Integer Computation of Image Orthorectification for High Speed Throughput


 Janis Knight
 1 years ago
 Views:
Transcription
1 Integer Computation of Image Orthorectification for High Speed Throughput Paul Sundlie Joseph French Eric Balster Abstract This paper presents an integerbased approach to the orthorectification of aerial imagery. The orthorectification process is a backprojection algorithm with the use of a CAHV camera model, Digital Terrain Map (DTM), and implementation of the collinearity equations. In many airborne imaging systems, the orthorectification process is a computational bottleneck which hinders processing throughput. The proposed integerbased approach reduces the computation time of the projection process by an average of 27.6%. In addition, the proposed solution easily lends itself to further processing improvement using embedded solutions such as FPGAs. I. INTRODUCTION The use of digital aerial imagery for surveying large areas of land is in use across a variety of disciplines. A common trait within these applications is the benefit of rectified image data. Image rectification refers to the processing of a raw image in order to produce a result that can be superimposed to a map. Depending on the accuracy of the rectification method chosen, the result may or may not be an orthoimage; the term orthoimage being used only for resulting imagery of the greatest achievable accuracy [3]. With the right components, the creation of one orthoimage is not troublesome. However, when producing aerially acquired realtime digital orthoimages, the computational burden becomes an obstacle. To produce an orthoimage, the user must have knowledge of two sets of data: the parameters of the camera system used to acquire the data, and elevation information of the observed area [3]. The characteristics of the camera system consist of the external and internal parameters. The internal parameters provide information on the image orientation with variables such as the focal length and location of the principal point, while the exterior parameters provide the position and orientation of the camera system in world coordinates [2]. The second set of data required is the elevation information of the area in view. This information comes in the form of a Digital Elevation Map (DEM). The term DEM is a general specification; specific terms include DTM, which provides ground elevation information, and Digital Surface Map (DSM), which includes the highest elevation at each point (such as the top of a building) [3]. With the required data sets present, the next step is to specify the algorithm for rectification. Various methods can be found in the literature, such as polynomial rectification or projective transformation [6]. One method, known as backprojection, is given in [5, 6]. An optimization of backprojection is the focus of this paper. Camera systems of today produce images consisting in the millions of pixels. When taken from the air, these images will often be acquired with an array if individual cameras. The images are stitched together to produce a mosaic which is then processed. It becomes computationally time consuming to process large orthomosaics in realtime. In order to accelerate the process, an implementation using a fixedpoint integer approach is used. This approach is presented here because of its ability to accelerate a software solution as well as show feasibility for an embedded solution using an FPGA. FPGAs have been shown to be superior, in terms of speed, to both a CPU and GPU for image processing applications [8]. The following sections provide a solution for implementing the backprojection method using a fixedpoint integer approach which results in improved throughput. Measurements are compared with the backprojection method of [4]. Section II discusses the main components of the implementation. These include the CAHV camera model [1], the backprojection algorithm [5, 6], and a review of fixedpoint mathematics. Section III covers the subject of retaining precision, followed by the results in section IV. Finally, section V offers some concluding comments and a discussion of future possibilities. II. BACKGROUND The proceeding subsections outline the main components of the implementation. The CAHV camera model is discussed first, followed by the backprojection algorithm and then an overview of fixedpoint integer mathematics. A. CAHV Camera Model The classic photogrammetric camera model provides the internal and external parameters of the viewing camera [2]. First documented by Yakimovsky and Cunningham [1], the CAHV camera model provides the same information through the use of four 3 dimensional vectors (C, A, H, and V). The four vectors given in the CAHV model provide the transformation from world coordinates to image coordinates [2]. Each individual vector provides its own set of significant
2 (X c, Y c, Z c )  Perspective center in the object coordinate system f  Focal length m ij  (i, j) th component of M, a 3x3 rotation matrix defining the transformation between the image and object space Fig. 1. CAHV camera model (source: [2]) information. The C vector specifies the location of the perspective center of the sensor, the A vector is a unit vector oriented in the direction the camera is pointing [2], and the H and V vectors are termed the horizontal and vertical vectors of the camera where H and V are first assumed to be orthogonal unit vectors and are termed H and V such that A, H, and V are mutually orthogonal [1]; these three vectors then define the image plane oriented to the real world coordinates. For a detailed explanation of all the information contained within the CAHV camera model, as well as its conversion to the photogrammetric model, see reference [2]. B. BackProjection When creating an orthoimage two of the common algorithms are forward and backprojection [6]. Forward projection creates a digitally orthorectified image by projecting the original image onto the DEM, calculating the object space coordinates, and then projected those into the new orthoimage. In contrast, back