DOCSLIB.ORG

A Method of Generating Panoramic Street Strip Image Map with Mobile Mapping System

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Method of Generating Panoramic Street Strip Image Map with Mobile Mapping System The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic A METHOD OF GENERATING PANORAMIC STREET STRIP IMAGE MAP WITH MOBILE MAPPING SYSTEM Chen Tianen a *, Kohei Yamamoto a, Kikuo Tachibana a a PASCO CORP. R&D CENTER, 2-8-10 Higashiyama Meguro-Ku, Tokyo 153-0043, JAPAN – (tnieah3292, kootho1810, kainka9209)@pasco.co.jp Commission I, ICWG I/VA KEY WORDS: Mobile Mapping System, Omni-Directional Camera, Laser Point Cloud, Street-Side Map, Image Stitching ABSTRACT: This paper explores a method of generating panoramic street strip image map which is called as “Pano-Street” here and contains both sides, ground surface and overhead part of a street with a sequence of 360° panoramic images captured with Point Grey’s Ladybug3 mounted on the top of Mitsubishi MMS-X 220 at 2m intervals along the streets in urban environment. On-board GPS/IMU, speedometer and post sequence image analysis technology such as bundle adjustment provided much more accuracy level position and attitude data for these panoramic images, and laser data. The principle for generating panoramic street strip image map is similar to that of the traditional aero ortho-images. A special 3D DEM(3D-Mesh called here) was firstly generated with laser data, the depth map generated from dense image matching with the sequence of 360° panoramic images, or the existing GIS spatial data along the MMS trajectory, then all 360° panoramic images were projected and stitched on the 3D-Mesh with the position and attitude data. This makes it possible to make large scale panoramic street strip image maps for most types of cities, and provides another kind of street view way to view the 360° scene along the street by avoiding the switch of image bubbles like Google Street View and Bing Maps Streetside. 1. INTRODUCTION visualization of street landscapes, and displayed on the internet Much more efforts have been invested into large scale urban to provide users a visual seamless immersive summary of a city. 3D model construction from vision-based 3D modelling Two kinds of low-cost, shortcut and functionary methods to (Baillard et al., 1999; El-Hakim, 2002; Bentrah et al., 2004a; provide users much more detail immersive street scenes are Surendra et al., 2014) and fusion 3D laser point clouds with systems such as Google Street View and Microsoft Live Street- images (Frueh et al., 2005; Zhao and Shibasaki, 2001; Stamos Side that enable users to virtually visit cities by navigating and Allen, 2000) for these applications such as navigation, between immersive 360° panoramic images (Vincent 2007), or driving direction pre-visualizations and augmented reality as bubbles, and multi-perspective strip panoramas that can demonstrated in Google Earth or Microsoft Virtual Earth in the provide a visual summary of a city street with a long image last decades, but the progress on the street view level is not so strip constructed a sequence video images along the street remarkable and the achieved results are still on the level of (Román et al. 2004; Román and Lensch 2006; Agarwala et al. research. The reasons may be that the ground environments of 2006; Rav-Acha et al. 2008). the urban streets are tens of times much more complex, and often lack textured areas, contain repetitive structures, moving The former could provide a photorealistic impression from a cars and pedestrians, many occlusions, strong lighting changes, particular viewpoint inside a bubble by panning and zooming and cast shadows. These properties make the above two the bubble or the image, but they do not provide a good visual methods difficult in the sense of finding enough reliable point sense of a larger aggregate such as a whole city block or longer matches between overlapping images and extracting key street and need vast amount of geo-registered panoramic skeleton points from 3D laser point clouds for following 3D imagery. Navigating such photo collections is laborious and model construction. To create visually pleasing modes at a high similar to hunting for a given location on foot: walk “along” the level of detail in urban environments, an enormous amount of street, (e.g., jumping from bubble to bubble in Street View) manual work, such as importing the geometry obtained from looking around, until you find the location of interest. Since construction plans, polylines, points or selecting primitive automatically geo-located addresses and/or GPS readings are shapes and correspondence points for image-based modelling often off by several meters or more especially in urban settings, or complex data acquisition are involved. In fact, like aero visually searching for a location is often needed. Within a ortho-images in GIS, a panoramic street strip image map is bubble, severe foreshortening of a street side from such a also a low-cost, shortcut and useful GIS data to meet the basic distance makes recognition almost impossible. Furthermore, a requirements of the above applications and these applications huge data size is inevitable to archive an entire city. such as virtual travel and architectural walkthroughs to some extent. It could be also suitable for registration, transmission, The latter could provide a useful visual summary of all the landmarks along a city street using a long image strip * Corresponding author This contribution has been peer-reviewed. doi:10.5194/isprsarchives-XLI-B1-537-2016 537 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic generated with slit or line scanning mechanism (Zheng and 2. PANO-STREET OVERVIEW Tsuji, 