The goal of this project is to give you hands-on experience of using one of the digital tools introduced in class for image analysis. Choose one of the following two options.

OPTION 1: Quantitative Analysis of Large Image Collections with ImageJ

Choose two or more collections of images and use ImageJ to identify stylistic differences between these collections. Use ImageMeasure to generate a set of statistics on each of the collections and use ImagePlot to map out these statistics to identify stylistic patterns in each collection. Use other tools in the ImageJ toolkit when necessary. In your presentation, you must develop a coherent and persuasive argument on how stylistic patterns of these collections are different and how you account for these differences. For example, you may explore how styles of painting vary between different painters, different schools of painters, and different stages of a painter’s career, among others.

You may need to explore several collections of paintings before you can decide on two or three that allow for meaningful comparisons.


  • If you have difficulty using ImagePlot on your computer, you may use other programs (such as Microsoft Excel for PC users, Numbers for Mac users, and Google Sheets) to map out the statistics calculated by ImageMeasure. The file ImageMeasure generates is a .csv file (which is a simple file format used to store tabular data). You may open it and plot out the statistics in Microsoft Excel, Numbers, or Google Sheets following the instructions in the tutorial: “How to Open a .CSV File and Insert a Scatter Chart in Google Sheets and Microsoft Excel.” Check the Resources > Software Tutorials page on the course website for this file.
  • You may find large collections of oil paintings in the course project folder on Bucknell Netspace. Follow the instructions below to access Netspace; then navigate to projects > FOUN098-55 > Oil Paintings in the West.
  • For what each statistical measure in ImageMeasure means, see ImagePlot documentation. ImagePlot documentation also provides detailed tutorials on ImageMeasure, ImagePlot, and many other macros in the ImageJ suite.
  • For some exemplary studies conducted with ImageJ, consult Lev Manovich’s “How to Compare One Million Images?” and Projects from Software Studies Initiative.

OPTION 2: Interpretative Annotations of an Image with Neatline

Choose an image (e.g., a painting, a historical photo, a piece of propaganda art or pop art, or a map) and create a public-facing project that shares your interpretation of the image by annotating five to ten aspects of it on the Neatline platform. Your annotations should be thoughtful, coherent, and visually rich.

  • Thoughtful: A thoughtful annotation provides cogent comments on a thematic or stylistic feature of the image and/or adds new layers of meanings to the image by situating it in an appropriate context.
  • Coherent: All annotations should be organized around a coherent theme and provide a coherent interpretation of the image (much like how different paragraphs in an essay contribute to the development of a coherent argument).
  • Visually rich: To take full advantage of what the Neatline platform offers, enrich your annotation with hyperlinks and visual materials pertinent to your discussion.


Like when you are writing a short essay, consider the following questions carefully before you start on the project:

  1. What is your purpose of annotating this image? What is your interpretation of the image that you want to share with others?
  2. What evidence (text, image, video, etc.) do you want to use to communicate your interpretation to your audience and persuade them?


  • For an exemplary image annotation project created by Bucknell students, click here: WWII propaganda on Adolf Hitler.
  • “Neatline Tutorial_How to Build your First Neatline Exhibit” and “Neatline Tutorial_How to Annotate an Image” provide tutorials on Neatline. Check the Resources > Software Tutorials page on the course website for these files.


Whichever option you choose, share your visualization with the class in a 5-minute presentation on Wednesday and Friday. All projects are due before class on Wednesday, November 9.


  • If you choose option 1, you must create a PowerPoint slides file for the presentation. Submit both your slides and all the ImageMeasure-generated statistics to the appropriate online drop box. ImageMeasure will generate multiple statistics files for your project (one for each collection of images). To submit them, place all files in a folder and right click the folder to zip/compress them first.
  • If you choose option 2, there is nothing you need to do. I have access to your projects in situ.