projection projects points from a resampled orthoimage onto the DEM and then into the image space of the original image in order to acquire the pixel value for the corresponding orthoimage coordinate. Back projection provides the advantage of resampling the image as it is created, while forward projection requires resampling after the projection process is complete [5]. Within the back projection algorithm, the relationship between the image and the object space of the DEM is defined by the collinearity principle. This principle states that the perspective center, the image plane coordinate, and the object point on the DEM all lie upon a straight line [5]. The collinearity equations are given by x = f m 11(X X c ) + m 12 (Y Y c ) + m 13 (Z Z c ) m 31 (X X c ) + m 32 (Y Y c ) + m 33 (Z Z c ) y = f m 21(X X c ) + m 22 (Y Y c ) + m 23 (Z Z c ) m 31 (X X c ) + m 32 (Y Y c ) + m 33 (Z Z c ) The components of the collinearity equations are as follows: (x, y)  Image coordinates (X, Y )  Object coordinates (1) The rotation matrix, M, is often produced by using three sequential rotations (ω, φ, and κ) [5]. However, when using the CAHV camera model this rotation matrix is calculated using the method given in [2]. H M = V (2) A The steps of the backprojection algorithm are 1 Resample the DEM to create an empty orthoimage grid space. 2 Interpolate the elevations across the new grid. 3 Use the collinearity relationship to project the object space coordinates into the source image. 4 Interpolate to obtain the intensity value for the current pixel in the orthoimage. As suggested in [6], computation time can be reduced by optimizing the back projection to be done with as few multiplications as possible. Instead, the numerator and denominator of the collinearity equations, given in Equation 1, are precalculated for use by additive increments instead of multiplications. This allows the projection algorithm to through each pixel using only additions and the division that is part of the collinearity relationship. C. FixedPoint Arithmetic One of the goals of the proposed implementation is to show the feasibility of an embedded backprojection algorithm on an FPGA or similar device. When targeting an FPGA, it is well known that integer based computations provide superior throughput performance over floating point [7]. Fixed point arithmetic allows for a software solution that provides both a speed increase and models the future hardware solution. Fixed point solutions allow the use of integers in place of floating point variables while retaining much of the precision of the original values. A numerical example most clearly illustrates the fixed point method used in this paper. A simple example using an addition between two floating point values is shown. λ represents a scale factor that is used to adjust the float value before storing it as an integer. f 1 = f 2 = λ = 6 x 1 = f 1 x 2 = f 2 (3) x 1 = 2589 x 2 = 2730
3 Fig. 2. From left to right: Projection of [4], proposed integerbased projection, and contrast adjusted difference image. At this point, the addition is undertaken using the variables x 1 and x 2. However, the true value is defined by the addition between f 1 and f 2. The scale factor variable above is initially chosen as a power of two because such a value allows the multiplication to be done using a bitwise shift; a bitwise shift is both computationally efficient in software and easily implemented in hardware. The result of x 1 + x 2 are binary shifted down before it is stored as the final value. floatingpoint f 1 + f 2 = fixed (x 1 + x 2 ) = As can be seen, the precision is retained to the tenths place of the true value. A larger scale factor can improve the precision. For example, precision to the 1000 ths place is achievable when using a scale factor of λ = 10; the result is The division within the collinearity equations can be replaced with a multiplication by precalculating a scaled multiplier. This multiplier, α, changes slightly with each pixel increment and must be included as an additional variable that is incremented as each pixel is analysed. α = ( m 31 (X X c ) + m 32 (Y Y c ) + m 33 (Z Z c ) ) (4) ˆx = [m 11 (X X c ) + m 12 (Y Y c ) + m 13 (Z Z c )] α (5) ŷ = [m 21 (X X c ) + m 22 (Y Y c ) + m 23 (Z Z c )] α (6) x ˆx = (7) y = ŷ The image position, x, is then scaled down with a bit shift to the true image coordinate. The same process is done for the column coordinate, y. An algorithmic comparison of the original method and the proposed integer method is given in the Appendix. (8) III. RETAINING PRECISION Typically, backprojection algorithms use double precision variables for calculating the projected image coordinates. One of the chief constraints when designing the integerbased implementation is to ensure that the resulting imagery is identical with respect to the original; the original being a floating point based backprojection algorithm based upon the theory in [4]. In order to measure this, peak signaltonoiseratio (PSNR) is utilized. Optimizing the scale factor, λ, is the main focus towards designing a system that retains the precision of the original. Figure 3 shows the test procedure to ensure accuracy of the proposed method. Fig. 3. Test Setup Figure 4 shows the results of the PSNR analysis on a set a 10 unique images. The scale factor, λ, is varied and the average MSE between the resulting set of imagery and the originals is recorded and the PSNR is calculated. Viewing this graph, one can see that the PSNR is close to ideal when λ = 36; therefore, for optimum results, a scale factor of 36 is chosen, as improvement is not seen past this point; specifically, for λ = 36, a PSNR of 59.4dB and MSE of are recorded. Figure 2 shows a zoomed 470 x 470 pixel section of a single processed image using each method. The resulting product is sufficiently similar to the original, with only a few pixel intensity variations across the entire image. Tests with larger data sets show consistent results; for example, on a set of 150 unique images the average MSE is found to be 0.08 with λ = 36.
