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Systems of Perception
Engineering & Social Sciences Program
Madrid, Spain
Dates: 8/31/17 - 12/22/17
Systems of Perception
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Computer Engineering
Instruction in: English
Transcript Source: Partner Institution
Course Details: Level 400
Recommended Semester Credits: 3
Contact Hours: 42
DESCRIPTION
1.- Introduction to Computer Vision.
1.1. Definitions.
1.2. History
1.3. Modules
1.4. Human vision sense
1.5. Applications
2.- Elements of Computer Vision systems
2.1. Lenses
2.2. Digital cameras
2.3. Image processing boards
2.4. Software
3.- Digital images.
3.1. Spatial sampling, grey levels.
3.2. Pixels.
3.3. Arithmetical and logical Operations.
3.4. Colour.
4.- Spatial filtering
4.1. Image Transformations.
4.2. Convolution.
4.3. Correlation.
4.4. Geometrical Transformations.
5.- Image Pre-processing.
5.1. Contrast
5.2. Histogram modification
5.3. Noise reduction
5.4. Image sharpening
5.5 False colour
6.- Feature extraction.
6.1. Edge detection.
6.2. Movement detection.
7.- Segmentation.
7.1. Thresholding and labelling.
7.2. Region growing.
7.2. Split & Merge.
7.3. Mean-Shift
8.- Morphological Transforms and object description.
8.1. Morphological Transforms for binary images
8.2. Morphological Transforms for grey level images
8.3. Region descriptors.
8.4. Shape descriptors.
9.- Object recognition.
9.1. Basic concepts.
9.2. Classifier evaluation
9.3. Bayes¿ classifier.
9.4. Clustering.
1.1. Definitions.
1.2. History
1.3. Modules
1.4. Human vision sense
1.5. Applications
2.- Elements of Computer Vision systems
2.1. Lenses
2.2. Digital cameras
2.3. Image processing boards
2.4. Software
3.- Digital images.
3.1. Spatial sampling, grey levels.
3.2. Pixels.
3.3. Arithmetical and logical Operations.
3.4. Colour.
4.- Spatial filtering
4.1. Image Transformations.
4.2. Convolution.
4.3. Correlation.
4.4. Geometrical Transformations.
5.- Image Pre-processing.
5.1. Contrast
5.2. Histogram modification
5.3. Noise reduction
5.4. Image sharpening
5.5 False colour
6.- Feature extraction.
6.1. Edge detection.
6.2. Movement detection.
7.- Segmentation.
7.1. Thresholding and labelling.
7.2. Region growing.
7.2. Split & Merge.
7.3. Mean-Shift
8.- Morphological Transforms and object description.
8.1. Morphological Transforms for binary images
8.2. Morphological Transforms for grey level images
8.3. Region descriptors.
8.4. Shape descriptors.
9.- Object recognition.
9.1. Basic concepts.
9.2. Classifier evaluation
9.3. Bayes¿ classifier.
9.4. Clustering.