文章中首先将这些问题的处理方法分为不同的类别,将行人特征分为底层特征、基于学习的特征和混合特征,分类与定位方法分为滑动窗口法和超越滑动窗口法,并从纵横两个方向对这些方法的优缺点进行分析和比较,然后总结了构建行人检测器在实现细节上的一些经验,最后对行人检测技术的未来进行展望.Pedestrian detection is an active area of research with challenge in computer vision.This study conducts a detailed survey on state-of-the-art pedestrian detection methods from 2005 to 2011,focusing on the two most important problems:feature extraction,the classification and localization.We divided these methods into different categories;pedestrian features are divided into three subcategories:low-level feature,learning-based feature and hybrid feature.On the other hand,classification and localization is also divided into two sub-categories:sliding window and beyond sliding window.According to the taxonomy,the pros and cons of different approaches are discussed.Finally,some experiences of how to construct a robust pedestrian detector are presented and future research trends are proposed.国家自然科学基金(No.60873179);高等学校博士学科点专项科研基金(No.20090121110032);深圳市科技计划-基础研究(No.JC200903180630A);深圳市科技研发基金-深港创新圈计划(No.ZYB200907110169A);福建省教育厅基金(No.JA10196

A Perspective Invariant Image Matching Algorithm

针对ASIfT(AffInE SCAlE InVArIAnT fEATurE TrAnSfOrM)算法存在的仿射采样策略、采样点离散设置等问题,提出了一种基于粒子群优化的图像透视不变特征PSIfT(PErSPECTIVE SCAlE InVArIAnT fEATurE TrAnSfOrM)算法.该算法通过虚拟相机的透视采样来模拟景物在多视角图像中的变形.在此基础上,将图像匹配问题转换为透视变换的优化问题,并以粒子群算法为工具,研究了虚拟相机旋转参数搜索空间、适应值函数的合理设定.针对三组不同类型低空遥感图像的实验结果表明,该算法比ASIfT、SIfT(SCAlE InVArIAnT fEATurE TrAnSfOrM)、HArrIS AffInE和MSEr(MAXIMAlly STAblE EXTrEMAl rEgIOnS)等算法获得更多的特征匹配对,有效地提高了算法对视角变化的鲁棒性.To solve the problem of affine transform and discrete sampling in ASIFT(Affine scale invariant feature transform),the PSIFT(Perspective scale invariant feature transform),which is based on particle swarm optimization,is proposed in this paper.The proposed algorithm uses a virtual camera and homographic transform to simulate perspective distortion among multi-view images.Therefore,particle swarm optimization is employed to determine the appropriate homography,which is decomposed into three rotation matrices.Experimental results obtained on three categories of low-altitude remote sensing images show that the proposed method outperforms significantly the state-of-the-art ASIFT,SIFT,Harris-affine and MSER,especially when images suffer severe perspective distortion.国家自然科学基金(61103052;61202143); 国家教育部博士点基金(20090121110032); 福建省产学重大科技项目(2011H6020); 福建省自然科学基金项目(2011J01013;2013J01245;2013J05100); 深圳市科技计划项目(JC200903180630A;ZYB200907110169A); 厦门市科技计划项目(3502Z20123022;3502Z20110010); 福建省教育厅基金项目(JK2012025)资助~

基于多尺度卷积神经网络特征融合的植株叶片检测技术

植株叶片检测是植株科学培育和精准农业过程中重要的环节之一。传统植株叶片检测的做法对操作人员的专业知识提出了较高要求，且人工成本高、耗时周期长。基于此，提出基于多尺度卷积神经网络特征融合（MCFF）的植株叶片检测技术。从深度学习技术辅助植株培育的需求出发，基于多尺度卷积神经网络特征融合，针对莲座模式植物、拟南芥和烟草3种不同类型、不同分辨率的植株进行叶片计数检测。经过与其他主流算法的比较，发现MCFF具备较高的检测精确度，平均精度均值（mAP）为0.662，实现了高度竞争的性能（AP=0.946），各项指标接近实用水平

Extract Feature Lines from Building LiDAR Point Cloud Based on Multi-structure Estimators

建筑物激光雷达(lIgHT dETECTIOn And rAngIng,lIdAr)点云特征线对于多视角点云配准、建筑物对称性检测、建筑物三维重建等应用具有十分重要的意义.由于lIdAr点云具有数据量庞大的特点,传统的算法难以实现建筑物特征线的快速提取.针对这个问题,提出一种基于多结构鲁棒估计的建筑物特征线提取算法,该算法利用历史模型信息进行条件采样,并通过迭代搜索符合所有特征线性质的模型.根据建筑物lIdAr数据的实验结果表明,该方法与传统的rAnSAC(rAndOM SAMPlE COnSEnSuS)、MlESAC(MAXIMuM lIkElIHOOd ESTIMATIOn SAMPlE COnSEnSuS)等算法相比,避免了无效、重复的特征线采样过程,在相同时间内可获取更多的直线内点,从而有效提高了建筑物特征线的提取效率.Feature lines extracted from building LiDAR(Light Detection and Ranging)point cloud data are of great significance in multiple views registration,building symmetry detection,3Dsurface reconstruction,among others.Since the LiDAR data are generally associated with a huge amounts of 3Dpoints,traditional algorithms suffer from the time complexity of rapidly extracting feature lines from building point cloud.In order to solve this problem,we present a feature lines extracted algorithm based on multi-structure robust estimation.In the proposed method,historical models generated by random strategy have been used for conditional sampling new models.Consequently,the searching process aims at extracting all feature lines from the model set.In the section of experiments,the multi-structure algorithm has been compared with the RANSAC(random sample consensus)and MLESAC(maximum likelihood estimation sample consensus).Results acquired from our LiDAR dataset indicate that the proposed method improves the efficiency of building feature lines extraction,since the multi-structure algorithm avoids many invalid and repeated sampling processes.Therefore,we can generate more feature lines at the same time.国家自然科学基金(61103052); 国家科技支撑计划(201309110001); 国家高技术研究发展计划(863计划)(2012AA12A208-06); 福建省产学重大科技项目(2011H6020); 福建省自然科学基金(2012J01013;2013J01245); 福建省教育厅专项课题(JK2012025); 厦门市科技计划项目(3502Z20110010

Affine SIFT Feature Optimization Algorithm Based on Fuzzy Control

ASIfT算法是图像特征匹配的有效工具,具有很强的仿射不变性。在ASIfT算法的基础上,利用nEldEr-MEAd单纯形方法优化采样点,并通过模糊控制策略实现单纯形参数的自适应调整。针对一组低空遥感图像的实验结果表明,本文方法保持了ASIfT算法对仿射变换的鲁棒性,并且能获得比ASIfT和SIfT算法更多的特征匹配对。Affine SIFT is an efficient tool for image matching,which has been proven to be invariant to affine distortion.To acquire more matches between reference image and input image,this paper used Nelder-Mead Simplex to optimize the best affine transformation based on transforms acquired by ASIFT.Moreover,fuzzy control has been employed to construct an adaptive parameter tuning strategy of Simplex.Experiments conducted remote sensing images show that the proposed algorithm is also invariant to affine distortion.Furthermore,the proposed method achieved promising results since we acquired more correct matches than ASIFT and SIFT.国家自然科学基金资助项目(61202143;61103052);高等学校博士学科点基金资助项目(20090121110032);深圳市科技项目(JC200903180630A;ZYB200907110169A);福建省产学重大项目(2011H6020);福建省自然科学基金资助项目(2011J01013);福建省教育厅项目(JA10196