4 separate from those used to test precision, and measures the time it takes to project each one. Fig. 4. Optimum Scale Factor Analysis; PSNR Fig. 7. Back Projection Computation Results IV. RESULTS This section highlights the timing results between the floating point and integerbased backprojection. The projection is computed on a single core of a Xeon X5460 processor running at 3.16Ghz with a 6MB cache and 32GB 667 MHz RAM. The results, shown in Figure 7, reveal an average throughput improvement of 27.6% and an average projection time of 3.67sec per frame when utilizing the interbased method; the floating point method projected each frame with an average time of 5.07sec when tested. V. CONCLUSION The fixed point integerbased solution for the back projection of aerial imagery provides a 27.6% improvement to throughput, while retaining image quality through the use of an optimized scale factor. This implementation shows the feasibility of an embedded solution, such as an FPGA, which will likely provide additional improvements to throughput speed for backprojecting imagery. REFERENCES Fig. 5. Algorithm Profiling of [4] Figures 5 and 6 show initial results from profiling the projection of a single image. In this case, the system is given an image of 9k x 9k pixels and projects it using a given CAHV camera model. As can be seen, the switch to an interbased implementation of the backprojection results in a noticeable decrease in processing time. [1] Yakimovsky, Y., and R. Cunningham (1978), A system for extracting threedimensional measurements from a stereo pair of TV cameras, Computer Graphics and Image Processing, 7, [2] Di, K., and R. Li (2004), CAHVOR camera model and its photogrammetric conversion for planetary applications, J. Geophys. Res., 109, E04004, doi: /2003je [3] Kasser, M., and Y. Egels (2002), Digital Photogrammetry, Taylor & Francis, New York. [4] MISR Science Team. Algorithm Theoretical Basis Documents. [Online] Available: homepage/for scientists/ atbd/viewinstrument.php?instrument=19. [5] Mikhail, E. M., J.S. Bethel, and J.D. McGlone (2001), Introduction to Modern Photogrammetry, John Wiley, New York. [6] Novak, K Rectification of digital imagery, Photogrammetric Engineering & Remote Sensing, 58(3): [7] MeyerBaese, U. (2007), Digital Signal Processing with Field Programmable Gate Arrays, Springer, New York [8] Asano, S., T. Maruyama, and Y. Yamaguchi (2009), Performance comparison of fpga, gpu and cpu in image processing, International Conference on Field Programmable Logic and Applications, 2009, pp Fig. 6. Algorithm Profiling of Proposed IntegerBased Projection The system is then tested while simulating real world operation. The test data for this consists of 660 individual images,
5 APPENDIX The notable aspects of the original and integerbased algorithms are outlined below. The main differences are discussed. For reference, x, y, and z are the change in each respective dimension across the DEM cells. The variables with num subscripts are the respective numerators within the collinearity equation for both dimensions; D is the denominator. Algorithm 1 Floating Point Procedure Loop through DEM region xp num = [m 11 (X X c )+m 12 (Y Y c )+m 13 (Z Z c )] yp num = [m 21 (X X c )+m 22 (Y Y c )+m 23 (Z Z c )] D = [m 31 (X X c ) + m 32 (Y Y c ) + m 33 (Z Z c )] x num = x (m 11 ) + z (m 13 ) y num = y (m 21 ) + z (m 23 ) D = x (m 31 ) + z (m 33 ) Loop through subsampled DEM cell xp = xpnum D yp = ypnum D xp num = xp num + xp num yp num = yp num + yp num D = D + D Algorithm 2 IntegerBased Procedure Loop through DEM region xp num = [m 11 (X X c )+m 12 (Y Y c )+m 13 (Z Z c )] yp num = [m 21 (X X c )+m 22 (Y Y c )+m 23 (Z Z c )] D = [m 31 (X X c ) + m 32 (Y Y c ) + m 33 (Z Z c )] x num = x (m 11 ) + z (m 13 ) y num = y (m 21 ) + z (m 23 ) D = x (m 31 ) + z (m 33 ) Λ = 2λ D Λ = 2λ D D(D+ D) xp = Λ xp num yp = Λ yp num xp = xp λ yp = yp λ Loop through subsampled DEM cell xp num = xp num + xp num yp num = yp num + yp num Λ = Λ + Λ Interpolate gray level of the current pixel end end Interpolate grey level of the current pixel end end The floating point algorithm functions by processing each individual cell within the DEM. Each cell is iterated over in accordance with the subsampling rate and the grey values for the pixels are interpolated from the source image. The integerbased algorithm adds a scaled multiplier, Λ, which scales the x and y coordinates and allows for the avoidance of the collinearity division. The scaling is done by a power of two in order to create a situation in which the true coordinate can be retrieved by a simple bitwise shift. The initial xp and yp values within the inner are integer multiplies; both xp num and yp num are cast to integers before entering the. Note, the inner of the modified algorithm contains no divisions; instead, it is composed only of additions, shifts, and multiplies.