1992; Gupta and Hartley, 1997) and image patch stiching or video mosaicking mechanism (Zheng et al., 2004; The purpose and the theory of making panoramic street strip Zhu et al., 2004). Kopf et al. (2010) provided a street slide image map with ground street level panoramic images are which dynamically combines the aspects of the immersive similar to that of aerial ortho-images. Figure 2 illustrates the nature of bubbles and the overview properties of the multi- flow diagrams of Pano-Street generation. More detail perspective strip panoramas. As the user slides sideways, the descriptions about these diagrams will be presented in the multi-perspective panorama is dynamically constructed and following subsections. rendered to simulate either a pseudo perspective or hyper- perspective view of the street side from a distance by aligning and overlaying perspective projections of dropped resolution bubble images oriented towards the street side. Unfortunately, because all of the source imagery is compressed into a single flat summary, they lack the spatial geographic information as the traditional ortho-images generated from aero and satellite images. Figure 2. Flow diagrams of Pano-Street generation. 2.1 360° Panoramic Images Panoramic images are a new type of visual data that provide many new possibilities as compared to the classic planar images since panoramic images allow observing a scene from various viewing directions. Large scale GIS for street-level viewing, geographical mapping and other location-based visualizations, high end security and surveillance, city planning, simulation and measurement analysis, entertainment solutions (a) Caged 3D Mesh (b) Trough-shaped 3D Mesh for lighting models, full dome projection content, tele-presence, Figure 1. Two Types of 3D Meshes Used as Projection Base virtual navigation and other immersive experiences are Plans for Panoramic Street Strip Image Map Generation. examples of such interesting applications. Other more applications of panoramic images for 3D close range This paper explores a method of making panoramic street strip photogrammetry have been also reported due to their abilities image map which is called as “Pano-Street” here and contains to record much visual information of the real environment in both sides, ground surface and overhead part all directions of a one image at field view angle of 360° in longitude direction street with a sequence of 360° panoramic images which are and 180° in latitude direction, especially their usage in the captured with a Point Grey’s Ladybug3 mounted on the top of narrow spatial sites. The most famous application example is Mitsubishi MMS-X 220 at 2m intervals along the streets in that Google Street View has used panoramic cameras over the urban environment (Chen et al., 2012), and projected/stitched world to collect geo-referenced panoramic images of city on to one special 3D mesh called projection base plan (See environments to expand the Google web GIS database for more Figure 1) which is equivalent to the DEM/DSM used to detail view of the ground surface. generate ortho-image with aerial/satellite images, and constructed with existing 2D/3D GIS data along the street. On- The use of panoramic photographs dates back until the early board GPS/IMU, speedometer and post sequence image years of photography. First panoramic images have been analysis technology such as bundle adjustment provided high recorded with rotating frame cameras or by swing lens accuracy position and attitude data for these panoramic images, techniques. During the 19th century, panoramic cameras have and laser scanning data. This makes it possible to make large been combined with angular reading in order to measure the scale panoramic street strip image maps with characters of both rotation
Load more

Recommended publications
  • A Stitching Algorithm for Automated Surface Inspection of Rotationally Symmetric Components
    A Stitching Algorithm for Automated Surface Inspection of Rotationally Symmetric Components Tobias Schlagenhauf1, Tim Brander1, Jürgen Fleischer1 1Karlsruhe Institute of Technology (KIT) wbk-Institute of Production Science Kaiserstraße 12, 76131 Karlsruhe, Germany Abstract This paper provides a novel approach to stitching surface images of rotationally symmetric parts. It presents a process pipeline that uses a feature-based stitching approach to create a distortion-free and true-to-life image from a video file. The developed process thus enables, for example, condition monitoring without having to view many individual images. For validation purposes, this will be demonstrated in the paper using the concrete example of a worn ball screw drive spindle. The developed algorithm aims at reproducing the functional principle of a line scan camera system, whereby the physical measuring systems are replaced by a feature-based approach. For evaluation of the stitching algorithms, metrics are used, some of which have only been developed in this work or have been supplemented by test procedures already in use. The applicability of the developed algorithm is not only limited to machine tool spindles. Instead, the developed method allows a general approach to the surface inspection of various rotationally symmetric components and can therefore be used in a variety of industrial applications. Deep-learning-based detection Algorithms can easily be implemented to generate a complete pipeline for failure detection and condition monitoring on rotationally symmetric parts. Keywords Image Stitching, Video Stitching, Condition Monitoring, Rotationally Symmetric Components 1. Introduction The remainder of the paper is structured as follows. Section 2 reviews the current state of the art in the field of stitching.
