Cursos de formación

Objective: Understand different image types available in MATLAB, and how they can be read into MATLAB.

• Exploring image types
• Supported MATLAB data types for representing images
• Binary image
• Grayscale images
• Indexed image
• RGB image
• Importing and exporting images in MATLAB
• Viewing the image
• Single image
• Multiple image frames
• Finding image pixel values: IMPIXELINFO
• Calculating image statistics
• Converting image formats

Objective: Enhance image characteristics by adjusting the image intensity and isolating a region of interest.

• Adjusting image intensity
• Histogram stretching
• Histogram equalization
• Histogram adjustment
• Using arithmetic functions to enhance images
• Addition - increase brightness
• Multiplication - increase sharpness
• Subtraction - detect change
• Division - detect change
• Correcting image alignment: rotating
• Cropping and resizing images
• Exploring the basics of image registration
• Selecting control points
• Registering an image
• Correcting lens distortion

Objective: Understand how block processing works; investigate the implementation of both spatial domain and frequency domain filters; investigate how to use filtering techniques to reduce the effects of unwanted distortions such as noise, blurring, and background illumination or to enhance an image.

• Defining filtering
• Filtering process
• Performing filtering
• Filtering applications: smoothing, edge detection, and sharpening
• Frequency domain filter design
• Modeling and removing noise
• General block operations
• Region of interest processing
• Specific applications of filtering

Objective: Extract image features and measurements using different segmentation methodologies.

• Isolating image features using thresholding
• Performing morphological segmentation
• Creation of structuring elements
• Erosion and dilation
• Measurement of region properties
• Reconstructing images and objects
• Performing morphological reconstruction
• Detecting edges in an image
• Edge detection functions
• Hough transform
• Applying color-based image segmentation
• Isolating objects using watershed segmentation
• Segmenting images based on texture

Objective: Investigate and solve problems using a set of case studies.

• Motion detection
• Text recognition
• Finding particles
• Bouncing ball
• Ball tracking
• Microarray analysis
• Pattern matching
• Face recognition