Photogrammetry: DTM Extraction & Editing
Photogrammetry: DTM Extraction & Editing How can one determine the x, y, and z of a location? Approaches to DTM Extraction Ground surveying Digitized topographic maps Traditional photogrammetry Hardcopy
More informationAnalysis of GPU Parallel Computing based on Matlab
Analysis of GPU Parallel Computing based on Matlab Mingzhe Wang, Bo Wang, Qiu He, Xiuxiu Liu, Kunshuai Zhu (School of Computer and Control Engineering, University of Chinese Academy of Sciences, Huairou,
More informationLeica Photogrammetry Suite Project Manager
Leica Photogrammetry Suite Project Manager Copyright 2006 Leica Geosystems Geospatial Imaging, LLC All rights reserved. Printed in the United States of America. The information contained in this document
More informationGraphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:
More informationPHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY
PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY V. Knyaz a, *, Yu. Visilter, S. Zheltov a State Research Institute for Aviation System (GosNIIAS), 7, Victorenko str., Moscow, Russia
More informationPHOTOGRAMMETRIC TRIANGULATION OF 3D CUBIC SPLINES INTRODUCTION
PHOTOGRAMMETRIC TRIANGULATION OF 3D CUBIC SPLINES Keith F. Blonquist, Research Engineer Robert T. Pack, Associate Professor Utah State University 4110 Old Main Hill Logan, UT 84322 ABSTRACT A common application
More informationREAL TIME 3D FUSION OF IMAGERY AND MOBILE LIDAR INTRODUCTION
REAL TIME 3D FUSION OF IMAGERY AND MOBILE LIDAR Paul Mrstik, Vice President Technology Kresimir Kusevic, R&D Engineer Terrapoint Inc. 1401 Antares Dr. Ottawa, Ontario K2E 8C4 Canada paul.mrstik@terrapoint.com
More informationComparison of Intel Pentium III and Pentium 4 Processor Performance
White Paper October 2001 Prepared by: Workstations Division Engineering Compaq Computer Corporation Contents Introduction... 3 Comparison of Pentium 4 and Pentium III Architecture Benefits... 3 Case for
More informationGraphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:
More informationHigh Resolution Digital Surface Models and Orthoimages for Telecom Network Planning
Renouard, Lehmann 241 High Resolution Digital Surface Models and Orthoimages for Telecom Network Planning LAURENT RENOUARD, S ophia Antipolis FRANK LEHMANN, Berlin ABSTRACT DLR of Germany and ISTAR of
More informationHardwareAware Analysis and. Presentation Date: Sep 15 th 2009 Chrissie C. Cui
HardwareAware Analysis and Optimization of Stable Fluids Presentation Date: Sep 15 th 2009 Chrissie C. Cui Outline Introduction Highlights Flop and Bandwidth Analysis Mehrstellen Schemes Advection Caching
More informationQuality Report Generated with Pro version 2.2.5
Quality Report Generated with Pro version 2.2.5 Important: Click on the different icons for: Help to analyze the results in the Quality Report Additional information about the sections Click here for additional
More information3D Graphics Hardware Graphics II Spring 1999
3D Graphics Hardware 15463 Graphics II Spring 1999 Topics Graphics Architecture Uniprocessor Acceleration FrontEnd Multiprocessing Pipelined Parallel BackEnd Multiprocessing Pipelined Parallel Graphics
More informationDefinition of Photogrammetry. Aircraft: Cessna 206 Turbo Charged
LSIT RLS Review Seminar: Photogrammetry John Cahoon Certified Photogrammetrist President Kenney Aerial Mapping, Inc. 4008 North 15 th Avenue Phoenix, AZ 85015 6022586471 jcahoon@kamaz.com www.kamaz.com
More informationReal Time Image Rotation and Resizing, Algorithms and Implementations
Real Time Image Rotation and Resizing, Algorithms and Implementations Robert D. Turney and Chris H. Dick CORE SOLUTIONS GROUP, XILINX, INC. 2100 LOGIC DRIVE SAN JOSE, CA 951243450 ABSTRACT Recent growth
More informationGeometric transformations and registration of images, orthoimage generation and mosaicing
Geometric transformations and registration of images, orthoimage generation and mosaicing E.P. Baltsavias Institute of Geodesy and Photogrammetry, ETHZ Zurich E.P. Baltsavias, Athens, 5.2000, p.1 Geometric
More informationDigital Orthophoto Production In the Desktop Environment 1
Digital Orthophoto Production In the Desktop Environment 1 By Dr. Roy A. Welch and Thomas R. Jordan Digital orthophotos are proving suitable for a variety of mapping, GIS and environmental monitoring tasks.
More informationXilinx FPGA Implementation of a Pixel Processor for Object Detection Applications
Xilinx FPGA Implementation of a Pixel Processor for Object Detection Applications Peter Mc Curry, Fearghal Morgan, Liam Kilmartin Communications and Signal Processing Research Unit, Department of Electronic
More informationA FPGA based Generic Architecture for Polynomial Matrix Multiplication in Image Processing
A FPGA based Generic Architecture for Polynomial Matrix Multiplication in Image Processing Prof. Dr. S. K. Shah 1, S. M. Phirke 2 Head of PG, Dept. of ETC, SKN College of Engineering, Pune, India 1 PG
More informationOrthophotography CHAPTER GENERAL
CHAPTER 14 Orthophotography 14.1 GENERAL Today, many uses for geospatial mapping products require current planimetric feature data. Analysis and design from geospatial data sets generally require a known
More informationCalibration and Georeferencing of Aerial Digital Cameras
'Photogrammetric Week 05' Dieter Fritsch, Ed. Wichmann Verlag, Heidelberg 2005. Hofmann 105 Calibration and Georeferencing of Aerial Digital Cameras OTTO HOFMANN, Brunnthal ABSTRACT The conventional determination
More informationFixedpoint Mathematics
Appendix A Fixedpoint Mathematics In this appendix, we will introduce the notation and operations that we use for fixedpoint mathematics. For some platforms, e.g., lowcost mobile phones, fixedpoint
More informationTHE CONTROL OF A ROBOT ENDEFFECTOR USING PHOTOGRAMMETRY
THE CONTROL OF A ROBOT ENDEFFECTOR USING PHOTOGRAMMETRY Dr. T. Clarke & Dr. X. Wang Optical Metrology Centre, City University, Northampton Square, London, EC1V 0HB, UK t.a.clarke@city.ac.uk, x.wang@city.ac.uk
More informationA Computer Vision System on a Chip: a case study from the automotive domain
A Computer Vision System on a Chip: a case study from the automotive domain Gideon P. Stein Elchanan Rushinek Gaby Hayun Amnon Shashua Mobileye Vision Technologies Ltd. Hebrew University Jerusalem, Israel
More informationPractitioner s Guide:
www.methodfinder.net Practitioner s Guide: Low Cost Amateur Aerial Pictures with Balloon and Digital Camera Low Cost Orthophoto Production in Battambang Town, Cambodia Bundesministerium für wirtschaftliche
More informationTest Results Using the DTM Software of PCI
M. Schlüter, B.S. Schulz, C. Wende Test Results Using the DTM Software of PCI Bundesamt für Kartographie und Geodäsie RichardStraussAllee 11 60598Frankfurt (Main) Internet: Email: http://www.ifag.de
More informationCS231M Project Report  Automated RealTime Face Tracking and Blending
CS231M Project Report  Automated RealTime Face Tracking and Blending Steven Lee, slee2010@stanford.edu June 6, 2015 1 Introduction Summary statement: The goal of this project is to create an Android
More informationWhite paper: How accurate are UAV surveying methods?
