ECE 4210

Art Support Analysis Algorithms

Course Description

Course information provided by the Courses of Study 2015-2016.

The analysis of fine art by art historians and conservators involves the close examination of various images of the works of art. The image data available for this course include digitized x-radiographs of the paintings of Vincent van Gogh and Johannes Vermeer and beta-radiographs of prints by Rembrandt van Rijn. This course will provide a basic description of the traditional manual examination tasks and of the building blocks of image processing that can be used to automate them. Students will assess and improve "starter" schemes performing canvas thread counting and thread angle measurement and laid paper chain line pattern matching. The interactive capabilities of Mathematica will be exploited in providing the computer code for the "starter" schemes and the presentation of image processing concepts.

When Offered Fall.

Permission Note Enrollment limited to: undergraduate students only.
Prerequisites/Corequisites Prerequisite: ECE 3250.

• Understand extension of one-dimensional linear signal processing (e.g. convolution, correlation, sampling, interpolation, frequency-selective filtering, spectral analysis) to two-dimensional signal processing.
• Understand the effect on images of basic morphological image processing algorithms (such as dilation, erosion, closing, and opening), projection transforms (such as the Radon transform), and enhancement schemes (such as histogram equalization).
• Apply a combination of two-dimensional signal processing to actual digitized x-rays of fine art paintings in order to determine local thread density and angle of the canvas support.
• Apply a combination of two-dimensional signal processing to actual digitized beta-radiographs of laid paper in order to extract chain line patterns and test their connective similarity.

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