Image processing and analysis

Vector spaces, signals, and images Digital image acquisition: analogue to digital conversion, sampling and quantization, look-up table conversions, scaling. Digital image formats: representation and description. The discrete Fourier transform (DFT) The discrete cosine transform (DCT) Digital image processing: histogram manipulation, thresholding, image segmentation, clustering techniques, split and merge algorithms, region processing, edge detection, region adjacency graph. Image transformations and histogram equalization Convolution and filtering: linear and non-linear filtering operations, mage convolution, separable convolutions, image enhancement, image restoration. Filter banks Wavelets and the discrete wavelet transform (DWT) Digital image analysis: noise analysis, texture analysis, fourier descriptors, feature extraction, pattern recognition, corner detection, saliency maps. Color image analysis: representation, encoding, scalar and vector approaches, clustering techniques, color invariants, color constancy algorithms. Template matching: similarity and dissimilarity metrics, cross-correlation, multi-resolution algorithms, graph matching, image retrieval., 2D object detection, recognition and positioning. High level image descriptors.

On completion of this course the students: Knowledge Possess an understanding of the fundamental characteristics of digital systems used in imaging, together with general concepts of science, quantitative methods. Possess advanced knowledge of (i.e. to describe, analyse and reason about) basic algorithms for image manipulation, characterization, filtering, segmentation, feature extraction and template matching in direct space and Fourier space. Possess advanced knowledge of how monochrome digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation. Possess advanced knowledge of methods of capturing and reproducing images in digital systems. Possess knowledge and understanding of the mathematical methods commonly used for representing, compressing and processing signals and images. Skills Are able to use mathematical techniques in colour imaging and demonstrate the use of tools such as spreadsheets and specialist maths applications to solve problems in signal and image processing. Are able to to explore a range of practical techniques, by developing their own simple processing functions using library facilities and tools such as, e.g., Matlab or Python. Are able to implement the techniques in the topics studied and compare their performances in certain image processing tasks. Are able to use relevant and suitable methods when carrying out research and development activities in the area of image processing General competence Have the learning skills to continue acquiring new knowledge and skills in a manner that is largely self-directed Are able to contribute to innovative thinking and innovation processes

Forelesninger|Lab.øvelser|Nettstøttet læring|Oppgaveløsning Utfyllende informasjon: The course will be offered both as an ordinary campus course and in a flexible way to off-campus students. Lecture notes, e-lectures and other types of e-learning material will be offered through Fronter. Communication between the teachers and the students, and among the students, will be facilitated by Fronter. Obligatoriske arbeidskrav: None