Scilab for Image Processing & Computer Vision
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a sub-field of digital signal processing, digital image processing has many advantages over analogue image processing; it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing. Computer vision deals with how computers can be used for gaining high level understanding from digital images or video with the aim to automate human visual system.
This training provides the foundation concepts and techniques for digital image processing and computer vision with Scilab with the home-grown use of the IPCV module (Image Processing and Computer Vision module), The IPCV module extends the capabilities of its predecessors on principles, theories, algorithms and practical implementation.
The IPCV has been one of the most downloaded modules for Scilab in recent months. By participating in this training, your exploration into IPCV is complete with the training materials, illustrated with numerous examples of practical significance by our very own developers.
Come and participate in this training program to prepare yourself in dealing with image processing and computer vision. Join us now!
Course Outline
Fundamental of Images Processing and Computer Vision
- Image representation in Scilab
- Importing and exporting images
- Displaying the image with Scilab
- What is image pixel and what does the value means
- Converting image between Data Classes and Image Types
- Image Characteristics
- Image Processing vs Computer Vision
Source of Images, from Pictures, Video and Camera
- Importing image from pictures
- Importing video to Scilab
- Acquiring image from usb webcam to Scilab
- Viewing the acquired image
Image Enhancement
- Adjusting image intensity
- Histogram stretching
- Histogram equalization
- Histogram adjustment
- Enhancing images using arithmetic operations
- Addition – Increase brightness
- Multiplication – Increase sharpness
- Subtraction – Detect change
- Division – Detect change
- Correcting image alignment
- Rotating images
- Affine Transform
- Perspective Transform
- Cropping and resizing images
Filtering Images
- Block processing
- Defining block processing
- Distinct block operations
- Sliding neighborhood operations
- Image convolution and correlation
- Spatial domain filtering
- Frequency domain filtering
- Region of interest processing
Image Restoration
- Reducing noise
- Modeling noise
- Filtering noise
- Deblurring images
- Correcting background illumination
Image Feature Extraction
- Image thresholding
- Edge detection
- Edge detection function
- Radon transform
- Morphological segmentation
- Structuring element
- Dilation and erosion
- Measuring region properties
- Color-based image segmentation
Real-time Frames Processing
- Real-time frames processing with webcam