    [Show full text]
  • Fast Vignetting Correction and Color Matching for Panoramic Image Stitching
    FAST VIGNETTING CORRECTION AND COLOR MATCHING FOR PANORAMIC IMAGE STITCHING Colin Doutre and Panos Nasiopoulos Department of Electrical and Computer Engineering The University of British Columbia, Vancouver, Canada ABSTRACT When images are stitched together to form a panorama there is often color mismatch between the source images due to vignetting and differences in exposure and white balance between images. In this paper a low complexity method is proposed to correct vignetting and differences in color Fig. 1. Two images showing severe color mismatch aligned with no between images, producing panoramas that look consistent blending (left) and multi-band blending [1] (right) across all source images. Unlike most previous methods To compensate for brightness/color differences between which require complex non-linear optimization to solve for images in panoramas, several techniques have been correction parameters, our method requires only linear proposed [1],[6]-[9]. The simplest of these is to multiply regressions with a low number of parameters, resulting in a each image by a scaling factor [1]. A simple scaling can fast, computationally efficient method. Experimental results somewhat correct exposure differences, but cannot correct show the proposed method effectively removes vignetting for vignetting, so a number of more sophisticated techniques effects and produces images that are highly visually have been developed. consistent in color and brightness. A common camera model is used in most previous work on vignetting and exposure correction for panoramas [6]-[9]. Index Terms— color correction, vignetting, panorama, A scene radiance value L is mapped to an image pixel value image stitching I, through: 1. INTRODUCTION I = f ()eV ()x L (1) A popular application of image registration techniques is to In equation (1), e is the exposure with which the image stitch together multiple photos into a panorama [1][2].
    [Show full text]
  • Robust L2E Estimation of Transformation for Non-Rigid Registration Jiayi Ma, Weichao Qiu, Ji Zhao, Yong Ma, Alan L
    IEEE TRANSACTIONS ON SIGNAL PROCESSING 1 Robust L2E Estimation of Transformation for Non-Rigid Registration Jiayi Ma, Weichao Qiu, Ji Zhao, Yong Ma, Alan L. Yuille, and Zhuowen Tu Abstract—We introduce a new transformation estimation al- problem of dense correspondence is typically associated with gorithm using the L2E estimator, and apply it to non-rigid reg- image alignment/registration, which aims to overlaying two istration for building robust sparse and dense correspondences. or more images with shared content, either at the pixel level In the sparse point case, our method iteratively recovers the point correspondence and estimates the transformation between (e.g., stereo matching [5] and optical flow [6], [7]) or the two point sets. Feature descriptors such as shape context are object/scene level (e.g., pictorial structure model [8] and SIFT used to establish rough correspondence. We then estimate the flow [4]). It is a crucial step in all image analysis tasks in transformation using our robust algorithm. This enables us to which the final information is gained from the combination deal with the noise and outliers which arise in the correspondence of various data sources, e.g., in image fusion, change detec- step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. In the dense point tion, multichannel image restoration, as well as object/scene case for non-rigid image registration, our approach consists of recognition. matching both sparsely and densely sampled SIFT features, and The registration problem can also be categorized into rigid it has particular advantages in handling significant scale changes or non-rigid registration depending on the form of the data.
