This course addresses data acquisition and processing in the context of digital media arts. Understanding sensors and their limitations is an important prerequisite to building robust and satisfying information processing artifacts. This course will allow students to better understand both the concepts as well as the techniques underlying a variety of sensor typologies and various data acquisition approaches.

While the course covers technical materials, the goal of the course is to uncover new possibilities with which students can investigate digital data and imagery. As opposed to courses that manipulate image data through commerdial applications such as Photoshop, this course works with general purpose programing and mathematical tools that offer opportunities and freedoms prepackaged software solutions deny. Course materials include readings in perception theory, sensor design, fundamentals of machine vision as well as documentation of select art works that engage in various fashions in advanced sensing methods.

Our lab is Ubuntu based and has a wide array of sensor types, an industry grade commercial machine vision library as well as an open source research grade vision library. We also have microprocessor based ccd cameras, ieee1394 compliant digital cameras, analogue video cameras with fast frame grabber cards and use Python under Wingware (both opensource) for this course. With this infrastructure and instructor guidance, students will be able to explore new ways of seeing, hearing and feeling and become familiar with software culture.

Prerequisites: MediaRobotics I or equivalent


Here are video documents of student work from previous MRII courses
2006
Brian Clark: underpants [.mpg], Bogdan Marion: flag [.mpg], Jesse Fabian: portaroad [.mpg]
2008
Steve Hibit: somewhere in France [.mp4], Chris Caprolingua: face [.wmv], Steve Korzelius: jack 1.0 [.wmv]


Here are the lecture notes, source code and tutorials for MRII