Discovering Canvas Orientation of Van Gogh Paintings *
|
|
- Jerome Price
- 5 years ago
- Views:
Transcription
1 OpenStax-CNX module: m Discovering Canvas Orientation of Van Gogh Paintings * Nirali Desai This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License Art Matters In Vincent Van Gogh's lifetime, he sold a single painting. Today, he is one of the most famous artists of all time. He created over 900 paintings in a short career that lasted only a decade. Art historians studying Van Gogh need the help of technology to analyze paintings and determine the probability of them being counterfeits. The letters left behind by Van Gogh indicate the rolls of canvas he received for painting were delivered by his brother. Professor Don Johnson has worked closely with the Van Gogh Museum in Amsterdam to determine which paintings came from the same bolts of canvas. Using signal processing allows us to determine the location of a painting on a roll of canvas used by Van Gogh, which would help art historians and their study of Van Gogh's works. 2 Van Gogh's Canvas Preparation Van Gogh used canvases brought to him by his brother from a canvas priming company. Van Gogh would get a 2.10 m by 10 m roll and cut smaller portions of the canvas to use for his paintings. The canvas was stretched by rst nailing the top side to a board. Then, the bottom side was secured to another board using a hook and lace method. The canvas was primed, removed from the boards, rolled up, and sold. Since the nails were more closely spaced together than the hooks, we can analyze whether a painting was cut from the top or bottom of the original sheet of canvas. Notable concerns arise when a middle group is formed that does not match with either of the narrow spacings of nails or the wider spacings of the hook-and-lace mechanism. A representation of the setup of a canvas while being primed. The black dots at the top edge are nails. The bottom edge has hooks that are laced to the bottom board. * Version 1.1: Dec 18, :02 pm
2 OpenStax-CNX module: m Basis for Clique Formation Cliques, by denition, represent a group of paintings that come from the same bolt of canvas. A bolt of canvas is approximately 100 m of canvas. The canvas is made in bolts and then cut into 10 m rolls to be primed. Don Johnson sorted and separated Van Gogh's works into cliques based on thread density, angle measurements dependent on the departure of horizontal and vertical threads from the coordinate axes, and thread count. Our project involves the analysis of these cliques by examining the location of specic paintings in each clique. In order to determine where a painting was cut from a given roll of canvas, we sort paintings by analyzing the distance between nails that were present during the priming process for the roll of canvas. It is important to understand bolts and rolls are dierent. Bolts are 100 m of canvas while rolls are 10 m of canvas cut from a bolt. Canvas is created in bolts and later cut and primed in rolls. Since Don Johnson looked at the weaves of canvas, he was able to sort the paintings into which bolt they came from. Later, we group some pdf plots into the "left" and "right" sides of a clique. Left and right are words Don Johnson uses to describe which side of the bolt the painting came from. We are sorting the painting into "top" and "bottom" clusters. Top and bottom refer to canvas rolls. Top means the painting was near the nail side while bottom means the painting was closer to the hook side. All paintings that are on one side of a bolt are not necessarily on the same side for priming. Once the canvas was cut in 10 m rolls, it could have been ipped around in the process of priming. 4 The K-Means Algorithm K-means is an algorithm that can be used to sort data into a given number of clusters. It rst estimates an initial centroid for each cluster. Then, it assigns each data point to a cluster based on the centroid. The centroids are then recalculated for each cluster based of the data points that fall within each one. The data is reassigned, and the centroids are recalculated until they no longer change. When implementing the k-means algorithm for this project, we did not code the function by hand and instead relied on MATLAB's built in k-means function for our calculations. When clustering, the value of k is decided by experimentation. This means identifying a value of k for which clusters appear to be most spatially dense. We rst tried to calculate the k-means of cliques by clustering with the probability density function of variable ysmooth. Ysmooth represents a discrete pdf lled with the nail spacings of a whole clique, which is interpolated by convolving with the Hanning window, and then normalized. However, we then calculated the k-means of dierent cliques by clustering according to the mean and standard deviation of nail spacings in the selected clique. With this, we obtained more meaningful results because clusters created using this method are more spatially dense. As such, we are choosing to cluster according to mean and standard deviation. 5 Coded Implementation We worked with 4 dierent MATLAB les while trying to implement the k-means algorithm: Files from Professor Don Johnson: nail_data: Contains data of 5 dierent cliques with varying numbers of paintings. Data structures are organized by cliques, painting name, and nail spacings. The clique and nail spacing of each painting is gathered by nail_data. nailprint: Prints out formatted data from nail_data. Paintings are organized by cliques and then paintings present within each clique. Subsequent data is representative of spacings. nailplot: Calling nailplot will always print out Figures 15 and 16. nailplot: Calling nailplot will always print out Figures 15 and 16. Figure 15 is a histogram of paintings colored by their clique, and then sorted into left and right. Left is indicative of hook-and-lace implementation, while right indicates a use of nails at the edge of a painting.