    [Show full text]
  • Video Stitching for Linear Camera Arrays 1
    LAI ET AL.: VIDEO STITCHING FOR LINEAR CAMERA ARRAYS 1 Video Stitching for Linear Camera Arrays Wei-Sheng Lai1;2 1 University of California, Merced [email protected] 2 NVIDIA Orazio Gallo2 [email protected] Jinwei Gu2 [email protected] Deqing Sun2 [email protected] Ming-Hsuan Yang1 [email protected] Jan Kautz2 [email protected] Abstract Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts. In this work, we propose a wide- baseline video stitching algorithm for linear camera arrays that is temporally stable and tolerant to strong parallax. Our key insight is that stitching can be cast as a problem of learning a smooth spatial interpolation between the input videos. To solve this prob- lem, inspired by pushbroom cameras, we introduce a fast pushbroom interpolation layer and propose a novel pushbroom stitching network, which learns a dense flow field to smoothly align the multiple input videos for spatial interpolation. Our approach out- performs the state-of-the-art by a significant margin, as we show with a user study, and has immediate applications in many areas such as virtual reality, immersive telepresence, arXiv:1907.13622v1 [cs.CV] 31 Jul 2019 autonomous driving, and video surveillance. c 2019. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. (a) Our stitching result (b) [19] (c) [3] (d) [12] (e) Ours Figure 1: Examples of video stitching. Inspired by pushbroom cameras, we propose a deep pushbroom stitching network to stitch multiple wide-baseline videos of dynamic scenes into a single panoramic video.
    [Show full text]
  • Image Stitching for Panoramas Last Updated: 12-May-2021
    Image Stitching for Panoramas Last Updated: 12-May-2021 Copyright © 2020-2021, Jonathan Sachs All Rights Reserved Contents Introduction ................................................................................................................................... 3 Taking the photographs ................................................................................................................ 4 Equipment ................................................................................................................................. 4 Use a rectilinear lens................................................................................................................. 6 Use the same camera settings for all the images ...................................................................... 6 Image overlap ........................................................................................................................... 7 Camera orientation ................................................................................................................... 7 Composition .............................................................................................................................. 7 Avoid polarizing filters ............................................................................................................... 7 Single- and Multi-row panoramas .............................................................................................. 8 Mark the beginning of each series of images ...........................................................................
    [Show full text]
  • Sift Based Image Stitching
    Journal of Computing Technologies (2278 – 3814) / # 14 / Volume 3 Issue 3 SIFT BASED IMAGE STITCHING Amol Tirlotkar, Swapnil S. Thakur, Gaurav Kamble, Swati Shishupal Department of Information Technology Atharva College of Engineering, University of Mumbai, India. { amoltirlotkar, thakur.swapnil09, gauravkamble293 , shishupal.swati }@gmail.com Abstract - This paper concerns the problem of image Camera. Image stitching is one of the methods that can be stitching which applies to stitch the set of images to form used to create large image by the use of overlapping FOV a large image. The process to generate one large [8]. The drawback is the memory requirement and the panoramic image from a set of small overlapping images amount of computations for image stitching is very high. In is called Image stitching. Stitching algorithm implements this project, this problem is resolved by performing the a seamless stitching or connection between two images image stitching by reducing the amount of required key having its overlapping part to get better resolution or points. First, the stitching key points are determined by viewing angle in image. Stitched images are used in transmitting two reference images which are to be merged various applications such as creating geographic maps or together. in medical fields. Most of the existing methods of image stitching either It uses a method based on invariant features to stitch produce a ‘rough’ stitch or produce a ghosting or blur effect. image which includes image matching and image For region-based image stitching algorithm detect the image merging. Image stitching represents different stages to edge, prepare for the extraction of feature points.
