Automatic Panoramic Image Stitching Using Invariant Features
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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. -
Pnp-Net: a Hybrid Perspective-N-Point Network
PnP-Net: A hybrid Perspective-n-Point Network Roy Sheffer1, Ami Wiesel1;2 (1) The Hebrew University of Jerusalem (2) Google Research Abstract. We consider the robust Perspective-n-Point (PnP) problem using a hybrid approach that combines deep learning with model based algorithms. PnP is the problem of estimating the pose of a calibrated camera given a set of 3D points in the world and their corresponding 2D projections in the image. In its more challenging robust version, some of the correspondences may be mismatched and must be efficiently dis- carded. Classical solutions address PnP via iterative robust non-linear least squares method that exploit the problem's geometry but are ei- ther inaccurate or computationally intensive. In contrast, we propose to combine a deep learning initial phase followed by a model-based fine tuning phase. This hybrid approach, denoted by PnP-Net, succeeds in estimating the unknown pose parameters under correspondence errors and noise, with low and fixed computational complexity requirements. We demonstrate its advantages on both synthetic data and real world data. 1 Introduction Camera pose estimation is a fundamental problem that is used in a wide vari- ety of applications such as autonomous driving and augmented reality. The goal of perspective-n-point (PnP) is to estimate the pose parameters of a calibrated arXiv:2003.04626v1 [cs.CV] 10 Mar 2020 camera given the 3D coordinates of a set of objects in the world and their corre- sponding 2D coordinates in an image that was taken (see Fig. 1). Currently, PnP can be efficiently solved under both accurate and low noise conditions [15,17]. -
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]. -
A Multi-Temporal Image Registration Method Based on Edge Matching and Maximum Likelihood Estimation Sample Consensus
A MULTI-TEMPORAL IMAGE REGISTRATION METHOD BASED ON EDGE MATCHING AND MAXIMUM LIKELIHOOD ESTIMATION SAMPLE CONSENSUS Yan Songa, XiuXiao Yuana, Honggen Xua aSchool of Remote Sensing and Information Engineering School,Wuhan University, 129 Luoyu Road,Wuhan,China 430079– [email protected] Commission III, WG III/1 KEY WORDS: Change Detection, Remote Sensing, Registration, Edge Matching, Maximum Likelihood Sample Consensus ABSTRACT: In the paper, we propose a newly registration method for multi-temporal image registration. Multi-temporal image registration has two difficulties: one is how to design matching strategy to gain enough initial correspondence points. Because the wrong matching correspondence points are unavoidable, we are unable to know how many outliers, so the second difficult of registration is how to calculate true registration parameter from the initial point set correctly. In this paper, we present edge matching to resolve the first difficulty, and creatively introduce maximum likelihood estimation sample consensus to resolve the robustness of registration parameter calculation. The experiment shows, the feature matching we utilized has better performance than traditional normalization correlation coefficient. And the Maximum Likelihood Estimation Sample Conesus is able to solve the true registration parameter robustly. And it can relieve us from defining threshold. In experiment, we select a pair of IKONOS imagery. The feature matching combined with the Maximum Likelihood Estimation Sample Consensus has robust and satisfying registration result. 1. INTRODUCTION majority. So in the second stage, we pay attention on how to calculate registration parameter robustly. These will make us Remote sensing imagery change detection has gain more and robustly calculate registration parameter successfully, even more attention in academic and research society in near decades. -
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. -
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. -
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 ........................................................................... -
Density Adaptive Point Set Registration
Density Adaptive Point Set Registration Felix Jaremo¨ Lawin, Martin Danelljan, Fahad Shahbaz Khan, Per-Erik Forssen,´ Michael Felsberg Computer Vision Laboratory, Department of Electrical Engineering, Linkoping¨ University, Sweden {felix.jaremo-lawin, martin.danelljan, fahad.khan, per-erik.forssen, michael.felsberg}@liu.se Abstract Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the den- sity of 3D points. This fundamental problem is primar- ily caused by changes in the sensor location across point sets. We revisit the foundations of the probabilistic regis- tration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probabil- ity distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimiz- ing the Kullback-Leibler divergence in an Expectation Max- imization based framework. Our density-adaptive regis- tration successfully handles severe density variations com- monly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real- world Lidar datasets. The results demonstrate that our ap- proach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling. Figure 1. Two example Lidar scans (top row), with signifi- cantly varying density of 3D-points. State-of-the-art probabilistic method [6] (middle left) only aligns the regions with high density. This is caused by the emphasis on dense regions, as visualized by 1. -
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. -
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. -
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 -
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.