Building Community Memory through On-line
... building community memory through on-line technology should be explored. In as much as is possible, these concepts, tools and processes should be easily transferable and applicable across different communities. It is hoped that a cohesive process and associated tools for gathering, indexing and arch ...
... building community memory through on-line technology should be explored. In as much as is possible, these concepts, tools and processes should be easily transferable and applicable across different communities. It is hoped that a cohesive process and associated tools for gathering, indexing and arch ...
Difference-Imaging of Undersampled Data - IPAC
... The consequences of this are that 1. You cannot fully register a stack of images. Only integer-pixel shifts are allowed. 2. No reference image with the same sampling as the stack of target images will work effectively. Instead, we need to construct an oversampled effective reference, which can then ...
... The consequences of this are that 1. You cannot fully register a stack of images. Only integer-pixel shifts are allowed. 2. No reference image with the same sampling as the stack of target images will work effectively. Instead, we need to construct an oversampled effective reference, which can then ...
Image processing 1 16.03.2017 Tasks in this course are given in
... std2(C) function. Calculate mean and standard deviation of green and blue channels of image A. 2. Function find is handy in searching such pixel values from an image that fulfills certain criteria. Create a black and white presentation of image C such that background is black (0) and foreground (1) ...
... std2(C) function. Calculate mean and standard deviation of green and blue channels of image A. 2. Function find is handy in searching such pixel values from an image that fulfills certain criteria. Create a black and white presentation of image C such that background is black (0) and foreground (1) ...
Medical Imaging Sciences - American University of Beirut
... Medical Imaging is a branch of science and healthcare that engages the use of imaging to both diagnose and treat disease processes visualized within the human body. Radiographers produce diagnostic images of internal structures of the body, performing investigations that vary in complexity, from sim ...
... Medical Imaging is a branch of science and healthcare that engages the use of imaging to both diagnose and treat disease processes visualized within the human body. Radiographers produce diagnostic images of internal structures of the body, performing investigations that vary in complexity, from sim ...
Deflectometry: 3D-Metrology from Nanometer to Meter
... We will discuss deflectometry as an imaging principle with a wide spectrum of new applications, and we will demonstrate some novel deflectometric sensors. The intrinsic features of deflectometry—incoherence, source encoding, high dynamical range, simplicity, and scalability—enable new sensors and un ...
... We will discuss deflectometry as an imaging principle with a wide spectrum of new applications, and we will demonstrate some novel deflectometric sensors. The intrinsic features of deflectometry—incoherence, source encoding, high dynamical range, simplicity, and scalability—enable new sensors and un ...
Kuva-analyysi
... polygons or of a combination of above • Surfaces are utilised to render simple objects (balls, ...
... polygons or of a combination of above • Surfaces are utilised to render simple objects (balls, ...
Medical image computing
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, data science, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care.The main goal of MIC is to extract clinically relevant information or knowledge from medical images. While closely related to the field of medical imaging, MIC focuses on the computational analysis of the images, not their acquisition. The methods can be grouped into several broad categories: image segmentation, image registration, image-based physiological modeling, and others.