16-311: Introduction to Robotics Lab 2 – Robot Vision January 21, 2015 Sarah Tan Introduction to Robotics – Lab 2, p.1

Introduction to Robotics – Lab 2, p.2 Thresholding Making a binary, black-and-white image. Given an image and a threshold value, Set all pixels of value ≤ threshold to 0 Set all pixels of value > threshold to 255 (or just 1)

Introduction to Robotics – Lab 2, p.3 Thresholding threshold(image, 75); Every pixel with value higher than 75: set to 255 Every pixel with value lower than 75: set to 0

Introduction to Robotics – Lab 2, p.4 Thresholding Good for removing noise and artifacts. Of course, overdoing it would lose you precious details

Segmentation Segmentation: A program that segments a binary image into connected components Algorithm – Double Raster Scan in Notes

Introduction to Robotics – Lab 2, p.6 What you’re doing Building a vision pipeline to estimate the distance to two targets Targets have tennis ball in known square pattern Steps: - Thresholding - Segmentation - Filtering / Data Association - Determining Distance by Scale and Perspective

Introduction to Robotics – Lab 2, p.7 Example 2ft away

Introduction to Robotics – Lab 2, p.8 Grading Part 1 (10 points) Matlab and Image Basics (each question should be done in 2-3 lines of Matlab Code) Take images of each of the target 2,4,8,16 ft away Part 2 (90 points) Implement 3 functions Thresholding, Segmentation and Filtering, Determining Distance to Target Use 3 functions on 8 Images from Part 1 to get segmented images and estimation on Distance to Target Clear and Well-documented code may improve your grade

Introduction to Robotics – Lab 2, p.9 Important stuff Due date Tuesday, January 27th Handin Handin a zip file of your submission on blackboard. Details can be found on the course website and lab pdf Start this lab EARLY!

Introduction to Robotics – Lab 2, p.10 Need help? Check out our piazza page! TA Office Hours In the REL See website for the schedule TA dlist [Sarah Tan ]