3 OpenStax-CNX module: m Figure 16 has separate histograms for each clique, and plots nail spacing (cm) vs. percentage of nails at that spacing for the given clique. The histograms for each clique also delineate right from left, but only for the paintings in the specied clique. Figure 17 and 18 will be printed out if a clique is specied in a command line. Figure 17 prints out the nail spacing for every painting in the specied clique on separate histograms. Nail spacing (cm) vs. frequency of nail spacing for all paintings sorted into the left group. Figure 18 is identical to Figure 17, except specic to the right group. Our le: nailcluster: 1. Convolved vector of nail spacings for each painting with normal pdf, and then used Hanning window to interpolate. While this code was not used in the actual clustering, it was important in obtaining the mean and standard deviation parameters of each clique. 2. Used the normalized pdf vector to obtain mean and standard deviation parameters for clustering. 3. Performed a k-means clustering of each clique by implementing the kmeans function built into MAT- LAB. 4. Calling nailcluster will print out 3 gures: a) The rst gure will be a plot of Frequency vs. Spacings. Frequency represents the number of occurrences of each width of nail or hook-and-lace spacing. b) The second gure will be a plot of the pdfs for the left side of each clique. This separates the data by each painting within a given clique. c) The third gure will be a plot of the pdfs for the right side of each clique and separates data in the same way as the second gure. 6 Clustered Paintings by Spacings When running the code for Clique 1, we obtain the following results:
4 OpenStax-CNX module: m The pdf plots for the left side of Clique 1: The pdf plots for the right side of Clique 1:
5 OpenStax-CNX module: m Clique 1 is an interesting clique. After interpolating the values for nail spacing, convolving them to form a normalized pdf, and performing k-means clustering, we plot the value for spacings as shown above. The plot indicates that there exists a middle, unknown, group of nail spacings which occur on any given side for a painting. The green cluster could potentially be classied as noise. More data may give rise to a distinct new clique but with our current data, we can't be sure. If a third cluster did exist, Professor Don Johnson's theory is that Van Gogh might have received canvas rolls primed at a dierent facility. This facility, instead of using the recognizable nail and hook-and-lace methods, would have primed the canvas in similar ways for both the top and the bottom of the frame. When running the code for Clique 3, we obtain the following results:
6 OpenStax-CNX module: m The pdf plots for the left side of Clique 3:
7 OpenStax-CNX module: m The pdf plots for the "right" side of Clique 3:
8 OpenStax-CNX module: m Clique 3 could be classied as a boring clique because the analysis of nail spacings, and hook-and-lace spacings for the paintings in this clique was consistent with predicted spacings. Clustering via the k-means algorithm results in relatively clean groupings. The unknown cluster is an outlier because only one painting appears in this cluster. The pdf plots analyzing specic paintings matches up with the overall data and our expectations for this clique. We obtained the following results for Clique 6: The pdfs for the left side of Clique 6:
9 OpenStax-CNX module: m The pdfs for the right side of Clique 6:
10 OpenStax-CNX module: m Clique 6 could also be classied as a boring clique because Clustering via the kmeans algorithm results in relatively clean groupings. The unknown cluster is an outlier because only one painting appears in this cluster. The pdf plots analyzing specic paintings matches up with the overall data and our expectations for this clique. 7 Conclusions We contributed to Don Johnson's research project by applying signal processing in the form of the k-means algorithm to the canvases of origin for specically grouped paintings by Vincent Van Gogh. The methods we used are not limited to Van Gogh's paintings and could be applied to other artists' work done on canvas prepared in similar ways. 8 Poster This is our poster from the presentation.