White paper: How accurate are UAV surveying methods? How accurate is UAV surveying? Testing stockpile volumetrics to get your answer. A comparison between Pix4D UAV photogrammetry software and GNSS / terrestrial
More informationShear :: Blocks (Video and Image Processing Blockset )
1 of 6 15/12/2009 11:15 Shear Shift rows or columns of image by linearly varying offset Library Geometric Transformations Description The Shear block shifts the rows or columns of an image by a gradually
More informationSpeed Performance Improvement of Vehicle Blob Tracking System
Speed Performance Improvement of Vehicle Blob Tracking System Sung Chun Lee and Ram Nevatia University of Southern California, Los Angeles, CA 90089, USA sungchun@usc.edu, nevatia@usc.edu Abstract. A speed
More informationA Survey of Video Processing with Field Programmable Gate Arrays (FGPA)
A Survey of Video Processing with Field Programmable Gate Arrays (FGPA) Heather Garnell Abstract This paper is a highlevel, survey of recent developments in the area of video processing using reconfigurable
More informationOBJECT'S SURFACE ROUGHNESS MEASUREMENT USING A HIGH RESOLUTION DIGITAL CAMERA
OBJECT'S SURFACE ROUGHNESS MEASUREMENT USING A HIGH RESOLUTION DIGITAL CAMERA Hyosung LEE, Yangdam EO, Yongil KIM and Kiwon AHN, Korea ABSTRACT This study aims to present an extraction of the threedimensional
More informationComputer Graphics Hardware An Overview
Computer Graphics Hardware An Overview Graphics System Monitor Input devices CPU/Memory GPU Raster Graphics System Raster: An array of picture elements Based on rasterscan TV technology The screen (and
More informationVideoRate Stereo Vision on a Reconfigurable Hardware. Ahmad Darabiha Department of Electrical and Computer Engineering University of Toronto
VideoRate Stereo Vision on a Reconfigurable Hardware Ahmad Darabiha Department of Electrical and Computer Engineering University of Toronto Introduction What is Stereo Vision? The ability of finding the
More informationParallel Computing with MATLAB
Parallel Computing with MATLAB Scott Benway Senior Account Manager Jiro Doke, Ph.D. Senior Application Engineer 2013 The MathWorks, Inc. 1 Acceleration Strategies Applied in MATLAB Approach Options Best
More informationARM CortexA* Series Processors. Haoyang Lu, Zheng Lu, Yong Li, James Cortese
ARM CortexA* Series Processors Haoyang Lu, Zheng Lu, Yong Li, James Cortese ARM CortexA* Series Processors Applications Instruction Set Multicore Memory Management Exclusive Features ARM CortexA* series:
More informationGeoImaging Accelerator Pansharp Test Results
GeoImaging Accelerator Pansharp Test Results Executive Summary After demonstrating the exceptional performance improvement in the orthorectification module (approximately fourteenfold see GXL Ortho Performance
More informationRESEARCHES ON HAZARD AVOIDANCE CAMERAS CALIBRATION OF LUNAR ROVER
ICSO International Conference on Space Optics 48 October RESERCHES ON HZRD VOIDNCE CMERS CLIBRTION OF LUNR ROVER Li Chunyan,,Wang Li,,LU Xin,,Chen Jihua 3,Fan Shenghong 3. Beijing Institute of Control
More informationA PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA
A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA N. Zarrinpanjeh a, F. Dadrassjavan b, H. Fattahi c * a Islamic Azad University of Qazvin  nzarrin@qiau.ac.ir
More informationCreating a Digital Surface Model from Airborne LIDAR ASCII Coordinate Data
Creating a Digital Surface Model from Airborne LIDAR ASCII Coordinate Data TUTORIAL Light Detection and Ranging (LIDAR) techniques use similar principles to those of Radio Detection and Ranging (RADAR)
More informationAutomatic, CLERMONTFERRAND, FRANCE
1 Dorin ROIBAN, 2 Lionel DAMEZ, 3 JeanPierre DERUTIN 1 Blaise Pascal University, LASMEA/Laboratory of Sciences and Materials for Electronics and of Automatic, VIRO / VIsion and RObotic, CLERMONTFERRAND,
More informationFloating Point Engine using VHDL
Floating Point Engine using VHDL Najib Ghatte #1, Shilpa Patil #2, Deepak Bhoir #3 Fr. Conceicao Rodrigues College of Engineering Fr. Agnel Ashram, Bandstand, Bandra (W), Mumbai: 400 050, India Abstract
More informationC4 Computer Vision. 4 Lectures Michaelmas Term Tutorial Sheet Prof A. Zisserman. fundamental matrix, recovering egomotion, applications.
