Course Catalogue

LJMU Partner Taught

To develop learners’ understanding basic concepts, methodologies and algorithms of digital image processing. For the last few decades, image processing has emerged as an important technology to extract useful information for scene understanding. The primary goal of this course is to lay a solid foundation for students to study advanced image analysis topics such as object recognition, wavelet transforms.

Outline Syllabus:Introduction to Image – processing system. Digital imaging modalities: gamma-ray imaging, x-ray imaging, CT (computed tomography) imaging ultraviolet imaging, visible-spectrum imaging, millimetre-wave imaging, radio-band imaging, ultrasound imaging, electron microscopy, information overlays/ human-generated imagery, low-mid, and high-level image processing, anatomy of the human eye, facts about the fovea, brightness perception, optical illusions/perceptual phenomena, facts about rods and cones, colour spaces additive colour spaces (RGB). Image transforms 2D discrete Fourier transform, discrete cosine transform (DCT), Hadamard transform, Haar transform, Wavelet transform. Geometric transforms and Spatial Filtering Image histograms, point operations, thresholding, digital negative, contrast stretching, histogram equalization, histogram specification, gamma correction, spatial filters intro to edge detection, geometric operations, translation, scaling, flipping, linear transformations, rotation, shears, affine transformations, projective transformations, example: estimating a projective transformation, bilinear interpolation. Spatial filters connection to convolution, the spatial domain applying spatial filters, smoothing filters, averaging to remove g. Gaussian noise, sharpening filters, general low-pass filters, horizontal and vertical edge detectors, the Laplacian. Enhancing edges, Unsharp masking, Sobel edge detectors, the median filter. Filtering in the Frequency Domain Interpreting the 2D DFT of images. MATLAB’s fftshift. Frequency-domain filtering. Ideal (box) filtering. Circular (instead of rectangular) filters. Spatial vs. frequency domain trade-offs. Looking at filters in the frequency domain. Gaussian low-pass filters. High pass filtering. Laplacian filters. Sampling and aliasing. Edge detection The Sobel edge detector, image gradients, thresholding gradient, Laplacian-of-Gaussian detector, The Canny edge detector. Edge linking. Boundary following. Chain coding, The Hough transform. Thresholding & segmentation Thresholding, relationship to segmentation, relationship to image histogram, Otsu's algorithm, variable/ adaptive thresholding. Object detection Template matching, cross-correlation, Image features, Shi-Tomasi corner detector, affine invariance, SIFT features, in visual effects Morphological Image processing Structuring elements, operations on sets of pixels, erosion, dilation, opening, closing, opening and boundary extraction. Colour Image processing Colour formation, human perception of colour, chromaticity diagram, gamma correction, colour image segmentation. Image restoration & reconstruction Image degradation model, estimating the noise model, removing periodic noise with a notch filter, adaptive filters, blur/degradation, the inverse filter, the Wiener filter, Introduction to image reconstruction from projections, CT scan geometries, the radon transform, the Fourier-Slice theorem.

Additional Information:This module is part of the Level 6 of the BEng(Hons) in Biomedical Engineering