11 OpenStax-CNX module: m Future Work and References Professor Don H. Johnson, Department of Electrical and Computer Engineering, Rice University, dhj@rice.edu Eva Dyer, Department of Electrical and Computer Engineering, Rice University, e.dyer@rice.edu Johnson, Don H., Ella Hendriks, and C. Richard Johnson. Art Matters: "Interpreting Canvas Weave Matches." International Journal for Technical Art History 5 (2013): Web. 8 Dec Hendriks-v2.pdf Krichevsky, Raphail E., Victor K. Tro mov. The Performance of Universal Encoding. IEEE Transactions on Information Theory 27, No. 2 (March, 1981): Matteucci, Matteo. "K-Means Clustering." A Tutorial on Clustering Algorithms. N.p., n.d. Web. 25 Nov "Van Gogh Fun Facts." The Van Gogh Gallery. Templeton Reid, LLC, 15 Jan Web. 17 Dec
THREAD COUNT REPORT. Chrysanthemums Pierre-Auguste Renoir (Mums / ) Presented by the Thread Count Automation Project
THREAD COUNT REPORT Chrysanthemums Pierre-Auguste Renoir 1881 1882 ( / 1933.1173) from the Art Institute of Chicago Presented by the Thread Count Automation Project C. Richard Johnson, Jr. (Cornell University,
More informationStacks of Wheat (End of Day, Autumn) Claude Monet (W1270 / )
THREAD COUNT REPORT Stacks of Wheat (End of Day, Autumn) Claude Monet 1890 1891 (W1270 / 1933.444) from the Art Institute of Chicago Presented by the Thread Count Automation Project C. Richard Johnson,
More informationThe Ancestors of Tehamana OR Tehamana Has Many Parents (Merahi metua no Tehamana) Paul Gauguin 1893 (W497 / )
THREAD COUNT REPORT The Ancestors of Tehamana OR Tehamana Has Many Parents (Merahi metua no Tehamana) Paul Gauguin 1893 (W497 / 1980.613) from the Art Institute of Chicago Presented by the Thread Count
More informationTHREAD COUNT REPORT. Paris Street; Rainy Day, 1877 Gustave Caillebotte 1877 (B57 / ) from the Art Institute of Chicago
THREAD COUNT REPORT Paris Street; Rainy Day, 1877 Gustave Caillebotte 1877 (B57 / 1964.336) from the Art Institute of Chicago Presented by the Thread Count Automation Project C. Richard Johnson, Jr. (Cornell
More informationChrist in the House of Mary and Martha Johannes Vermeer c L02; accession number NG 1670 National Gallery of Scotland
1 WEAVE MAP REPORT Christ in the House of Mary and Martha Johannes Vermeer c. 1654-1655 L02; accession number NG 1670 National Gallery of Scotland C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu
More informationWoman Holding a Balance Johannes Vermeer c L19; accession number National Gallery of Art, DC
1 WEAVE MAP REPORT Woman Holding a Balance Johannes Vermeer c. 1663-64 L19; accession number 1942.9.97 National Gallery of Art, DC C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William
More informationImage Processing - License Plate Localization and Letters Extraction *
OpenStax-CNX module: m33156 1 Image Processing - License Plate Localization and Letters Extraction * Cynthia Sung Chinwei Hu Kyle Li Lei Cao This work is produced by OpenStax-CNX and licensed under the
More informationA Young Woman Seated at a Virginal Johannes Vermeer c L36; JVe-100 The Leiden Collection
1 WEAVE MAP REPORT A Young Woman Seated at a Virginal Johannes Vermeer c. 1670-1672 L36; JVe-100 The Leiden Collection C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares
More informationC. Richard Johnson, Jr. Cornell University William A. Sethares University of Wisconsin - Madison
1 WEAVE MAP REPORT The Art of Painting Johannes Vermeer c. 1666-1668 L26; inv. 9128 Kunsthistorisches Museum C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares University
More informationWoman with a Lute Johannes Vermeer c L14; accession number Metropolitan Museum of Art
1 WEAVE MAP REPORT Woman with a Lute Johannes Vermeer c. 1662-1665 L14; accession number 25.110.24 Metropolitan Museum of Art C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William
More informationDo Weave Matches Imply Canvas Roll Matches? Don H. Johnson Ella Hendriks Muriel Geldof C. Richard Johnson, Jr. Abstract
Do Weave Matches Imply Canvas Roll Matches? Don H. Johnson Ella Hendriks Muriel Geldof C. Richard Johnson, Jr. Abstract Computational algorithms for measuring thread counts from scanned x-rays produce
More informationOfficer and Laughing Girl Johannes Vermeer c L06; accession number The Frick Collection
1 WEAVE MAP REPORT Officer and Laughing Girl Johannes Vermeer c. 1657-60 L06; accession number 1911.1.127 The Frick Collection C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William
More informationC. Richard Johnson, Jr. Cornell University William A. Sethares University of Wisconsin - Madison