    [Show full text]
  • Shooting Panoramas and Virtual Reality
    4104_ch09_p3.qxd 6/25/03 11:17 PM Page 176 4104_ch09_p3.qxd 6/25/03 11:17 PM Page 177 Shooting Panoramas and Virtual Reality With just a little help from special software, you can extend the capabilities of your digital camera to create stunning panoramas and enticing virtual reality (VR) images. This chapter shows you how to shoot—and create— images that are not only more beautiful, but 177 ■ also have practical applications in the commer- SHOOTING PANORAMAS AND VIRTUAL REALITY cial world as well. Chapter Contents 9 Panoramas and Object Movies Shooting Simple Panoramas Extending Your View Object Movies Shooting Tips for Object Movies Mikkel Aaland Mikkel 4104_ch09_p3.qxd 6/25/03 11:18 PM Page 178 Panoramas and Object Movies Look at Figure 9.1. It’s not what it seems. This panorama is actually comprised of several images “stitched” together using a computer and special software. Because of the limitations of the digital camera’s optical system, it would have been nearly impos- sible to make this in a single shot. Panoramas like this can be printed, or with an extended angle of view and more software help, they can be turned into interactive virtual reality (VR) movies viewable on a computer monitor and distributed via the Web or on a CD. Figure 9.1: This panorama made by Scott Highton using a Nikon Coolpix 990 is comprised of 178 four adjacent images, “stitched” together using Photoshop Elements’ 2 Photomerge plug-in. ■ If you look at Figure 9.2 you’ll see another example where software was used to extend the capabilities of a digital camera.
    [Show full text]
  • Image Stitching
    Panoramic Image Mosaics Image Stitching Computer Vision CSE 576, Spring 2008 Full screen panoramas (cubic): http://www.panoramas.dk/ Richard Szeliski Mars: http://www.panoramas.dk/fullscreen3/f2_mars97.html Microsoft Research 2003 New Years Eve: http://www.panoramas.dk/fullscreen3/f1.html Richard Szeliski Image Stitching 2 Gigapixel panoramas & images Image Mosaics + + … + = Mapping / Tourism / WWT Medical Imaging Goal: Stitch together several images into a seamless composite Richard Szeliski Image Stitching 3 Richard Szeliski Image Stitching 4 Today’s lecture Readings Image alignment and stitching • Szeliski, CVAA: • motion models • Chapter 3.5: Image warping • Chapter 5.1: Feature-based alignment (in preparation) • image warping • Chapter 8.1: Motion models • point-based alignment • Chapter 8.2: Global alignment • Chapter 8.3: Compositing • complete mosaics (global alignment) • compositing and blending • Recognizing Panoramas, Brown & Lowe, ICCV’2003 • ghost and parallax removal • Szeliski & Shum, SIGGRAPH'97 Richard Szeliski Image Stitching 5 Richard Szeliski Image Stitching 6 Motion models What happens when we take two images with a camera and try to align them? Motion models • translation? • rotation? • scale? • affine? • perspective? … see interactive demo (VideoMosaic) Richard Szeliski Image Stitching 8 Image Warping image filtering: change range of image g(x) = h(f(x)) Image Warping f f h x x image warping: change domain of image g(x) = f(h(x)) f f h x x Richard Szeliski Image Stitching 10 Image Warping Parametric (global) warping
    [Show full text]
  • Robust Panoramic Image Stitching CS231A Final Report
    Robust Panoramic Image Stitching CS231A Final Report Harrison Chau Robert Karol Department of Aeronautics and Astronautics Department of Aeronautics and Astronautics Stanford University Stanford University Stanford, CA, USA Stanford, CA, USA [email protected] [email protected] Abstract— Creation of panoramas using computer vision is not a new algorithms combined in a novel way. First, an image database idea, however, most algorithms are focused on creating a panorama is created. This is done manually by taking pictures from a using all of the images in a directory. The method which will be variety of locations around the world. SIFT keypoints are then explained in detail takes this approach one step further by not found for each image. The algorithm then sorts through the requiring the images in each panorama be separated out manually. images to find keypoint matches between the images. A Instead, it clusters a set of pictures into separate panoramas based on clustering algorithm is run which separates the images into scale invariant feature matching. Then uses these separate clusters to separate panoramas, and the individual groups of images are stitch together panoramic images. stitched and mosaicked together to form separate panoramas. Keywords—Panorama; SIFT; RANSAC; Homography; II. RELATED WORK Keypoint; Stitching; Clustering A. Prior Algorithms I. INTRODUCTION Algorithms which have been used to create panoramas You travel often for work or pleasure but you have had a have been developed in the past and implemented many times. passion for photography since childhood. Over the years, you Some of these algorithms have even been optimized to run in take many different photographs at each of your destination.
