Homework answers / question archive / Color Segmentation using GMM Table of Contents: Introduction What you need to do Problem Statement Video Lecture Submission Guidelines Collaboration Policy Introduction Have you ever played with these adorable Nao robots? Click on the image to watch a cool demo

Color Segmentation using GMM Table of Contents: Introduction What you need to do Problem Statement Video Lecture Submission Guidelines Collaboration Policy Introduction Have you ever played with these adorable Nao robots? Click on the image to watch a cool demo

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Color Segmentation using GMM

Table of Contents:

Introduction

What you need to do

Problem Statement

Video Lecture

Submission Guidelines

Collaboration Policy

Introduction

Have you ever played with these adorable Nao robots? Click on the image to watch a cool

demo.

CMSC426 Computer Vision

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Nao robots are star players in RoboCup, an annual autonomous robot soccer competitions. We

are planning to build the Maryland RoboCup team to compete in RoboCup 2020, we need your

help. Would you like to help us in Nao’s soccer training? We need to train Nao to detect a

soccer ball and estimate the depth of the ball to know how far to kick.

Nao’s training has two phases:

Color Segmentation using Gaussian Mixture Model (GMM)

Ball Distance Estimation

What you need to do

To make logistics easier, we have collected camera data from Nao robot on behalf of you and

saved the data in the form of color images. Click here to download. The image names represent

the depth of the ball from Nao robot in centimeters. -We will release the test dataset 48 hours

before the deadline i.e. 11:59:59PM, Sunday, September 20. Test images are available here to

download**.

Problem Statement

1. Write MATLAB code to cluster the orange ball using Single Gaussian [30 points]

2. Write MATLAB code to cluster the orange ball using Gaussian Mixture Model [40 points]

and estimate the distance to the ball [20 points]. Also, plot all the GMM ellipsoids [10

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points].

You are NOT allowed to use any built-in MATLAB function(s) like fitgmdist() or

gmdistribution.fit() for GMM. To help you with code implementation, we have given the

pseudocode :-)

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Video Lecture

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Submission Guidelines

If your submission does not comply with the following guidelines, you’ll be given ZERO

credit

File tree and naming

Your submission on Canvas must be a zip file, following the naming convention

YourDirectoryID_proj1.zip. For example, xyz123_proj1.zip. The file must have the following

directory structure.

YourDirectoryID_proj1.zip.

train_images/.

test_images/.

results/.

gaussian.m (For Single Gaussian)

GMM.m

trainGMM.m

testGMM.m

measureDepth.m

plotGMM.m

report.pdf

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Report

For each section of the project, explain briefly what you did, and describe any interesting

problems you encountered and/or solutions you implemented. You must include the following

details in your writeup:

Your choice of color space, initialization method and number of gaussians in the GMM

Explain why GMM is better than single gaussian

Present your distance estimate and cluster segmentation results for each test image

Explain strengths and limitations of your algorithm. Also, explain why the algorithm failed

on some test images

As usual, your report must be full English sentences, not commented code. There is a word

limit of 1500 words and no minimum length requirement