Image processing

By Scott Weiss. Magnetic Resonance Materials in Physics, Biology and Medicine, 2018

2,723 citations recorded by the source corpus.

Image processing entails altering an image's composition to enhance its graphical content for human interpretation and autonomous machine perception. Digital image processing is a subset of the electronic domain in which an image is transformed into a collection of tin y numbers, or pixels, that reflect a physical property, like s cene radiance, are stored in a digital memory, and are then processed by a computer or other digital hardware. Two key application areas have sparked interest in digital i mage processing techniques: improving pictorial information for human interpretation and processing image data for storage, transmission, and representation for autonomous machine perception. Boundaries are described by edges, and as edge detection i s one of the most challenging image processing tasks, it is an issue of fundamental significance .

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Citations in the corpus

A Survey of Shape Feature Extraction Techniques (2008, methodology): “[26, 27] and [28] gave the boundary tracing method and morphological methods to achieve convex hulls respectively.”
Vehicle Detection Using Normalized Color and Edge Map (2007, methodology): “In [18], a good moment-based method is provided for estimating the orientation of the longest axis of a region.”
Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “Table 1: Examples of inverse problems in imaging Application Forward model Notes Denoising [58] A = I I is the identity matrix Deconvolution [58, 59] A(x) = h ∗ x h is a known blur kernel and ∗ denotes convolution.”
LINKING BENTHIC ALGAL BIOMASS TO STREAM SUBSTRATUM TOPOGRAPHY 1 (2007, methodology): “Z-stacks were analyzed with Image J digital image analysis software (Abramoff et al. 2004; U. S. National Institute of Health, Bethesda, MD, USA, http://rsb.info.nih.gov/ij/) and an associated plugin, SurfCharJ (Chinga et al. 2003), for mean surface roughness (the average distance from the bottom…”
American sign language recognition with the kinect (2011, methodology): “RADAR, LIDAR, structured light techniques and sonar have all been used to generate depth maps [19].”
An automated palmprint recognition system (2005, methodology): “After that, contour of the hand shape is obtained by using eight neighborhood border tracing algorithm [14].”