C4 Computer Vision 4 Lectures Michaelmas Term 2004 1 Tutorial Sheet Prof A. Zisserman Overview Lecture 1: Stereo Reconstruction I: epipolar geometry, fundamental matrix. Lecture 2: Stereo Reconstruction
More informationMixed Precision Iterative Refinement Methods Energy Efficiency on Hybrid Hardware Platforms
Mixed Precision Iterative Refinement Methods Energy Efficiency on Hybrid Hardware Platforms Björn Rocker Hamburg, June 17th 2010 Engineering Mathematics and Computing Lab (EMCL) KIT University of the State
More informationCapturing Road Network Data Using Mobile Mapping Technology
Capturing Road Network Data Using Mobile Mapping Technology Guangping He, Greg Orvets Lambda Tech International, Inc. Waukesha, WI53186, USA he@lambdatech.com KEY WORDS: DATA CAPTURE, MOBILE MAPPING,
More informationEmbedded Systems Lecture 15: HW & SW Optimisations. Björn Franke University of Edinburgh
Embedded Systems Lecture 15: HW & SW Optimisations Björn Franke University of Edinburgh Overview SW Optimisations FloatingPoint to FixedPoint Conversion HW Optimisations ApplicationSpecific Instruction
More informationDigital Photogrammetric System. Version 6.0.2 USER MANUAL. Block adjustment
Digital Photogrammetric System Version 6.0.2 USER MANUAL Table of Contents 1. Purpose of the document... 4 2. General information... 4 3. The toolbar... 5 4. Adjustment batch mode... 6 5. Objects displaying
More informationLabVIEW Day 3: Arrays and Clusters
LabVIEW Day 3: Arrays and Clusters Vern Lindberg By now you should be getting used to LabVIEW. You should know how to Create a Constant, Control, or Indicator. I will assume you know how to create a new
More informationFractal Video Compression in OpenCL: An Evaluation of CPUs, GPUs, and FPGAs as Acceleration Platforms. Doris Chen, Deshanand Singh Jan 24 th, 2013
Fractal Video Compression in OpenCL: An Evaluation of CPUs, GPUs, and FPGAs as Acceleration Platforms Doris Chen, Deshanand Singh Jan 24 th, 2013 Platform Evaluation Challenges Conducting a fair platform
More informationTECHNOLOGY. How an Architectural Design Firm Leverages Virtual GPU Technology for Global Collaboration
TECHNOLOGY GPU CONFERENCE 2016 How an Architectural Design Firm Leverages Virtual GPU Technology for Global Collaboration What are we going to learn today? Why CannonDesign decided to virtualize our infrastructure
More informationRealScape Series Fixed Assets Change Judgment System
Business Innovation / Products and Technologies RealScape Series Fixed Assets Change Judgment System KUNIEDA Kazuo, IWATA Makoto, HASHIZUME Kazuaki, KOIZUMI Hirokazu Abstract The Tokyo Metropolitan Government,
More informationImplementation of Canny Edge Detector of color images on CELL/B.E. Architecture.
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture. Chirag Gupta,Sumod Mohan K cgupta@clemson.edu, sumodm@clemson.edu Abstract In this project we propose a method to improve
More informationLet s put together a Manual Processor
Lecture 14 Let s put together a Manual Processor Hardware Lecture 14 Slide 1 The processor Inside every computer there is at least one processor which can take an instruction, some operands and produce
More informationNotes on the Use of Multiple Image Sizes at OpenCV stereo
Notes on the Use of Multiple Image Sizes at OpenCV stereo Antonio Albiol April 5, 2012 Abstract This paper explains how to use different image sizes at different tasks done when using OpenCV for stereo
More informationAnnex A1 COURSE DESCRIPTIONS AND LEARNING OBJECTIVES FOR:
Annex A1 COURSE DESCRIPTIONS AND LEARNING OBJECTIVES FOR: Discrete Math (Already in ACGM, course description has been revised, and learning outcomes have been added.) Electrical Circuits I (Already in
More informationIntroduction. www.imagesystems.se
Product information Image Systems AB Main office: Ågatan 40, SE582 22 Linköping Phone +46 13 200 100, fax +46 13 200 150 info@imagesystems.se, Introduction Motion is the world leading software for advanced
More informationMove From Design to Deployment Faster. ni.com
What s New in LabVIEW RealTime and LabVIEW FPGA Move From Design to Deployment Faster Supporting Embedded Designers Through Integrated System Design Software Communication Interface Processing Elements
More informationAdaptive Stable Additive Methods for Linear Algebraic Calculations
Adaptive Stable Additive Methods for Linear Algebraic Calculations József Smidla, Péter Tar, István Maros University of Pannonia Veszprém, Hungary 4 th of July 204. / 2 József Smidla, Péter Tar, István
More informationHigh Resolution RF Analysis: The Benefits of Lidar Terrain & Clutter Datasets
0 High Resolution RF Analysis: The Benefits of Lidar Terrain & Clutter Datasets January 15, 2014 Martin Rais 1 High Resolution Terrain & Clutter Datasets: Why Lidar? There are myriad methods, techniques
More informationGMP implementation on CUDA  A Backward Compatible Design With Performance Tuning
1 GMP implementation on CUDA  A Backward Compatible Design With Performance Tuning Hao Jun Liu, Chu Tong Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto haojun.liu@utoronto.ca,
More information3. INNER PRODUCT SPACES
. INNER PRODUCT SPACES.. Definition So far we have studied abstract vector spaces. These are a generalisation of the geometric spaces R and R. But these have more structure than just that of a vector space.
