What is a digital image? How is processing done with a digital image? Classification of image Block diagram of DIP Quality Workforce Algorithm for Fruit Sorter Block Diagram of Face Detection Block Diagram of Comparing to Two Images

1. DIGITAL IMAGE PROCESSING USING
MATLAB WITH ARDUINO
PREPARED BY
SHIVANG RANA (14BEE099)
RAJ PATEL (14BEE091)
GUIDED BY
PROF. MAYUR GOJIYA

2. OVERVIEW OF THE SESSION
• What is digital image?
• How processing is done with digital image?
• Classification of image
• Block diagram of DIP
• Quality Workforce Algorithm for Fruit Sorter
• Block Diagram of Face Detection
• Block Diagram of Comparing to Two Images

3. WHAT IS A DIGITAL IMAGE?
• Digital image can be defined as set of digital values arranged in 2-dimensional manner.
• Sampling of image
(A) 1 sample/point (B) 3 sample/point (C) Multi sample/point

4. SAMPLING

5. HOW PROCESSING IS DONE ON DIGITAL IMAGE
• Process on digital image focus on 2 major parts
(A) Enhancement of pictorial information for human understanding
(B) Processing of image for storing, transmitting and receiving data.
IR vision image of
PCB
Picture taken & enhanced by drone

6. CLASSIFICATION OF IMAGES
• Reflection Images
- Information primarily about object surfaces
- Optical imaging, radar, sonar, laser
• Emission Images
- Information primarily to the internal object
- Thermal, Infrared, MRI (Magnetic Resonance Interference)
• Absorption Images
- X-rays, Transmission microscopy, Types of sonic images

7. BLOCK DIAGRAM OF DIGITAL IMAGE PROCESSING
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
External world

8. IMAGE RESTORATION
Image
after
enhance
Degradation
function +
Noise
function
Restoration
filter
Restore
d Image
Image Restoration is the operation of taking a corrupt/noisy image and estimating the
clean, original image.

9. MORPHOLOGICAL PROCESSING
• Morphology is a broad set of image processing operations that process images based on
shapes. Morphological operations apply a structuring element to an input image,
creating an output image of the same size. In a morphological operation, the value of
each pixel in the output image is based on a comparison of the corresponding pixel in
the input image with its neighbors.

10. SEGMENTATION
• It is a process of partitioning a digital image into multiple segments (sets of pixels).
• The goal of segmentation is to simplify or change the representation of an image into
something that is more meaningful and easier to analyze.
Source image Segmented image

11. OBJECT RECOGNITION
• Object recognition is a process for identifying a specific object in a digital image or video.
Object recognition algorithms rely on matching, learning, or pattern recognition algorithms
using appearance-based or feature-based techniques.

12. QUANTIZATION
8 bit per pixel 4 bit per
pixel
2 bit per
pixel

13. STEREO IMAGE AND DISPARITY

14. INTERSECTION OF SCIENTIFIC AREAS

15. APPLICATIONS OF IMAGE PROCESSING
- Face Recognition
- For Quality Assurance
- Space exploration and Astronomy
- Medical Applications to detect tumour
- For Image Restoration processes
- Security System Applications
- Document Verification
- Video and Film Effects
- Neural Network
- Agricultural sorting
- Geographical image processing

16. HOW ACTUALLY IMAGE PROCESSING
WORKS?
• Low level process: Noise removal & Image Sharpening
- I/p : Image O/p : Image
• Mid Level process: Object Recognition & Segmentation
- I/p : Image O/p : Attributes
• High Level process: Scene Understanding & Autonomous
Navigation
- I/p : Attributes O/p : Understanding

17. QUALITY CHECK FOR APPLE FRUIT
ALGORITHM

18. Fruit Quality Checker Block Diagram

19. 1: Bright Light
2: Bright Light
3: CMOS Camera
4: Object (Apple)
Processing Flow for the Quality System

20. 1) COLOUR DETECTION
• In this process of fruit colour detected according to RGB values, here fruits
are sorted according to colour and size.
• RGB values for every colour is different combination.
• Algorithm:
- Take small areas colour values of RGB and take all their mean. And store in
a RGB (3 coordinate) variable.
- Compare the value with threshold i.e if G > then threshold => Green
coloured

21. 2) EDGE DETECTION
• Once colour is detected, size is needed to be found out.
• Edge Extraction is key factor in this kind of fruit sorting algorithm
• First Grey image is found. And then Canny method is the best to find
out the edge of any object.

22. FRUIT SIZE GRADING
• We need to keep some standard values for apple size through which
we can compare the test apple with best apple.

26. CONCLUSION

27. BIBLIOGRAPHY
• Gonzalez and Woods, Digital Image Processing. Pearson Education Inc.
• Rudra Pratap, Getting Started With MATLAB, Oxford University.
• http://ivpl.eecs.northwestern.edu/research
• Image processing with Aggelos K. Katsaggelos [M.S., Ph.D. degrees in electrical
engineering from the Georgia Institute of Technology, Atlanta, Georgia].