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Digital Image Processing using MatLAB with Arduino | PPTX
DIGITAL IMAGE PROCESSING USING
MATLAB WITH ARDUINO
PREPARED BY
SHIVANG RANA (14BEE099)
RAJ PATEL (14BEE091)
GUIDED BY
PROF. MAYUR GOJIYA
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
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
SAMPLING
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
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
BLOCK DIAGRAM OF DIGITAL IMAGE PROCESSING
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
External world
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.
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.
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
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.
QUANTIZATION
8 bit per pixel 4 bit per
pixel
2 bit per
pixel
STEREO IMAGE AND DISPARITY
INTERSECTION OF SCIENTIFIC AREAS
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
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
QUALITY CHECK FOR APPLE FRUIT
ALGORITHM
Fruit Quality Checker Block Diagram
1: Bright Light
2: Bright Light
3: CMOS Camera
4: Object (Apple)
Processing Flow for the Quality System
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
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.
FRUIT SIZE GRADING
• We need to keep some standard values for apple size through which
we can compare the test apple with best apple.
CONCLUSION
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].

Digital Image Processing using MatLAB with Arduino

  • 1.
    DIGITAL IMAGE PROCESSINGUSING MATLAB WITH ARDUINO PREPARED BY SHIVANG RANA (14BEE099) RAJ PATEL (14BEE091) GUIDED BY PROF. MAYUR GOJIYA
  • 2.
    OVERVIEW OF THESESSION • 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 ADIGITAL 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.
  • 5.
    HOW PROCESSING ISDONE 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 OFDIGITAL 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 dImage Image Restoration is the operation of taking a corrupt/noisy image and estimating the clean, original image.
  • 9.
    MORPHOLOGICAL PROCESSING • Morphologyis 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 isa 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 • Objectrecognition 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 perpixel 4 bit per pixel 2 bit per pixel
  • 13.
  • 14.
  • 15.
    APPLICATIONS OF IMAGEPROCESSING - 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 IMAGEPROCESSING 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 FORAPPLE FRUIT ALGORITHM
  • 18.
    Fruit Quality CheckerBlock 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.
  • 27.
    BIBLIOGRAPHY • Gonzalez andWoods, 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].