More informationUsing MATLAB to Measure the Diameter of an Object within an Image
Using MATLAB to Measure the Diameter of an Object within an Image Keywords: MATLAB, Diameter, Image, Measure, Image Processing Toolbox Author: Matthew Wesolowski Date: November 14 th 2014 Executive Summary
More informationFiles Used in this Tutorial
Generate Point Clouds Tutorial This tutorial shows how to generate point clouds from IKONOS satellite stereo imagery. You will view the point clouds in the ENVI LiDAR Viewer. The estimated time to complete
More informationAccident Scene Diagramming Using New Photogrammetric Technique
970944 Accident Scene Diagramming Using New Photogrammetric Technique Stephen Fenton, Richard Kerr, Knott Laboratory, Inc. ABSTRACT One of the challenges for accident reconstructionists is creating accurate
More informationQUANTITATIVE ANALYSIS OF IMAGE QUALITY OF LOSSY COMPRESSION IMAGES
QUANTITATIVE ANALYSIS OF IMAGE QUALITY OF LOSSY COMPRESSION IMAGES Ryuji Matsuoka*, Mitsuo Sone, Kiyonari Fukue, Kohei Cho, Haruhisa Shimoda Tokai University Research & Information Center 2284 Tomigaya,
More informationNonlinear Iterative Partial Least Squares Method
Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., RichardPlouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for
More informationBHARATHIAR UNIVERSITY: COIMBATORE CENTRE FOR COLLABORATION OF INDUSTRY AND INSTITUTIONS(CCII) CERTIFICATE IN ADVANCED PROGRAMMING C++ LANGUAGE
Certificate in Advanced Programming  C++ Language Page 1 of 7 BHARATHIAR UNIVERSITY: COIMBATORE 641046 CENTRE FOR COLLABORATION OF INDUSTRY AND INSTITUTIONS(CCII) CERTIFICATE IN ADVANCED PROGRAMMING C++
More informationLecture 3: Single processor architecture and memory
Lecture 3: Single processor architecture and memory David Bindel 30 Jan 2014 Logistics Raised enrollment from 75 to 94 last Friday. Current enrollment is 90; C4 and CMS should be current? HW 0 (getting
More informationGrade 6 Mathematics Performance Level Descriptors
Limited Grade 6 Mathematics Performance Level Descriptors A student performing at the Limited Level demonstrates a minimal command of Ohio s Learning Standards for Grade 6 Mathematics. A student at this
More informationMPEG2 Encode. An Industry Standard Benchmark Consortium. Highlights Five different test files to stress different aspect of encoders
DENBench Version 1.0 Benchmark Name: MPEG2 Encode Highlights Five different test files to stress different aspect of encoders Floating point and integer implementations Implements PSNR to check output
More informationArithmetic Operations
Arithmetic Operations Dongbing Gu School of Computer Science and Electronic Engineering University of Essex UK Spring 2013 D. Gu (Univ. of Essex) Arithmetic Operations Spring 2013 1 / 34 Outline 1 Introduction
More informationTHE REVISION OF IRANIAN 1:25000 SCALE TOPOGRAPHIC MAPS BY KVR1000 IMAGE USING RATIONAL FUNCTION MODEL
THE REVISION OF IRANIAN 1:25000 SCALE TOPOGRAPHIC MAPS BY KVR1000 IMAGE USING RATIONAL FUNCTION MODEL a b a G. Jamebozorg, M. J. Valadan Zoej, S. Sadeghian a National Cartographic Center, Tehran, Iran,
More informationC programming: exercise sheet L2STUE (20112012)
C programming: exercise sheet L2STUE (20112012) Algorithms and Flowcharts Exercise 1: comparison Write the flowchart and associated algorithm that compare two numbers a and b. Exercise 2: 2 nd order
More informationQGIS plugin or web app? Lessons learned from the development of a 3D georeferencer.