1 WEAVE MAP REPORT Girl with a Pearl Earring Johannes Vermeer c. 1665-67 L22; inventory number 670 The Mauritshuis C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares
More informationThe Love Letter Johannes Vermeer c L30; object number SK-A-1595 Rijksmuseum
1 WEAVE MAP REPORT The Love Letter Johannes Vermeer c. 1668-70 L30; object number SK-A-1595 Rijksmuseum C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares University
More informationC. Richard Johnson, Jr. Cornell University William A. Sethares University of Wisconsin - Madison
1 WEAVE MAP REPORT View of Delft Johannes Vermeer c. 1660-63 L12; inventory number 92 The Mauritshuis C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares University of
More informationA Young Woman Standing at a Virginal Johannes Vermeer c L33; inv. number NG1383 National Gallery London
1 WEAVE MAP REPORT A Young Woman Standing at a Virginal Johannes Vermeer c. 1670-74 L33; inv. number NG1383 National Gallery London C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William
More informationThe Little Street Johannes Vermeer c L11; object number SK-A-2860 Rijksmuseum
1 WEAVE MAP REPORT The Little Street Johannes Vermeer c. 1657-61 L11; object number SK-A-2860 Rijksmuseum C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares University
More informationThe Procuress Johannes Vermeer 1656 L03 / Gal.-Nr Staatliche Kunstsammlungen Dresden
1 WEAVE MAP REPORT The Procuress Johannes Vermeer 1656 L03 / Gal.-Nr. 1335 Staatliche Kunstsammlungen Dresden C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares University
More informationYoung Lady Reading a Letter at an Open Window Johannes Vermeer c L05 / Gal.-Nr Staatliche Kunstsammlungen Dresden
1 WEAVE MAP REPORT Young Lady Reading a Letter at an Open Window Johannes Vermeer c. 1657-58 L05 / Gal.-Nr. 1336 Staatliche Kunstsammlungen Dresden C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu
More informationYoung Woman with a Wine Glass Johannes Vermeer c L10; inv. 316 Herzog Anton Ulrich Museum
1 WEAVE MAP REPORT Young Woman with a Wine Glass Johannes Vermeer c. 1659-1662 L10; inv. 316 Herzog Anton Ulrich Museum C. Richard Johnson, Jr. Cornell University johnson@ece.cornell.edu William A. Sethares
More informationCornell professor unlocks mysteries of paintings 19 November 2014, by Michael Hill
Cornell professor unlocks mysteries of paintings 19 November 2014, by Michael Hill A Rembrandt etching titled, Self Portrait Leaning on a Stone Sill, ca. 1639, etched by famed Dutch artist, Rembrandt Harmensz
More informationPitch Detection Algorithms
OpenStax-CNX module: m11714 1 Pitch Detection Algorithms Gareth Middleton This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 Abstract Two algorithms to
More informationShort Time Fourier Transform *
OpenStax-CNX module: m10570 1 Short Time Fourier Transform * Ivan Selesnick This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 1 Short Time Fourier Transform
More informationTransformation of graphs by greatest integer function
OpenStax-CNX module: m17290 1 Transformation of graphs by greatest integer function Sunil Kumar Singh This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0
More informationBasic Concepts * David Lane. 1 Probability of a Single Event
OpenStax-CNX module: m11169 1 Basic Concepts * David Lane This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 1 Probability of a Single Event If you roll
More informationMinor Keys and Scales *
OpenStax-CNX module: m10856 1 Minor Keys and Scales * Catherine Schmidt-Jones This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract The interval
More informationNumber Patterns - Grade 10 [CAPS] *
OpenStax-CNX module: m38376 1 Number Patterns - Grade 10 [CAPS] * Free High School Science Texts Project Based on Number Patterns by Rory Adams Free High School Science Texts Project Mark Horner Heather
More informationExploring QAM using LabView Simulation *
OpenStax-CNX module: m14499 1 Exploring QAM using LabView Simulation * Robert Kubichek This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 1 Exploring
More informationA THREAD COUNTING ALGORITHM FOR ART FORENSICS
A THREAD COUNTING ALGORITHM FOR ART FORENSICS Don H. Johnson C. Richard Johnson, Jr. Andrew G. Klein Elec. & Comp. Engineering Elec. & Comp. Engineering Elec. & Comp. Engineering Rice University Cornell
More informationFIR Filter Design by Frequency Sampling or Interpolation *
OpenStax-CX module: m689 FIR Filter Design by Frequency Sampling or Interpolation * C. Sidney Burrus This work is produced by OpenStax-CX and licensed under the Creative Commons Attribution License 2.
