18SPR ART 185, Optical Digital Culture - Intelligent Machine Vision


2018spr Instructor Supervisor		ART185 Optical Digital Culture - Intelligent Machine Vision (4 units) Jieliang (Rodger) Luo \| jieliang@ucsb.edu George Legrady Lecture/lab: Tues-Thurs 10:00am-11:50pm Elings Studio 2611 Office Hours: by appointment Lab Sessions: Tues/Thurs noon-3:00pm, Weds 1pm-4pm

Course Description		An arts-computation studio course focused on programming the behavior of mobile cameras that capture and project images connected to computers. Students will work with an autonomous robotic-camera system assembled in last year’s ART 185GL course. The system consists of 9 mobile-camera robots that autonomously move around in a restricted space continuously capturing and evaluating images, comparing what each robotic camera sees with the others’, resulting in collaborative effort to get greater complex views of a scene-of-interest. Keywords Mobile multi-camera system \| Computational collaborations \| Deep learning in computer vision Arts-engineering interdisciplinary collaboration \| Arts experimentation Critical questions How should the cameras move? Where should the cameras look, at what? How to present the visual information the cameras collect? Technical skills that will be acquired Programming on Arduino & Raspberry Pi and communications between the two systems Programming in Python, focusing on OpenCV Implementing deep learning models on the robots Basic understanding of reinforcement learning

Resources		Course GitHub \| Last Year's Course \| Student Forum

[wk 1] 04.03 04.05		Course Introduction Introduction to OpenCV \| A Brief History of Computer Vision Anaconda \| OpenCV \| OpenCV Demos \| In-class exercise Assignment One: Art Research Project Presentation Google Doc \| Demo 1 - Telestron \| Demo 2 - Box (Box - Behind the Scenes)

[wk 2] 04.10 04.12		Getting familiar with the robots I Arduino Section Robotic Chassis \| Arduino 1.6.0 \| Robotic Chassis Library Blinking LED Demo \| Motor Test Demo \| Autonomous Walking Demo \| Zumo 32U4 Robot User's Guide Raspberry Pi Section Instruction to Access Raspberry Pi \| Servo Demo Getting familiar with the robots II In-class exercise \| Homework

[wk 3] 04.17 04.19		Connecting Raspberry Pi and Arduino Step by Step Instruction of Face-to-Sound Demo Presentation of the art research project

[wk 4] 04.24 04.26		Final Project Proposal Groups \| Requirement Machine Learning Basics I Deep Learning Introduction \| Setup environments \| Image classifier Machine Learning Basics II Imitation learning \| Reinforcement learning

[wk 5] 05.01 05.03		Out of town - Singapore Working on the proposal Out of town - Singapore Working on the proposal

[wk 6] 05.08 05.10		Final Project Proposal Presentation Individual Group Meeting Discuss the doability of the proposal \| Finalize the proposal Guest Speaker: Sam Green CNN Visualization

[wk 7] 05.15 05.17		Work on the final project Work on the final project

[wk 8] 05.22 05.24		Work on the final project Work on the final project

[wk 9] 05.29 05.31 06.01		Work on the final project Get ready for The End of Year Show Work on the final project Get ready for The End of Year Show Setup for The End of Year Show Schedule \| Floor Plan

[wk 10] 06.05		Final Documentation Presentation Guest Speaker: Fabian Offert

Grading		10% Attendance & Participation 10% In-class exercises 20% Art Research Project Presentation 20% Proposal Presentation 40% Final Project The course is designed to accommodate both beginning and advanced students. All students will be expected to perform at the level of their expertise but programming experience is desirable.