1. Program Studi S-1 Teknik Informatika FMIPA Universitas Padjadjaran
VISI KOMPUTER D10K-7C02 Semester Ganjil CV99: Vision Projects Dr. Setiawan Hadi, M.Sc.CS.
Program Studi S-1 Teknik Informatika
FMIPA Universitas Padjadjaran

2. 20 Vision Problems 10.1 Baby Food 10.2 Labels on Glue 10.3 O-rings
10.4 Staying in Lane
10.5 Reading Notices
10.6 Mailboxes
10.7 Abandoned and Removed Object Detection
10.8 Surveillance
10.9 Traffic Lights
10.10 Real Time Face Tracking
10.11 Playing Pool
10.12 Open Windows
10.13 Modelling Doors
10.14 Determining the Time from Analogue Clocks
10.15 Which Page
10.16 Nut/Bolt/Washer Classification
10.17 Road Sign Recognition
10.18 License Plates
10.19 Counting Bicycles
10.20 Recognise Paintings

3. 10.1 Baby Food Description: Task:
On a production line cans of baby food are made by:
Fabricating the sides and lid of the can together but omitting the base of the can.
Dropping a spoon into the empty upside-down can.
Pouring the baby food (powder) into upside-down the can.
Sealing the base onto the can.
Task:
You are asked to develop an inspection system looking at the can between steps 2 and 3 in order to check that a single spoon has been placed into it (see Figure 10.1).

4. 10.1 Baby Food
Figure 10.1 Sample baby food can images from the production just after the spoon has been dropped into the can. Examples of no spoon, one spoon and two spoons are shown

5. 10.2 Labels on Glue Description/task:
On a production line for bottles of glue it is necessary to perform a number of inspection tests (see Figure 10.2):
Check that each bottle has a label.
Check that the label on the bottle is straight.
Check that the label is not torn or folded.

6. 10.2 Labels on Glue
Figure 10.2 Sample images of bottles of glue are shown some without labels and some with crooked (or damaged labels). Note that these are not images from a real production line and in many cases the labels have been manually damaged/removed

7. 10.3 O-rings Descriptio/tasks:
You are asked to detect defects (notches and breaks) in rubber O-rings as they pass before an inspection camera (see Figure 10.3).

8. 10.3 O-rings
Figure 10.3 Sample images of good O-rings (i.e. with no defects; see top row), broken O-rings (middle row) and O-rings with notches out of them (bottom row)

9. 10.4 Staying in Lane Description/task: Datasets:
In order to assist drivers, you are asked to develop a system to ensure that they stay in lane, and raise an alarm if they start to drift into another lane (without indicating). Given a video feed from a camera mounted on the dashboard of a car, looking forwards, you are asking to automatically detect the lines on each side of the lane of traffic that the car is in (see Figure 10.4).
Datasets:
This is an area of significant research interest and there are other datasets available online:

10. 10.4 Staying in Lane
Figure 10.4 Sample images from the dashboard mounted camera

11. 10.5 Reading Notices Description:
In order to facilitate auto-translation, you are asked to locate any text within an image and extract the text as a series of characters (see Figure 10.5). You may assume that the text will take up a reasonable portion of the image and will be more-or-less the right way up!

12. 10.5 Reading Notices
Figure 10.5 Sample images of notices

13. 10.6 Mailboxes

14. 10.7 Abandoned and Removed Object Detection

15. 10.8 Surveillance

16. 10.9 Traffic Lights

17. 10.10 Real Time Face Tracking

18. 10.11 Playing Pool

19. 10.12 Open Windows

20. 10.13 Modelling Doors

21. 10.14 Determining the Time from Analogue Clocks

22. 10.15 Which Page

23. 10.16 Nut/Bolt/Washer Classification

24. 10.17 Road Sign Recognition

25. 0.18 License Plates

26. 10.19 Counting Bicycles

27. 10.20 Recognise Paintings

28. Hasil Undian 10.1 Baby Food (Rahmat) 10.2 Labels on Glue (Fadly)
10.6 Mailboxes (Fathur)
10.9 Traffic Lights (Sofyan)
10.12 Open Windows (Yusuf)
10.16 Nut/Bolt/Washer Classification (Iqbal)
10.17 Road Sign Recognition (Febrian)
10.3 O-rings
10.14 Determining the Time from Analogue Clocks