Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable dete...Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images.展开更多
A large field of view is in high demand for disease diagnosis in clinical applications of optical coherence tomography(OCT)and OCT angiography(OCTA)imaging.Due to limits on the optical scanning range,the scanning spee...A large field of view is in high demand for disease diagnosis in clinical applications of optical coherence tomography(OCT)and OCT angiography(OCTA)imaging.Due to limits on the optical scanning range,the scanning speed,or the data processing speed,only a relatively small region could be acquired and processed for most of the current clinical OCT systems at one time and could generate a mosaic image of multiple adjacent small-region images with registration algorithms for disease analysis.In this work,we investigated performing cross-correlation(instead of phase-correlation)in the workflow of the Fourier–Mellin transform(FMT)method(called dual-cross-correlation-based translation and rotation registration,DCCTRR)for calculating translation and orientation offsets and compared its performance to the FMT method used on OCTA images alignment.Both phantom and in vivo experiments were implemented for comparisons,and the results quantitatively demonstrate that DCCTRR can align OCTA images with a lower overlap rate,which could improve the scanning efficiency of large-scale imaging in clinical applications.展开更多
农村房地一体档案是对农村宅基地、集体建设用地使用权及房屋所有权进行确权登记的重要依据,将签章后的纸质档案转为电子档案进行存储对不动产权证书办理具有重要意义。由于目前缺乏能识别档案内容并进行分类归档的工具,设计并实现了基...农村房地一体档案是对农村宅基地、集体建设用地使用权及房屋所有权进行确权登记的重要依据,将签章后的纸质档案转为电子档案进行存储对不动产权证书办理具有重要意义。由于目前缺乏能识别档案内容并进行分类归档的工具,设计并实现了基于Tesseract-OCR的农村房地一体归档系统。使用光学字符识别(Optical Character Recognition,OCR)对档案扫描图像进行识别,训练校正字库,提取图像中的文字信息,实现档案资料的分类存储。运用四川省某县的部分房地一体档案进行系统测验,应用结果表明,系统的识别归档准确率为96.5%,能满足房地一体档案归档需求,降低了人工识别归档的繁琐性,极大提高了归档的工作效率,提升了档案分类的准确度。展开更多
文摘Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images.
基金supported by the Natural Science Foundation of Jiangsu Province(No.BK20210227).
文摘A large field of view is in high demand for disease diagnosis in clinical applications of optical coherence tomography(OCT)and OCT angiography(OCTA)imaging.Due to limits on the optical scanning range,the scanning speed,or the data processing speed,only a relatively small region could be acquired and processed for most of the current clinical OCT systems at one time and could generate a mosaic image of multiple adjacent small-region images with registration algorithms for disease analysis.In this work,we investigated performing cross-correlation(instead of phase-correlation)in the workflow of the Fourier–Mellin transform(FMT)method(called dual-cross-correlation-based translation and rotation registration,DCCTRR)for calculating translation and orientation offsets and compared its performance to the FMT method used on OCTA images alignment.Both phantom and in vivo experiments were implemented for comparisons,and the results quantitatively demonstrate that DCCTRR can align OCTA images with a lower overlap rate,which could improve the scanning efficiency of large-scale imaging in clinical applications.
文摘农村房地一体档案是对农村宅基地、集体建设用地使用权及房屋所有权进行确权登记的重要依据,将签章后的纸质档案转为电子档案进行存储对不动产权证书办理具有重要意义。由于目前缺乏能识别档案内容并进行分类归档的工具,设计并实现了基于Tesseract-OCR的农村房地一体归档系统。使用光学字符识别(Optical Character Recognition,OCR)对档案扫描图像进行识别,训练校正字库,提取图像中的文字信息,实现档案资料的分类存储。运用四川省某县的部分房地一体档案进行系统测验,应用结果表明,系统的识别归档准确率为96.5%,能满足房地一体档案归档需求,降低了人工识别归档的繁琐性,极大提高了归档的工作效率,提升了档案分类的准确度。
文摘背景与目的:核磁共振成像(magnetic resonance imaging,MRI)图像对软组织结构具有较高的分辨率,但由于失真和缺乏剂量计算所需要的电子密度而限制了其在脑部肿瘤放射治疗中的应用,而MRI和CT图像融合可解决这一问题。本研究探讨MRI与CT的图像融合精度,及其对脑胶质瘤术后患者放疗临床靶区(clinical target volume,CTV)及危及器官(organs at risk,OARs)体积和中心位置的影响。方法:9例颅内胶质瘤术后患者MRI和CT图像采用标点法进行融合,评价其融合精度,分别采用体积法及几何中心法(center of geometry,COG)研究融合前后临床靶区和危及器官体积和中心的变化,测定病灶MRI-CT融合图像的COG与CT定位图像COG的距离,体积法测定病灶MRI与CT图像融合部分体积(VMRI-CT)占总体积(VMRI+CT)的百分比(PMRI-CT)。结果:采用人工标记法进行融合的精度小于1.5mm,完全达到脑部肿瘤的误差要求。融合后各危及器官体积无明显改变(P>0.05);9例患者中8例融合界面勾画的CTV体积比CT定位图像CTV体积减小13.85%～73.59%,1例体积增大10.35%;平均体积比较差异有统计学意义(P<0.05);融合后CTV的中心位置变化最大[(8.74±6.60)mm],其次为双眼[左右眼分别为(5.25±2.38)mm和(5.65±2.56)mm],脑干位置变化最小[(1.83±1.06)mm]。结论:采用人工标记的方法进行图像融合具有较高的融合精度,MRI与CT融合的方法可明显减少脑胶质瘤术后放疗CTV勾画的不确定性。
