Course Highlights

This hands-on course will introduce participants to a step-by-step approach in understanding video and image processing concepts, algorithms and their applications. It will come with useful C++ and SCILAB examples and exercises for participants to work on.

Course Objectives

• To provide participants with both fundamental and practical knowledge in video and image processing.
• To explore digital image processing techniques using VC++ and the latest release SCILAB and its toolboxes.

Who Should Attend

Engineer, researchers, scientists, and managers from the manufacturing, government and defense sectors who want to use or plan to use video and image processing, to learn the fundamental knowledge in video and image processing, to know how to use C++ and SCILAB for video and image processing, or to be involved in the purchase of products that involve video and image processing.

Course Outline:

1.   Introduction     
1.1.      Vision Terminology
1.2.      Visual Perception
1.2.1.  The Human Eye
1.3.      Applications
1.3.1.  Case Study: New Traffic Light Violation Enforcement System

2.   Image Acquisition
2.1.      Image Data Acquisition
2.2.      Image Formation and Focusing
2.3.      Magnification Factor
2.4.      f-number
2.5.      Depth of Field
2.6.      Lens Mounting
2.7.      Show case of various lenses
2.8.      FAQ: Basics of CCTV lenses


3.  Camera Interfaces and Video Standards
3.1.      Video Standard
3.1.1.  CCIR, EIA RS170, PAL, NTSC
3.2.      Colour perception
3.3.      Colour Models
3.3.1.  The RGB Model
3.3.2.  The HSI Model
3.4.      Infrared Imaging
3.4.1.  Case Study: Walk through Fever Scanner S3000
3.4.2.  Show case of various cameras, IR camera and stereo camera
3.5.      VC++ programming: Conversion of image from colour to             
                        monochrome

4.  Image Detection
4.1.      Solid State Sensors
4.2.      Sampling and Quantization
4.3.      Image data Acquisition (Signal parameter)
4.4.      Synchronization
4.5.      Interlacing
4.5.1.  VC++ example: High speed deBlurring and LPR
4.6.      Image Information

5.  Indoor Vision
5.1.      Illumination
5.1.1.  Back Lighting
5.1.2.  Front Lighting
5.1.3.  Strobe Lighting
5.1.4.  Structured Lighting
5.1.5.  LED and nIR Lighting
5.2.      Show case of various light sources
5.3.      Case Study: Tuning fork trimming system
5.4.      Exercise: Real-time screw thread inspection for HDD base
                                        Inspection of indexed gears
5.5.      Camera Calibration
5.5.1.  Case Study: Back-Projection method
5.5.2.  Calibration process for Back-Projection method
5.6.      VC++ programming: Detection of IC alignment and orientation

6.  Outdoor Vision
6.1.      Motion detection and object tracking
6.2.      Still object detection
6.3.      Object Tracking
6.3.1.  Case Study: i-VISION Object Tracker for outdoor environment
6.3.2.  Case Study: Detection of fire with low cost colour camera
6.3.3.  Case Study: Digital human detection at Checkpoints
6.4.      VC++ programming: Simple Motion detection

7.  Basic Relationship between Pixels
7.1.      Four basic classifications of image processing algorithms
7.2.      Basic relationship between pixels
7.3.      Neighbourhood
7.4.      Diagonal neighbours
7.5.      Connectivity
7.5.1. Types of connectivity

8. Arithmetic/Logic Operations
8.1.      Point operations
8.2.      Neighbourhood operations (convolution)
8.2.1.   Edge detection
8.2.2.  VC++ programming: Sobel operation  
 8.3.      Image histogram and thresholding
8.4.      Region growing

9.   Image Transformation
9.1.      Geometric spatial transformations
            9.1.1.   Exercise: correct distorted images using affine transformation
9.2.      Discrete Fourier transform
9.3.      Discrete Cosine transform
            9.3.1.   Exercise: DCT application on image compression
9.4.      Discrete wavelet transform

10. Image Enhancement
10.1.    Linear
10.2.    Median filters
10.3.    Wiener filters
10.4.    Intensity adjustmen
10.5.    Exercise: image enhancement for images captured by a PDA

11. Edge Detection
11.1 Edge detection principles
11.2 Sobel
11.3 Roberts
11.4 Laplacia
11.5 Prewitt
11.6 Frei and Chen
11.7 Canny

12. Morphological Image Operation
12.1 Dilation and erosion   
12.2 Open and close
12.3 Connected component labelling
12.4 Feature extraction
12.5 Exercise: fish behaviour analysis

13. Digital Image Compression
13.1 Image compression fundamental
13.2 Huffman coding
13.3 Run-length codin
13.4 Predictive coding
13.5 Transform coding
13.6 JPEG
13.7 JPEG 2000