Introduction to Digital Image Processing
2 IMAGE
One picture is worth more than ten thousand words.
3 WHAT IS AN IMAGE?
• An image is a 2D light intensity function, f(x,y)
• where x and y are spatial coordinates
• Amplitude of f at any pair of coordinates (x,y) is called the “intensity” or “gray-level” of the image.
• When spatial coordinates and amplitude values are all finite, discrete
quantities, the image is called as “digital image”.
• Digitization implies that a digital image is an approximation of a real scene.
• Image Processing: Applying a number of computer algorithms to process
digital images.
4 A DIGITAL IMAGE
5 A DIGITAL IMAGE
6 A DIGITAL IMAGE
7 TYPES OF DIGITAL IMAGES
Digital
Image
Black and Another
Color Image
White Image Classification
Binary Image
Gray Image Still Image Movie Image
(Two tone)
8 TYPES OF DIGITAL IMAGES
Digital Image
Binary Image
Gray Image Color Image
(Two tone)
I(x,y) {0 , 1} I(x,y) [0-255] IR(x,y) IG(x,y) IB(x,y)
9 TYPES OF DIGITAL IMAGES
10 TYPES OF DIGITAL IMAGES
11 TYPES OF DIGITAL IMAGES
12 COLOR COMPONENTS
13 WHAT IS IMAGE PROCESSING?
Image processing is manipulation of images by using brain or computer
14 WHY TO PROCESS IMAGES?
• To prepare it for display after acquisition •To enhance or restore them
• Correct aperture and color balance • Improve visibility of features
• Correct illumination, brightness, etc • Repair photographic errors
• Reconstruct image from projections
•To extract information from them
•To Prepare it for printing •Arial Surveillance
• Adjust image size •Face recognition
• Halftoning •Object Detection
•To facilitate their storage
• Efficient storage in digital cameras
• Video streaming on the internet
15 TYPES OF IMAGE PROCESSING TASKS
• low level Image • Mid level Image • High level Image
Processing Tasks: Processing Tasks: Processing Tasks:
• Sampling and Quantization • Edge detection/extraction • Automatic face recognition
• Noise Removal • Region Extraction and • Autonomous Navigation
• Restoration Segmentation • Automatic character
• Enhancement • Feature Extraction recognition
• Geometric manipulation • Shape Analysis • Anomaly detection
• Image coding and compression • Image detection etc. • Object Recognition etc.
etc.
16 EXAMPLES: IMAGE ENHANCEMENT
17 EXAMPLE: COLOR ENHANCEMENT
18 EXAMPLE: NOISE REMOVAL
19 IMAGE PROCESSING EXAMPLES
20 PSEUDO COLOR IMAGE PROCESSING
21 EXAMPLES: MEDICINE
22 IMAGE DEBLURRING: MOTION BLUR
23 EXAMPLE: FACE BLURRING
24 SATELLITE IMAGE SEGMENTATION
25 EXAMPLES: PCB INSPECTION
26 CONVEYER BELT APPLICATIONS
• Checking and Sorting
• For Examples: Checking bottles in supermarket
•Quality Control:
• Does object have the correct dimensions, color,
shape, etc.?
• Is object broken, crack on surface ?
•Robot Control
• Find precise location of the object to be picked
27 REGION EXTRACTION
28 CHARACTER RECOGNITION: AUTOMATIC NUMBER RECOGNITION
◼ Application :- To develop a robust algorithm for Optical Character Recognition for
reading the Name Plates of Pumps.
Acquire
Image
Enhancement
Thresholding Morphology Recognition
Output Text
29 MACHINE VISION: NUMBER RECOGNITION.
➢Original image ➢Saturation plane ➢Thresholded image
➢Hue plane ➢Intensity plane
30 LICENSE NUMBER PLATE RECOGNITION
Original Image Binarized image
Inverted binarised image
31 BIOMETRICS
◼ It is automated methods of recognizing a person based on physiological
and/or behavioral characteristics.
