Faculties and centres
Digital Image Processing is a constant evolving subject in its technological aspect, both in processing power and transmission, but also for the breakthrough digital processing techniques have experienced in areas such as filtering, compression and image analysis.
Current and future applications of Digital Image Processing are virtually endless. Digital television, video games, mobile phones, animated films are just some examples of the impact it currently has in the consumer society. In industrial and service applications, machine vision inspection and robotics are of great importance, as well as the great development in remote sensing and medical imaging.
From a historical perspective, digital image processing is a multidimensional generalization of the techniques of one-dimensional digital signal processing (DSP). The origins of DSP back to the nineteenth century, although its practical development does not appear until the sixties, when Cooley and Tukey proposed an efficient algorithm for the calculation of the Fourier transform: The FFT (Fast Fourier Transform). Then, with the advent of microprocessors, specific processors adapted for the calculation of FFT's were designed. These two factors have been very important in the spectacular advance of DSP, which has today penetrated all sectors of society and especially in the digital image processing.
Previous courses required:
20014 - SEÑALES Y SISTEMAS
20015 - TEORÍA DE LA COMUNICACIÓN
20019 - TRATAMIENTO DIGITAL DE SEÑAL
20021 - TELEVISIÓN.
UA Basic Transversal Competences
Basic Transversal Competences
Specific Competences: >> Competences Specific to Sound and Image
SO1: To explore and evaluate the role of different components of a digital image processing.
SO2: Understanding the wide range of present and future applications of digital image processing, both for the visible spectrum images such as those from other sensors (Radar, Ultrasonic, etc.).
SO3: To introduce students to the multidimensional digital signal processing in the field of images (2D) and video (3D).
SO4: To identify the problems of image processing as signal problems of linear systems, whose basic concepts have been acquired in previous courses.
SO5: Extending the theory of sampling and spectral representation to multidimensional signals.
SO6: Differentiate and justify the two major approaches to image processing: processing in the spatial and frequency domains.
SO7: To analyze and implement different techniques of coding and image compression.
SO8: Understanding the image analysis using methods of image segmentation and mathematical morphology.