This document summarizes a student presentation on automatic object exploration based on probability. It introduces the objectives of extracting features from partial objects, making an object feature database, computing probability from object features, and controlling a camera based on probability. It then outlines the experimental setup, database objects, feature extraction methods, probability calculation process, and system flow diagram. Finally, it discusses the scope, limitations, results, conclusions, and future recommendations of the project.

1. WELCOME
To
Presentation

2. Supervised By:
Dr. Md. Rokunuzzaman
Associated Professor
Department of Mechanical
Engineering, RUET
Submitted by:
Md Kamrul Hasan
Chowdhury
Roll:092069
Basudeb Biswas
Roll:092089
Heaven's Light is Our Guide
RAJSHAHI UNIVERSITY OF ENGINEERING &TECHNOLOGY
Department of Mechanical Engineering
THESIS & PROJECT REPORT
on
Automatic Object Exploration based on Probability

3. Objectives:
1. To extract features from partial object
2. To make a database of features of the object
3. To compute probability from features of the object
4. To control the movement of a camera based on
probability

4. Laptop
Stepper Motor
Webcam
Pinion
Rack
Electric-Circuit
Transformer
USB Cable Pendrive Base
Experimental Setup

5. Webcam
Laptop
Probability Calculation
Control Program
Stepper Motor

6. Database Object:
1. Computer ( Laptop )
2. Pen
3. Pendrive
4. Mobile

7. Full Image (Mobile )
Partial Object ( Part of an image)
A Schematic Diagram of a Partial Object Identification
Partial Object:
It is a part of a object.

8. Features:
1. Area: A= summation of area of pixel.
Width
Height
Bounding Box (aBB)
Object
(Area ai )
Pixel

9. 2. Perimeter: Length of boundary pixel.
Mathematically P= 2 (Height+ Width)
3. Density:
Density is defined as the ratio of the area and the area bounding box.
Mathematically it can be defined as
D= A/aBB
Where ,
A= Area of Pixel
aBB = The area of bounding box.
4. Bounding box ratio /Aspect ratio:
Bounding box ratio is defined as the ratio of the length of the Blob to the width.
Mathematically , B=Height/Width

10. Blob:
Blob defined as Binary Large Object collection of binary data
stored as a single entity.
Blobs
Blob Filter
Image (Pendrive)
A Schematic Diagram of Blob Identification

11. Database’s value of Computer’s Blobs
Area Perimeter Bounding Box Area Bounding Box
Ratio
Density
6020 1039.830519 10212 0.536232 0.589503
4050 429.487373 9455 0.393548 0.42835
48 85.213203 420 0.32857 0.114286
812 553.068109 13082 0.293839 0.062070
318 250.693434 1539 0.473684 0.206628
599 113.154329 1012 0.478261 0.591897

12. Similarly way also calculated database for Pendrive, Mobile and Pen.
Probability Calculation
Probability of Area:
Total area of database object A1 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑎i
Total area of input Object A2 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑎i
If (A1 < A2) THEN A=( A1 / A2)
ELSE A=( A2 / A1)
Probability of Perimeter:
Total Perimeter of Database object P1 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑝I
Total Perimeter of input object P2 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑝i
If (P1 < P2) THEN P=( P1 / P2)
ELSE P=( P2 / P1)

13. Probability of Bounding Box:
Total Perimeter of database object B1 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
bi
Total perimeter of input object B2 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
bi
If (B1 < B2) THEN B=( B1 / B2)
ELSE B=( B2 / B1)
Probability of Bounding Box Ratio:
Total Perimeter of database object BR1 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
bri
Total perimeter of input object BR2 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑏 ri
If (BR1 < BR2) THEN BR=( BR1 / BR2)
ELSE BR=( BR2 / BR1)

14. Probability of Density:
Total Perimeter of Database object D1 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑑 i
Total Perimeter of input Object D2 = 𝑖=1
𝑖=𝑛𝑜.𝑜𝑓 𝑏𝑙𝑜𝑏𝑠
𝑑 i
If (D1 < D2) THEN D=( D1 / D2)
ELSE D=( D2 / D1)
Probability= (
A+P+B+BR+D
N
) * 100
Here N number of Parameter.

15. Flow Diagram
Webcam Initialization
Image Capture
Feature Extraction
Probability Calculation
Webcam Control
Probability
90%
Web cam Stop
No
Yes

16. Scope and Limitation:
Scope:
• MARS robot.
• Outer space science.
• Optical character Recognition.
• Content based image indexing.
• Object counting and monitoring.
• Automated vehicle parking systems.
• Visual positioning and tracking.
Limitations:
• We have considered simple object.
• All objects are stationary.
• Webcam move only vertically.
• We have considered only geometric feature of the object to calculate probability.
• Objects are fixed in orientation.

17. Result:

19. Conclusions:
1. Features are extracted carefully from partial object and blob is detect by blob
filter.
2. The feature of the object are made as a database.
3. Probability is computed from feature of the object by open CV.
4. Webcam is controlled by the micro-controller with rack and pinion.

20. Future Recommendation:
1. One can use complex object.
2. Without blob other recognition system such as neural network or fuzzy logic
algorithm can be used to detect object.
3. More objects can be used in database.

21. THANK YOU TO ALL