- Basic principles of images: human visual system, light and color, acquisition and image representation.
- Image processing in space: linear and nonlinear transformations, histograms, 2D signals and systems, 2D convolution, spatial filters, gradient of images.
- Frequency image processing: 2D Fourier transform and its properties, Radon transform, 2D transformation and its properties. 2D discrete Fourier transform and its properties. 2D 2D transformations.
- Noise correction and compression of images: wavelet transform, PCA, signal to noise ratio, binary filters, Perona-Malik operator
- Color image processing: RGB model, contrast enhancement and sharpness in images, color correction and lighting.
- Morphological operators: Dilation, Erosion, language detection, morphological operators for binary images.
- Identifying shapes and characteristics: Identifying points, lines, thresholds, Sifts, Hogs, Hough transformations are classified
- Active contours, split & merge, graph-cuts, mean-shift, watershed: Segmentation of images
- Match images: pixel-based matching, character-based matching
- Advanced applications in image processing and computer vision: tracking, stereo, face recognition
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implemented in MATLAB 2018R
- Exercise 1 - Basic Image Operations
- Exercise 2 - Color Spaces
- Exercise 3 - Transformations
- Exercise 4 - Edges and Feature Descriptors
- Exercise 5 - Geometric Transformations
- Hackton
- Final project
Maor Assayag
Refael Shetrit
B.Sc in Computer Engineering, Ben Gurion University, Israel.