QGIS plugin or web app? Lessons learned from the development of a 3D georeferencer. Produit Timothée 1, Jens Ingensand 2, and Gillian Milani 3 12 University of Applied Sciences Western Switzerland, G2C
More informationAccelerating CFD using OpenFOAM with GPUs
Accelerating CFD using OpenFOAM with GPUs Authors: Saeed Iqbal and Kevin Tubbs The OpenFOAM CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. Its user base represents a wide
More informationCopyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and
Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere
More informationFeasibility Study of Searchable Image Encryption System of Streaming Service based on Cloud Computing Environment
Feasibility Study of Searchable Image Encryption System of Streaming Service based on Cloud Computing Environment JongGeun Jeong, ByungRae Cha, and Jongwon Kim Abstract In this paper, we sketch the idea
More informationSolution of Linear Systems
Chapter 3 Solution of Linear Systems In this chapter we study algorithms for possibly the most commonly occurring problem in scientific computing, the solution of linear systems of equations. We start
More informationG405 Specification and application
G405 Specification and application Pure hardware 4k/2k four channel Video Wall Processor Support 4k/2k input in HDMI, DP & DVI input ports Support video wall array up to 15x15 Image 180 degree flip function
More informationVirtual Mouse Implementation using Color Pointer Detection
International Journal of Research Studies in Science, Engineering and Technology Volume 1, Issue 5, August 2014, PP 2332 ISSN 23494751 (Print) & ISSN 2349476X (Online) Virtual Mouse Implementation using
More informationLecture 19 Camera Matrices and Calibration
Lecture 19 Camera Matrices and Calibration Project Suggestions Texture Synthesis for InPainting Section 10.5.1 in Szeliski Text Project Suggestions Image Stitching (Chapter 9) Face Recognition Chapter
More informationRecent Advances and Future Trends in Graphics Hardware. Michael Doggett Architect November 23, 2005
Recent Advances and Future Trends in Graphics Hardware Michael Doggett Architect November 23, 2005 Overview XBOX360 GPU : Xenos Rendering performance GPU architecture Unified shader Memory Export Texture/Vertex
More information3D Scanner using Line Laser. 1. Introduction. 2. Theory
. Introduction 3D Scanner using Line Laser Di Lu Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute The goal of 3D reconstruction is to recover the 3D properties of a geometric
More informationVLSI BASED COLOR INTERPOLATION ALGORITHM FOR REAL TIME IMAGE APPLICATIONS
VOL. 10, NO. 7, APRIL 2015 ISSN 18196608 VLSI BASED COLOR INTERPOLATION ALGORITHM FOR REAL TIME IMAGE APPLICATIONS Sudalai Utchimahali C. 1 and Rajakumar G. 2 1 M.E VLSI Design, Francis Xavier Engineering
More informationA VOXELIZATION BASED MESH GENERATION ALGORITHM FOR NUMERICAL MODELS USED IN FOUNDRY ENGINEERING
METALLURGY AND FOUNDRY ENGINEERING Vol. 38, 2012, No. 1 http://dx.doi.org/10.7494/mafe.2012.38.1.43 Micha³ Szucki *, Józef S. Suchy ** A VOXELIZATION BASED MESH GENERATION ALGORITHM FOR NUMERICAL MODELS
More informationEpipolar Geometry Prof. D. Stricker
Outline 1. Short introduction: points and lines Epipolar Geometry Prof. D. Stricker 2. Two views geometry: Epipolar geometry Relation point/line in two views The geometry of two cameras Definition of the
More informationAlgorithm and Programming Considerations for Embedded Reconfigurable Computers
Algorithm and Programming Considerations for Embedded Reconfigurable Computers Russell Duren, Associate Professor Engineering And Computer Science Baylor University Waco, Texas Douglas Fouts, Professor
More informationAssociative Property The property that states that the way addends are grouped or factors are grouped does not change the sum or the product.
addend A number that is added to another in an addition problem. 2 + 3 = 5 The addends are 2 and 3. area The number of square units needed to cover a surface. area = 9 square units array An arrangement
More informationUltimate Optics Testing
Ultimate Optics Testing New Generation Of Optics Tester Unrivaled performance based on latest advances in wavefront analysis A step beyond for measuring MTF, PSF, Aberrations Meet the most demanding metrology
More informationECE 842 Report Implementation of Elliptic Curve Cryptography
ECE 842 Report Implementation of Elliptic Curve Cryptography WeiYang Lin December 15, 2004 Abstract The aim of this report is to illustrate the issues in implementing a practical elliptic curve cryptographic
More informationQuantization Error and AccuracyPerformance Tradeoffs for Embedded Data Mining Workloads
Quantization Error and AccuracyPerformance Tradeoffs for Embedded Data Mining Workloads Ramanathan Narayanan, Berkin Özıṣıkyılmaz, Gokhan Memik, Alok Choudhary, and Joseph Zambreno Department of Electrical
More informationAPPM4720/5720: Fast algorithms for big data. Gunnar Martinsson The University of Colorado at Boulder
APPM4720/5720: Fast algorithms for big data Gunnar Martinsson The University of Colorado at Boulder Course objectives: The purpose of this course is to teach efficient algorithms for processing very large
More informationImage Compression through DCT and Huffman Coding Technique
International Journal of Current Engineering and Technology EISSN 2277 4106, PISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul
More informationCryptanalysis of Linear Congruence Generators
Cryptanalysis of Linear Congruence Generators Abstract Multiplicative congruential generators have been first suggested by D.H.Lehmer as an arithmetic procedure to generate pseudo random numbers. A mild
More informationAn Improved FloatingtoFixedPoint Conversion Scheme for DCT Quantization Algorithm
J Sign rocess Syst (2012) 66:135 139 DOI 10.1007/s1126501105901 An Improved FloatingtoFixedoint Conversion Scheme for DCT Quantization Algorithm Lin Wang Fuliang Yin Zhe Chen Received: 29 March 2010
More informationCustomizing Computational Methods for Visual Analytics with Big Data
BigData Visualization Customizing Computational Methods for Visual Analytics with Big Data Jaegul Choo and Haesun Park Georgia Tech O wing to the complexities and obscurities in largescale datasets (
More informationWavelet based ECW image compression
'Photogrammetric Week 01' D. Fritsch & R. Spiller, Eds. Wichmann Verlag, Heidelberg 2001. Ueffing 299 Wavelet based ECW image compression CHRISTOPH UEFFING, Merzhausen ABSTRACT The wavelet based ECW image
More informationImage registration in DataViewer
Image registration in DataViewer A brief user guide Starting from version 1.5.0.0, DataViewer provides tools for image registration for both 2D and 3D. In both cases, only rigid transformation is considered.
More informationW H I T E P A P E R. Volumetric Measure Using Geospatial Technology
W H I T E P A P E R Volumetric Measure Using Geospatial Technology Contents 1. Introduction... 1 2. Project Setup/Triangulation... 1 3. Workflow One: Extract DSM Terrain File... 1 3.1. Stereo Terrain Editing...
More information