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COMPUTER VISION
LAB
LAB 1
Gemma Rotger – Felipe Lumbreras
Spring 2017
LAB 1 – PROJECT GOALS
• Learn a statistical simple approach to background segmentation.
• Learn how to work with images sequences in Matlab and Octave.
• Use Matlab and/or Octave to perform a Traffic Application.
Deliverable deadline: Th. Mar. 9th. 23:00h
LAB 1 – SPEED LIMITS IN YOUR
COUNTRY
GOAL: COMPUTE THE CARS’S VELOCITY AND CHECK IF IT IS UNDER THE LIMITS OF LEGAL
LAB 1 – PROJECT MATERIALS
• Code• lab1.m • compute_statistics.m• simple_subtraction.m• gaussian_subtraction.m• morphology.m• report.docx
• Source Images • Highway sequence
http://changedetection.net/Datasets > 2014 Dataset > Baseline > Highway
Download it before the lab!
STEP 1
DETECT CARS
LAB 1
CODE PART
CAR 1
CAR 2
Co
mp
lete
Dat
a
LAB 1 – PROBLEM 1
Load the dataset and split it in two sets train and test (+1).
TRAIN
TEST
FRAMES
670 to 950
FRAMES
670 to 810
FRAMES
811 to 950
LAB 1 – PROBLEM 2
Compute the mean and the standard deviation of every
image pixel along the train sequence. (+1).
I want the solution to work in a matrix form. I don’t want to see any loop in the code of
this exercise! If you do that, your mark will be a 0.
LAB 1 – PROBLEM 2
Compute the mean and the standard deviation of every
image pixel along the train sequence. (+1).
MEAN
BACKGROUND
STD
VARIATION
LAB 1 – PROBLEM 3
For every single image in the test sequence, segment the
foreground by subtracting the background (+1.5)
Take into account that images
can be in different ranges:0 – 255 / 0 – 1
LAB 1 – PROBLEM 3
For every single image in the test sequence, segment the
foreground by subtracting the background (+1.5)
If you obtain a reversed segmentation
remember how to transform to the negative image from lab 0
LAB 1 – PROBLEM 3
For every single image in the test sequence, segment the
foreground by subtracting the background (+1.5)
There is a lot of noise. Why?
LAB 1 – PROBLEM 4
For every single image in the test sequence, segment the
foreground by using a Gaussian model (+2)
You can use a Gaussian mode as this one or use you own but you
have to argument it if is required on the class.
In this case alpha and beta are constants that you need to define.
Please play with this values and fill a table that you can find in the code.
LAB 1 – PROBLEM 4
For every single image in the test sequence, segment the
foreground by using a Gaussian model (+2)
We can model the Gaussian noise and then remove it!
Simple Subtraction (Gaussian) Noise Gaussian Subtraction
LAB 1 – PROBLEM 5
Apply some morphology techniques to improve the results. (+1)
Gaussian Subtraction + Morphology
LAB 1 – PROBLEM 6
Show your results in a video. (+0.5)
LAB 1 – OPTIONAL TASK
Freestyle. Try top improve your results with any
technique you like (+1)
© Zalman Lent
STEP 2
COMPUTE VELOCITY
LAB 1
MINI REPORT
LAB 1 – REPORT + CLASS
DISCUSSION
Make your own research and write down the steps you would follow in order to compute the velocity of cars and check if their velocity is in the limits of legal. It includes report + class discussion (no code) (+3)
CAR 1
EVALUATION
1. Code (+8)
2. Report (+3)
The evaluation will depend on the code, the report and the discussion.
Deadline: Th. Mar. 9th. 23:00h.
DELIVERABLES CERBERO
What you have to deliver:
1. Code:Lab1.m
2. Report1. Report.pdf
Zip it all together and name it GROUP##Lab1.zip
(remember that zip is zip not rar nor tar)
IMPORTANT! 1 deliverable per group
DELIVERABLES CERBERO
What NOT to deliver
1. The image sources
2. The evaluation code provided
3. The generated video
Deadline: Th. Mar. 9th. 23:00h. (Cerbero)