More informationQuadrature Amplitude Modulation (QAM) Experiments Using the National Instruments PXI-based Vector Signal Analyzer *
OpenStax-CNX module: m14500 1 Quadrature Amplitude Modulation (QAM) Experiments Using the National Instruments PXI-based Vector Signal Analyzer * Robert Kubichek This work is produced by OpenStax-CNX and
More informationForced Oscillations and Resonance *
OpenStax-CNX module: m42247 1 Forced Oscillations and Resonance * OpenStax This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract Observe resonance
More informationThin Lenses * OpenStax
OpenStax-CNX module: m58530 Thin Lenses * OpenStax This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 4.0 By the end of this section, you will be able to:
More informationOpenStax-CNX module: m Interval * Catherine Schmidt-Jones
OpenStax-CNX module: m10867 1 Interval * Catherine Schmidt-Jones This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract The distance between two
More informationDigital Filters in 16-QAM Communication. By: Eric Palmgren Fabio Ussher Samuel Whisler Joel Yin
Digital Filters in 16-QAM Communication By: Eric Palmgren Fabio Ussher Samuel Whisler Joel Yin Digital Filters in 16-QAM Communication By: Eric Palmgren Fabio Ussher Samuel Whisler Joel Yin Online:
More informationWhat is an FDM-TDM Transmultiplexer *
OpenStax-CNX module: m31548 1 What is an FDM-TDM Transmultiplexer * John Treichler This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 1 Frequency-Division
More informationTransverse Pulses - Grade 10 *
OpenStax-CNX module: m35714 1 Transverse Pulses - Grade 10 * Rory Adams Free High School Science Texts Project Heather Williams This work is produced by OpenStax-CNX and licensed under the Creative Commons
More informationWavelet Analysis of Crude Oil Futures. Collection Editor: Ian Akash Morrison
Wavelet Analysis of Crude Oil Futures Collection Editor: Ian Akash Morrison Wavelet Analysis of Crude Oil Futures Collection Editor: Ian Akash Morrison Authors: Ian Akash Morrison Aniruddha Sen Online:
More informationTree and Venn Diagrams
OpenStax-CNX module: m46944 1 Tree and Venn Diagrams OpenStax College This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 4.0 Sometimes, when the probability
More informationTDI2131 Digital Image Processing
TDI2131 Digital Image Processing Image Enhancement in Spatial Domain Lecture 3 John See Faculty of Information Technology Multimedia University Some portions of content adapted from Zhu Liu, AT&T Labs.
More informationOpenStax-CNX module: m Solar Cells * Andrew R. Barron. Based on Solar Cells by Bill Wilson
OpenStax-CNX module: m33803 1 Solar Cells * Andrew R. Barron Based on Solar Cells by Bill Wilson This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 note:
More informationENCODING COLOR IMAGES UNIT 3 LESSON 4
ENCODING COLOR IMAGES UNIT 3 LESSON 4 Use Use the Pixelation Tool to encode small color images with varying bits-per-pixel settings. OBJECTIVES Explain Use Explain the color encoding scheme for digital
More informationMPSK Tutorial. Tom Rondeau Tom Rondeau () MPSK Tutorial / 26
MPSK Tutorial Tom Rondeau 2012-09-22 Tom Rondeau () MPSK Tutorial 2012-09-22 1 / 26 Outline 1 Introduction 2 Digital Modulation Study 3 Tom Rondeau () MPSK Tutorial 2012-09-22 2 / 26 Download Materials
More information6. Multivariate EDA. ACE 492 SA - Spatial Analysis Fall 2003
1 Objectives 6. Multivariate EDA ACE 492 SA - Spatial Analysis Fall 2003 c 2003 by Luc Anselin, All Rights Reserved This lab covers some basic approaches to carry out EDA with a focus on discovering multivariate
More informationUsing Graphing Skills
Name Class Date Laboratory Skills 8 Using Graphing Skills Introduction Recorded data can be plotted on a graph. A graph is a pictorial representation of information recorded in a data table. It is used
More informationImage Enhancement in Spatial Domain
Image Enhancement in Spatial Domain 2 Image enhancement is a process, rather a preprocessing step, through which an original image is made suitable for a specific application. The application scenarios
More informationLinear Predictive Coding *
OpenStax-CNX module: m45345 1 Linear Predictive Coding * Kiefer Forseth This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 1 LPC Implementation Linear
More informationClassification of Road Images for Lane Detection
Classification of Road Images for Lane Detection Mingyu Kim minkyu89@stanford.edu Insun Jang insunj@stanford.edu Eunmo Yang eyang89@stanford.edu 1. Introduction In the research on autonomous car, it is
More information24.3 Production of Electromagnetic Waves *
