The lunar map is a product of primary scientific objectives of lunar exploration. Aiming at the characteristics of the Chang＇E-2 CCD data, an automatic stitching method used for 2C level CCD data from Chang＇E-2 luna...The lunar map is a product of primary scientific objectives of lunar exploration. Aiming at the characteristics of the Chang＇E-2 CCD data, an automatic stitching method used for 2C level CCD data from Chang＇E-2 lunar mission is proposed. Combining with the image registration technique and the characteristics of Chang＇E CCD images, the fast method proposed not only can overcome the contradiction of the high spatial resolution of the CCD images and the low positioning accuracy of the location coordinates, but also can speed up the processing and minimize the utilization of human resources to produce lunar mosaic map. Meanwhile, a new lunar map from 70oN to 70oS with spatial resolution of less than 10 m has been completed by the proposed method. Its average relative location accuracy of the adjacent orbits CCD image data is less than 3 pixels.展开更多
A novel automatic seamless stitching method is presented. Compared to the traditional method, it can speed the processing and minimize the utilization of human resources to produce global lunar map. Meanwhile, a new g...A novel automatic seamless stitching method is presented. Compared to the traditional method, it can speed the processing and minimize the utilization of human resources to produce global lunar map. Meanwhile, a new global image map of the Moon with spatial resolution of -120 m has been completed by the proposed method from Chang＇E-1 CCD image data.展开更多
For the purpose of identifying the stern of the SWATH （Small Waterplane Area Twin Hull） availably and perfecting the detection technique of the SWATH ship＇s performance, this paper presents a novel bidirectional im...For the purpose of identifying the stern of the SWATH （Small Waterplane Area Twin Hull） availably and perfecting the detection technique of the SWATH ship＇s performance, this paper presents a novel bidirectional image registration strategy and mosaicing technique based on the scale invariant feature transform （SIFT） algorithm. The proposed method can help us observe the stern with a great visual angle for analyzing the performance of the control fins of the SWATH. SIFT is one of the most effective local features of the scale, rotation and illumination invariant. However, there are a few false match rates in this algorithm. In terms of underwater machine vision, only by acquiring an accurate match rate can we find an underwater robot rapidly and identify the location of the object. Therefore, firstly, the selection of the match ratio principle is put forward in this paper; secondly, some advantages of the bidirectional registration algorithm are concluded by analyzing the characteristics of the unidirectional matching method. Finally, an automatic underwater image splicing method is proposed on the basis of fixed dimension, and then the edge of the image＇s overlapping section is merged by the principal components analysis algorithm. The experimental results achieve a better registration and smooth mosaicing effect, demonstrating that the proposed method is effective.展开更多
Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch...Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.展开更多
基金supported in part by the Science and Technology Development Fund of Macao,China (Nos.048/2016/A2,110/2014/A3,091/2013/A3,084/2012/A3,and 048/2012/A2)the National Natural Science Foundation of China (Nos.61170320 and 61272364)the Open Project Program of the State Key Lab of CAD & CG of Zhejiang University (No.A1513)
文摘The lunar map is a product of primary scientific objectives of lunar exploration. Aiming at the characteristics of the Chang＇E-2 CCD data, an automatic stitching method used for 2C level CCD data from Chang＇E-2 lunar mission is proposed. Combining with the image registration technique and the characteristics of Chang＇E CCD images, the fast method proposed not only can overcome the contradiction of the high spatial resolution of the CCD images and the low positioning accuracy of the location coordinates, but also can speed up the processing and minimize the utilization of human resources to produce lunar mosaic map. Meanwhile, a new lunar map from 70oN to 70oS with spatial resolution of less than 10 m has been completed by the proposed method. Its average relative location accuracy of the adjacent orbits CCD image data is less than 3 pixels.
基金supported by the Science and Technology Development Fund of Macao (Nos. 004/2011/A1 and 015/2010/A)the National High Technology Research and Development Program (No. 2010AA122202)
文摘A novel automatic seamless stitching method is presented. Compared to the traditional method, it can speed the processing and minimize the utilization of human resources to produce global lunar map. Meanwhile, a new global image map of the Moon with spatial resolution of -120 m has been completed by the proposed method from Chang＇E-1 CCD image data.
基金Supported by the "Liaoning Baiqianwan" Talents Program(No.200718625)the Program of Scientific Research Project of Liao Ning Province Education Commission(No.LS2010046)the National Commonweal Industry Scientific Research Project(No.201003024)
文摘For the purpose of identifying the stern of the SWATH （Small Waterplane Area Twin Hull） availably and perfecting the detection technique of the SWATH ship＇s performance, this paper presents a novel bidirectional image registration strategy and mosaicing technique based on the scale invariant feature transform （SIFT） algorithm. The proposed method can help us observe the stern with a great visual angle for analyzing the performance of the control fins of the SWATH. SIFT is one of the most effective local features of the scale, rotation and illumination invariant. However, there are a few false match rates in this algorithm. In terms of underwater machine vision, only by acquiring an accurate match rate can we find an underwater robot rapidly and identify the location of the object. Therefore, firstly, the selection of the match ratio principle is put forward in this paper; secondly, some advantages of the bidirectional registration algorithm are concluded by analyzing the characteristics of the unidirectional matching method. Finally, an automatic underwater image splicing method is proposed on the basis of fixed dimension, and then the edge of the image＇s overlapping section is merged by the principal components analysis algorithm. The experimental results achieve a better registration and smooth mosaicing effect, demonstrating that the proposed method is effective.
文摘Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.
