Digital Image Processing Algorithms and Applications

By Ioannis Pitas. 2000

603 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d71ddd89c327c8121cf302b75adf4e144552e332

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Citations in the corpus

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model (2022, background): “FF-Parser can be regarded as a learnable version of frequency filters which are wildly applied in the digital image processing [10].”
Superpixels and Polygons Using Simple Non-iterative Clustering (2017, methodology): “Contour tracing: The closed path along the boundary of each superpixel is traced using a standard contour tracing algorithm [26].”
A fully automated greedy square jigsaw puzzle solver (2011, methodology): “Under such conditions we attempt to find these segments using a region growing segmentation algorithm [17] with random seeds and a segmentation predicate based on the best buddies metric (cf.”
A review on automatic fetal and neonatal brain MRI segmentation (2017, unknown): “Further details on the aforementioned techniques can be found in textbooks on image processing, e.g. Pitas (2000); Pratt (2007); Bankman (2008).”
Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods (2004, background): “To assemble and link edge pixels separated by small breaks is computationally very intensive, even in a crude approximation (Ballard and Brown 1982, Lee and Jurkevich 1990, Pitas 2000).”
Global Filter Networks for Image Classification (2021, methodology): “The formulation of the global filter layer is motivated by the frequency filters in the digital image processing [32], where the global filter K can be regarded as a set of learnable frequency filters for different hidden dimensions.”