OpenStax-CNX module: m52452 1 24.3 Production of Electromagnetic Waves * Bobby Bailey Based on Production of Electromagnetic Waves by OpenStax This work is produced by OpenStax-CNX and licensed under the
More informationMultiple Sound Sources Localization Using Energetic Analysis Method
VOL.3, NO.4, DECEMBER 1 Multiple Sound Sources Localization Using Energetic Analysis Method Hasan Khaddour, Jiří Schimmel Department of Telecommunications FEEC, Brno University of Technology Purkyňova
More informationDOWNLOAD OR READ : VINCENT VAN GOGH ART PROFILES FOR KIDS PDF EBOOK EPUB MOBI
DOWNLOAD OR READ : VINCENT VAN GOGH ART PROFILES FOR KIDS PDF EBOOK EPUB MOBI Page 1 Page 2 vincent van gogh art profiles for kids vincent van gogh art pdf vincent van gogh art profiles for kids Download
More informationBeginning Harmonic Analysis *
OpenStax-CNX module: m11643 1 Beginning Harmonic Analysis * Catherine Schmidt-Jones This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract An introduction
More informationA Study of Slanted-Edge MTF Stability and Repeatability
A Study of Slanted-Edge MTF Stability and Repeatability Jackson K.M. Roland Imatest LLC, 2995 Wilderness Place Suite 103, Boulder, CO, USA ABSTRACT The slanted-edge method of measuring the spatial frequency
More informationNoise Measurements Using a Teledyne LeCroy Oscilloscope
Noise Measurements Using a Teledyne LeCroy Oscilloscope TECHNICAL BRIEF January 9, 2013 Summary Random noise arises from every electronic component comprising your circuits. The analysis of random electrical
More informationNoise Reduction Technique in Synthetic Aperture Radar Datasets using Adaptive and Laplacian Filters
RESEARCH ARTICLE OPEN ACCESS Noise Reduction Technique in Synthetic Aperture Radar Datasets using Adaptive and Laplacian Filters Sakshi Kukreti*, Amit Joshi*, Sudhir Kumar Chaturvedi* *(Department of Aerospace
More informationElectromagnetism - Grade 11
OpenStax-CNX module: m32837 1 Electromagnetism - Grade 11 Rory Adams Free High School Science Texts Project Mark Horner Heather Williams This work is produced by OpenStax-CNX and licensed under the Creative
More informationUsing Figures - The Basics
Using Figures - The Basics by David Caprette, Rice University OVERVIEW To be useful, the results of a scientific investigation or technical project must be communicated to others in the form of an oral
More informationPASS Sample Size Software
Chapter 945 Introduction This section describes the options that are available for the appearance of a histogram. A set of all these options can be stored as a template file which can be retrieved later.
More informationGE 113 REMOTE SENSING. Topic 7. Image Enhancement
GE 113 REMOTE SENSING Topic 7. Image Enhancement Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information Technology Caraga State
More informationPhysics 131 Lab 1: ONE-DIMENSIONAL MOTION
1 Name Date Partner(s) Physics 131 Lab 1: ONE-DIMENSIONAL MOTION OBJECTIVES To familiarize yourself with motion detector hardware. To explore how simple motions are represented on a displacement-time graph.
More informationPentatonic Scales: Theory and Applications
OpenStax-CNX module: m33374 1 Pentatonic Scales: Theory and Applications Mathias Lang This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract Pentatonic
More informationDetecting weft snakes
Detecting weft snakes C. Richard Johnson, Jr., Don H. Johnson, Ige Verslype, René Lugtigheid and Robert G. Erdmann Abstract Using recently developed image processing software, the presence of a wavy band
More informationCOMPUTATIONAL ART HISTORY Research Activity Report
1 COMPUTATIONAL ART HISTORY 2012-13 Research Activity Report C. Richard Johnson, Jr. :: Cornell University :: January 7, 2014 Summary :: In 2005, Professor Johnson began shifting his scholarship to the
More informationThe Wave Aspect of Light: Interference *
OpenStax-CNX module: m42501 1 The Wave Aspect of Light: Interference * OpenStax This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract Discuss the
More informationRestaurant Bill and Party Size
Restaurant Bill and Party Size Alignments to Content Standards: S-ID.B.6.b Task The owner of a local restaurant selected a random sample of dinner tables at his restaurant. For each table, the owner recorded
More informationThis page intentionally left blank
Appendix E Labs This page intentionally left blank Dice Lab (Worksheet) Objectives: 1. Learn how to calculate basic probabilities of dice. 2. Understand how theoretical probabilities explain experimental
More informationProbability - Grade 10 *
OpenStax-CNX module: m32623 1 Probability - Grade 10 * Rory Adams Free High School Science Texts Project Sarah Blyth Heather Williams This work is produced by OpenStax-CNX and licensed under the Creative