    [Show full text]
  • Dual-Fisheye Lens Stitching for 360-Degree Imaging
    DUAL-FISHEYE LENS STITCHING FOR 360-DEGREE IMAGING Tuan Ho 1* Madhukar Budagavi 2 1University of Texas – Arlington, Department of Electrical Engineering, Arlington, Texas, U.S.A 2Samsung Research America, Richardson, Texas, U.S.A ABSTRACT Dual-fisheye lens cameras have been increasingly used for 360-degree immersive imaging. However, the limited overlapping field of views and misalignment between the two lenses give rise to visible discontinuities in the stitching boundaries. This paper introduces a novel method for dual- fisheye camera stitching that adaptively minimizes the Fig. 1. (a) Samsung Gear 360 Camera (left). (b) Gear 360 discontinuities in the overlapping regions to generate full dual–fisheye output image (7776 x 3888 pixels) (right). spherical 360-degree images. Results show that this approach Left half: image taken by the front lens. Right half: image can produce good quality stitched images for Samsung Gear taken by the rear lens. 360 – a dual-fisheye camera, even with hard-to-stitch objects in the stitching borders. companies. The number of cameras used in these systems ranges from 8 to 17. These cameras typically deliver Index Terms— Dual-fisheye, Stitching, 360-degree professional quality, high-resolution 360-degree videos. Videos, Virtual Reality, Polydioptric Camera On the downside, these high-end 360-degree cameras are bulky and extremely expensive, even with the decreasing cost 1. INTRODUCTION of image sensors, and are out of reach for most of the regular users. To bring the immersive photography experience to the 360-degree videos and images have become very popular masses, Samsung has presented Gear 360 camera, shown in with the advent of easy-to-use 360-degree viewers such as Fig.
    [Show full text]
  • Panoweaver 9 User Manual (To Start the Online Help, Select Help > Help Topics from the Main Menu Or Press F1)
    Panoweaver User Manual www.easypano.com Table Of Contents Welcome .................................................................................................... 1 Introduction ............................................................................................... 3 What's New........................................................................................... 3 Edition Comparison ................................................................................ 4 Get Help ............................................................................................... 5 Install Panoweaver 9 ................................................................................... 7 System Requirements .......................................................................... 10 System Requirements ..................................................................... 10 For Windows .................................................................................. 10 For Macintosh ................................................................................ 11 Activate Panoweaver 9 ............................................................................... 13 Activate Panoweaver 9 ......................................................................... 13 Purchase ............................................................................................ 13 Product Activation ................................................................................ 13 Transfer License Key ...........................................................................
    [Show full text]
  • Automatic Panoramic Image Stitching Using Invariant Features
    Automatic Panoramic Image Stitching using Invariant Features Matthew Brown and David G. Lowe {mbrown|lowe}@cs.ubc.ca Department of Computer Science, University of British Columbia, Vancouver, Canada. Abstract can provide very accurate registration, but they require a close initialisation. Feature based registration does not re- This paper concerns the problem of fully automated quire initialisation, but traditional feature matching meth- panoramic image stitching. Though the 1D problem (single ods (e.g., correlation of image patches around Harris cor- axis of rotation) is well studied, 2D or multi-row stitching is ners [Har92, ST94]) lack the invariance properties needed more difficult. Previous approaches have used human input to enable reliable matching of arbitrary panoramic image or restrictions on the image sequence in order to establish sequences. matching images. In this work, we formulate stitching as a In this paper we describe an invariant feature based ap- multi-image matching problem, and use invariant local fea- proach to fully automatic panoramic image stitching. This tures to find matches between all of the images. Because of has several advantages over previous approaches. Firstly, this our method is insensitive to the ordering, orientation, our use of invariant features enables reliable matching of scale and illumination of the input images. It is also insen- panoramic image sequences despite rotation, zoom and illu- sitive to noise images that are not part of a panorama, and mination change in the input images. Secondly, by viewing can recognise multiple panoramas in an unordered image image stitching as a multi-image matching problem, we can dataset. In addition to providing more detail, this paper ex- automatically discover the matching relationships between tends our previous work in the area [BL03] by introducing the images, and recognise panoramas in unordered datasets.
    [Show full text]