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 as well as CCTV surveillance system installation and commission. It comes with useful examples and exercises for participants to work on.

Course Objectives:

 To provide participants with both fundamental and practical knowledge in video surveillance, video content analysis, and CCTV surveillance system installation and commission.
 To explore digital image processing techniques using VC++ and the latest release MATLAB 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 install and commission CCTV surveillance systems, to learn the fundamental knowledge in video and image processing, to know how to use C++ and industry software 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: Next Generation Traffic Light Violation
Enforcement System
1.4. Video surveillance market and technology trends

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. Command FAQs on CCTV lens

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 7um-14um IR camera
3.5. VC++ programming: Conversion of image from colour to gray

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. Case Study: High speed deBlurring and LPR on-the-fly
4.5.2. VC++ example: deInterlacing
4.6. Image Information

5. Indoor Vision
5.1. Illumination
5.1.1. Back Lighting, Front Lighting, Strobe Lighting,
Structured Lighting, LED and nIR Lighting
5.1.2. Case Study: Tuning fork trimming system
5.1.3. Exercise: Real-time screw thread inspection for HDD base
5.1.4. Exercise: Inspection of indexed gears
5.2. Camera Calibration
5.2.1. Case Study: Back-Projection method
5.3. Hotelling transform
5.3.1. VC++ programming: Detection of IC alignment
and orientation
5.4. Projections
5.4.1. Case Study: Feature-based OCR for animated characters
5.4.2. Case Study: Feature-based OCR for real-time cards reading
with web cameras

6. Outdoor Vision
6.1. Motion detection
6.2. Still object detection
6.3. Object Tracking
6.3.1. Live demonstration: i-VISION Object Tracker for
outdoor environments
6.3.2. Case Study: Detection of fire and smoke with low cost
colour camera
6.3.3. Case Study: Digital human detection on busy highway
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 neighbour
7.5. Connectivity
7.5.1. Types of connectivity

8. Arithmetic/Logic Operations
8.1. Neighbourhood operations (Convolution process)
8.1.1. Edge detection
8.1.2. VC++ programming: Sobel operation
8.2. Blob analysis, Boundary and Regional Descriptors
8.2.1. Boundary descriptors: Chain coding, Signature, Perimeter,
Polygonal Approximation
8.2.2. Regional descriptors: Area and coverage, Compactness,
Eccentricity, Invariant moments
8.2.3. Exercise: Chain coding
8.3. Hough transform
8.3.1. Exercise: Hough transformation
8.4. Image histogram and thresholding
8.4.1. Contrast stretching
8.4.2. Exercise: Dynamic thresholding
8.5. Region growing
8.5.1. Exercise: Single pass 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 filters
10.2. Median filters
10.3. Wiener filters
10.4. Intensity adjustment
10.5. Exercise: image enhancement for images captured by a PDA

11. Image Segmentation
11.1. Edge detection principles
11.2. Edge detection using various edge operators
11.3. Point detection
11.4 Region-based segmentation
11.5 Quadtree decomposition

12. Morphological Image Operations
12.1. Dilation and erosion
12.2. Open and close
12.3. Connected component labelling
12.4. Feature extraction
12.4.1. Case Study: People Tracking using Webcam
12.5. Exercise: fish behaviour analysis

13. Introduction to Biometric Systems
13.1. Face recognition
13.2. Fingerprint recognition
13.3. Iris recognition

14. CCTV Surveillance System Installation
14.1. Installation preparation
14.2. Installation of CCTV system
14.3. Reinstate work site

15. CCTV Surveillance System Commission
15.1. Prepare commission work activities
15.2. Test and commission CCTV systems
15.3. Hand over CCTV systems
15.4. User training