More informationUsing Charts and Graphs to Display Data
Page 1 of 7 Using Charts and Graphs to Display Data Introduction A Chart is defined as a sheet of information in the form of a table, graph, or diagram. A Graph is defined as a diagram that represents
More informationTorque on a Current Loop: Motors. and Meters
OpenStax-CNX module: m61560 1 Torque on a Current Loop: Motors * and Meters OpenStax Physics with Courseware Based on Torque on a Current Loop: Motors and Meters by OpenStax This work is produced by OpenStax-CNX
More informationDigital Image Processing. Lecture # 4 Image Enhancement (Histogram)
Digital Image Processing Lecture # 4 Image Enhancement (Histogram) 1 Histogram of a Grayscale Image Let I be a 1-band (grayscale) image. I(r,c) is an 8-bit integer between 0 and 255. Histogram, h I, of
More informationLesson Plan on Rubik s Cube Mosaics: An Intermediate guide for use in the classroom
Lesson Plan on Rubik s Cube Mosaics: An Intermediate guide for use in the classroom By Suzanne Kubik Middleborough High School Middleborough MA Grades 9-12 Algebra 2, Geometry, and Statistics Learning
More informationTutorial document written by Vincent Pelletier and Maria Kilfoil 2007.
Tutorial document written by Vincent Pelletier and Maria Kilfoil 2007. Overview This code finds and tracks round features (usually microscopic beads as viewed in microscopy) and outputs the results in
More informationTutorial on the Statistical Basis of ACE-PT Inc. s Proficiency Testing Schemes
Tutorial on the Statistical Basis of ACE-PT Inc. s Proficiency Testing Schemes Note: For the benefit of those who are not familiar with details of ISO 13528:2015 and with the underlying statistical principles
More informationUnit 12 - Electric Circuits. By: Albert Hall
Unit 12 - Electric Circuits By: Albert Hall Unit 12 - Electric Circuits By: Albert Hall Online: < http://cnx.org/content/col12001/1.1/ > OpenStax-CNX This selection and arrangement of content as a collection
More informationEngineering Fundamentals and Problem Solving, 6e
Engineering Fundamentals and Problem Solving, 6e Chapter 5 Representation of Technical Information Chapter Objectives 1. Recognize the importance of collecting, recording, plotting, and interpreting technical
More informationGraphing Techniques. Figure 1. c 2011 Advanced Instructional Systems, Inc. and the University of North Carolina 1
Graphing Techniques The construction of graphs is a very important technique in experimental physics. Graphs provide a compact and efficient way of displaying the functional relationship between two experimental
More informationFrequency Distribution and Graphs
Chapter 2 Frequency Distribution and Graphs 2.1 Organizing Qualitative Data Denition 2.1.1 A categorical frequency distribution lists the number of occurrences for each category of data. Example 2.1.1
More informationCS231A Final Project: Who Drew It? Style Analysis on DeviantART
CS231A Final Project: Who Drew It? Style Analysis on DeviantART Mindy Huang (mindyh) Ben-han Sung (bsung93) Abstract Our project studied popular portrait artists on Deviant Art and attempted to identify
More informationResting pulse After exercise Resting pulse After exercise. Trial Trial Trial Trial. Subject Subject
EXERCISE 2.3 Data Presentation Objectives After completing this exercise, you should be able to 1. Explain the difference between discrete and continuous variables and give examples. 2. Use one given data
More informationSTARCRAFT 2 is a highly dynamic and non-linear game.
JOURNAL OF COMPUTER SCIENCE AND AWESOMENESS 1 Early Prediction of Outcome of a Starcraft 2 Game Replay David Leblanc, Sushil Louis, Outline Paper Some interesting things to say here. Abstract The goal
More informationFractal expressionism
1997 2009, Millennium Mathematics Project, University of Cambridge. Permission is granted to print and copy this page on paper for non commercial use. For other uses, including electronic redistribution,
More informationZhan Chen and Israel Koren. University of Massachusetts, Amherst, MA 01003, USA. Abstract
Layer Assignment for Yield Enhancement Zhan Chen and Israel Koren Department of Electrical and Computer Engineering University of Massachusetts, Amherst, MA 0003, USA Abstract In this paper, two algorithms
More informationImagesPlus Basic Interface Operation
ImagesPlus Basic Interface Operation The basic interface operation menu options are located on the File, View, Open Images, Open Operators, and Help main menus. File Menu New The New command creates a
More informationNumerical: Data with quantity Discrete: whole number answers Example: How many siblings do you have?