• Physiological characteristics : • Behavioral characteristics :
• Face • Hand writing
• Fingerprints • Signature
• Gestures • voice
• Hand geometry
• Gait
• Iris
• Retina
32 BIOMETRICS: FINGERPRINT RECOGNITION
33 BIOMETRICS: IRIS RECOGNITION
34 IMAGE WATERMARKING Visible Watermark
Original Image Watermark Image Large Small
35 VISUAL INFORMATION RETRIEVAL
36 VIR:CBIR
Finger print searching can’t be done using a keyword search. ➢ Medical Image Databases
Face Recognition
➢ Trademark Image Registration
37 DIGITAL TRADEMARK SEARCHING
Sample Sample
Word-In-Mark Composite
Device-In-Mark
Trademarks Trademarks
Trademarks
38 AUTOMATIC QUALITY INSPECTION
39 IMAGE PROCESSING CHALLENGES
1. Illumination Variation:
40 IMAGE PROCESSING CHALLENGES
2. Pose Variability:
41 IMAGE PROCESSING CHALLENGES
3. Intra-class Variability:
42 IMAGE PROCESSING CHALLENGES
4. Occlusion:
43 IMAGE PROCESSING APPLICATIONS
Applications (Late 1960s & Early 1970s): Recent Applications:
• Space applications •Manufacturing and Quality inspection
• Robotic Navigation
• Medical applications
• Autonomous Vehicles
• Remote Earth resource observations
• Security and Monitoring
• Astronomy
• Optical Character Recognition (OCR)
• Satellite image processing • Biometrics: Face, Iris Recognition
• Defense • Medical Application: X-ray, MRI, CT-scan
• Industrial Applications imaging
• Digital Libraries and Video Searching
• Video Manipulation and Editing
• Visual Information Processing
• Video Surveillance and Monitoring
44 IMAGE FORMATION
Light is emitted by source Light is reflected from object Reflected light is
sensed by eye or by camera
45 IMAGE FORMATION
46 IMAGE FORMATION
47 IMAGE FORMATION
48 IMAGE FORMATION
The two triangles are similar, so wee can write:
𝑓 𝑟′
= (1)
𝑧 𝑟
Also, the triangle formed by 𝑥 𝑎𝑛𝑑 𝑦 coordinates and the
perpendicular distance 𝑟 is similar to the triangle formed by the
image plane coordinates 𝑥 ′ 𝑎𝑛𝑑 𝑦 ′ and the perpendicular distance 𝑟 ′ ,
so wee can write:
𝑥′ 𝑦′ 𝑟′
= = (2)
𝑥 𝑦 𝑟
49IMAGE FORMATION
Combining equations (1) and (2)
𝑥′ 𝑓 𝑦′ 𝑓
= 𝑎𝑛𝑑 =
𝑥 𝑧 𝑦 𝑧
So, the position of a point (𝑥, 𝑦, 𝑧) in the image plane is given as
below:
𝑓
𝑥′ = 𝑥
𝑧
𝑓
𝑦′ = 𝑦
𝑧
50 SAMPLING AND QUANTIZATION
◼ Camera: transforms the 3D
world into 2D image
◼ Perspective projection (Optics)
◼ Sampling the image plane
◼ Finite number of pixels
◼ Quantizing the color/gray-level
◼ Finite number of gray-level, colors
51 SPATIAL QUANTIZATION
52 IMAGE PROPERTIES: RESOLUTION
53 IMAGE PROPERTIES: CONTRAST
53
54 IMAGE SAMPLING
1024x1024 512 x 512 256 x 256
128 x 128 64 x 64 32 x 32
55 IMAGE ZOOMING
56 HOW MANY GRAY LEVELS ARE REQUIRED?
57 STUDY MATERIAL
Textbooks:
➢ Gonzalez, Rafael C, “Digital image processing”, Pearson
Education India, 2009.
➢ Schalkoff, Robert J, “Digital image processing and
computer vision”, WileyNew York, 1989.
➢ Jain, Anil K, “Fundamentals of digital image processing”,
Prentice-Hall, Inc., 1989.
NPTEL Video Lecture link:
➢ https://archive.nptel.ac.in/courses/117/105/117105079/
THANK YOU !
