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Advances in Parallel Techniques for Hyperspectral Image Processing
Published in Sanjay Saxena, Sudip Paul, High-Performance Medical Image Processing, 2022
Yaman Dua, Vinod Kumar, Ravi Shankar Singh
Image compression is the technique of reducing the size of the digital image by storing the information in some other form by using less number of bits than original data. Traditional image compression algorithms take an image as input and produce encoded bitstream as output. The bitstream can be transmitted/stored using less bandwidth/memory, and the original image can be reconstructed at any point in time. These algorithms can be classified into lossy or lossless based on the quality of the desired reconstruction. If the original image is required without any loss of information, the algorithm used is called lossless algorithms. It is mainly used for specific applications where any loss to data is not tolerable, due to this reason only a small compression performance can be achieved. When some amount of information loss is acceptable, lossy algorithms can provide better performance by providing the original image with some information loss and degradation after reconstruction. Digital grayscale or RGB image use these traditional techniques.
Introduction to Advanced Digital Image Processing
Published in Ankur Dumka, Alaknanda Ashok, Parag Verma, Poonam Verma, Advanced Digital Image Processing and Its Applications in Big Data, 2020
Ankur Dumka, Alaknanda Ashok, Parag Verma, Poonam Verma
A digital image is a representation of two-dimensional images as a finite set of digital picture elements termed as pixels. These pixel values represent various parameters like gray levels, height, colors, opacities, etc. of an image in the form of binary digits, and the binary digits can be represented in the form of mathematical equations. The digital image size can be determined by the matrix used to store the pixels based on their size. In order to access a particular pixel in the digital image, the relevant coordinates at x and y axis are defined. Each pixel has its own unique intensity and brightness. Pixels in an image will have different values as per an image or else the images may not appear different from each other. Various mixtures of colors will produce a color image. Pixel dimensions are the horizontal and the vertical measurements of an image. Each pixel is defined using the bit depth which is determined by the number of bits. Resolution is the spatial scale of digital images, is the indicator of the spatial frequency with which the images have been sampled, and can be measured in lpi, ppi, and dpi. Lpi stands for lines per inch and is used generally for magazine printing. Ppi stands for pixels per inch and refers to the pixel arrays depicting the real-world image. Dpi stands for dots per inch and is used to describe the printer’s resolution.
Satellite Optical Imagery
Published in Victor Raizer, Optical Remote Sensing of Ocean Hydrodynamics, 2019
Spatial resolution (called also geometrical resolution) is a measure of the smallest linear dimension on Earth’s ground area that can be resolved by the sensor. The angular resolution of optical lens θ is given by classical formula θ = 1.22λ/D, where λ is the wavelength of the radiation measured, and D the diameter of the aperture (both have to be in the same units, and θ in radians). In remote sensing, spatial resolution is given in terms of the IFOV and expressed by the size of the pixel in meters. In terms of digital images, spatial resolution refers to the number of pixels utilized in construction of the image. Images having higher spatial resolution are composed with a greater number of pixels than those of lower spatial resolution. In remote sensing, several following gradations of spatial resolution are separated: less than 5 m (VHR), 5–100 m (HR), 100–1000 m (medium resolution), and 1000 m–50 km (coarse resolution or low resolution). Spatial resolution is a key parameter of optical observations significantly affected the information content of remotely sensed data and detection performance.
A review on patient-specific facial and cranial implant design using Artificial Intelligence (AI) techniques
Published in Expert Review of Medical Devices, 2021
Afaque Rafique Memon, Jianning Li, Jan Egger, Xiaojun Chen
Fuessinger et al. used an SSM method to reconstruct an artificial bone defect on the right temporal bone. Statistical Shape Models are geometric models that explain the collection of semantically similar objects in a compressed form. SSM represent an average shape of various 3D objects as well as their variation in shape. The actual target surface bone was very near but does not exactly the same because the shape variability of the SSM was not available [42]. However, a Convolutional Neural Network (CNN) is a class of neural networks that is well known for processing data that has a grid like topology, such as an image. A digital image is a binary representation of visual data which contains pixels arranged in a grid-like fashion that contains pixel values to denote how bright and what color each pixel should be.
Effect of surface free energy on water absorption of roller-compacted concrete pavement containing calcium stearate powder
Published in Road Materials and Pavement Design, 2023
Ali Mohammad Lotphi, Amir Modarres
After taking the images by the CT scan, image processing was employed to detect the pores and their distribution in the samples. Image processing basically involves editing and modifying digital images to improve image quality or to extract the information needed. In a digital image, each component of the image, as an entry of a matrix, has a numerical value: the brightness intensity.