Types of data Numerical: Data with quantity Discrete: whole number answers Example: How many siblings do you have? Continuous: Answers can fall anywhere in between two whole numbers. Usually any type of
More informationESOL and Visual Arts
Seeing Main Idea and Details in Art ESOL and Visual Arts WIDA ELD and Common Core Standards: ELD 2: English language learners communicate information, ideas, and concepts necessary for academic success
More informationLandscape Painting By Birge Harrison READ ONLINE
Landscape Painting By Birge Harrison READ ONLINE WELCOME! I M MARLA I am a painter, art instructor, guide, coach and creative cheerleaader.? Pastel is a wonderous medium that offers up the best of drawing
More informationArtist s Prospectus. Application Active Nov.15, 2018 Application Deadline Feb.15, 2019
Artist s Prospectus Application Active Nov.15, 2018 Application Deadline Feb.15, 2019 The Parrsboro International Plein Air Festival (PIPAF) will take place in Parrsboro Nova Scotia from Thursday, June
More informationInformation representation
2Unit Chapter 11 1 Information representation Revision objectives By the end of the chapter you should be able to: show understanding of the basis of different number systems; use the binary, denary and
More informationChapter 10. Definition: Categorical Variables. Graphs, Good and Bad. Distribution
Chapter 10 Graphs, Good and Bad Chapter 10 3 Distribution Definition: Tells what values a variable takes and how often it takes these values Can be a table, graph, or function Categorical Variables Places
More informationExperiment 01 - RF Power detection
ECE 451 Automated Microwave Measurements Laboratory Experiment 01 - RF Power detection 1 Introduction This (and the next few) laboratory experiment explores the beginnings of microwave measurements, those
More informationSession Three: Pulsar Data and Dispersion Measure
Slide 1 Session Three: Pulsar Data and Dispersion Measure Sue Ann Heatherly and Sarah Scoles Slide 2 Plot Review Average pulse profile Time domain Reduced χ 2 Recall that last week, we learned about three
More informationScience Binder and Science Notebook. Discussions
Lane Tech H. Physics (Joseph/Machaj 2016-2017) A. Science Binder Science Binder and Science Notebook Name: Period: Unit 1: Scientific Methods - Reference Materials The binder is the storage device for
More informationVincent Van Gogh Sunflowers And Swirly Stars Smart About Art
Vincent Van Gogh Sunflowers And Swirly Stars Smart About Art We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer,
More informationII. Basic Concepts in Display Systems
Special Topics in Display Technology 1 st semester, 2016 II. Basic Concepts in Display Systems * Reference book: [Display Interfaces] (R. L. Myers, Wiley) 1. Display any system through which ( people through
More informationUNIVERSITY OF UTAH ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT
UNIVERSITY OF UTAH ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT ECE1020 COMPUTING ASSIGNMENT 3 N. E. COTTER MATLAB ARRAYS: RECEIVED SIGNALS PLUS NOISE READING Matlab Student Version: learning Matlab
More information4D-Particle filter localization for a simulated UAV
4D-Particle filter localization for a simulated UAV Anna Chiara Bellini annachiara.bellini@gmail.com Abstract. Particle filters are a mathematical method that can be used to build a belief about the location
More informationDSP First Lab 06: Digital Images: A/D and D/A
DSP First Lab 06: Digital Images: A/D and D/A Pre-Lab and Warm-Up: You should read at least the Pre-Lab and Warm-up sections of this lab assignment and go over all exercises in the Pre-Lab section before
More informationGE U111 HTT&TL, Lab 1: The Speed of Sound in Air, Acoustic Distance Measurement & Basic Concepts in MATLAB
GE U111 HTT&TL, Lab 1: The Speed of Sound in Air, Acoustic Distance Measurement & Basic Concepts in MATLAB Contents 1 Preview: Programming & Experiments Goals 2 2 Homework Assignment 3 3 Measuring The
More informationAn Introduction to the FDM-TDM Digital Transmultiplexer: Appendix C *
OpenStax-CNX module: m32675 1 An Introduction to the FDM-TDM Digital Transmultiplexer: Appendix C * John Treichler This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution
More informationLesson Title Art Form Grade Level. Media. Grade Level Theme Key Concept Link. Perception of Self Identity/Social Roles Watercolor Portraits
Lesson Title Art Form Grade Level Snapchat Self-Portrait Drawing, Painting, Multi- Media 2D Art Studio 2 (10th-12th) Grade Level Theme Key Concept Link Perception of Self Identity/Social Roles Watercolor
More information