Digital Image Processing and Recognition

TL;DR: A new approach to reconstruct the image from the projections within a limited range, called the image moment method, has been proposed and validated through computer simulations.

TL;DR: In this article, the use of very high spatial resolution satellite (VHSRS) technology is combined with archaeological methods to investigate the interplay between limestone quarries and roads in the study region.

TL;DR: In this paper, an interactive image analysis system (Susie) is demonstrated on a variety of tasks in automatic visual inspection, including: (a) the existential inspection and pitch measurement of female screw threads; (b) checking, the legibility of printing on tablets; (c) the enhancement of visibility of industrial radiographs; detecting defects on polished metal surfaces(hydraulics cylinder bores and bright, extruded copper bars); (e) analysing the texture of machined metal surfaces; locating the ends of fibrelike objects (asbestos) viewed under a

TL;DR: An image segmentation problem is formulated as a hypergraph partitioning problem and the recent k-way hypergraph techniques are used to find the partitions of the image into regions of coherent brightness/color.

TL;DR: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

TL;DR: The primary problem dealt with in this paper is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.

TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.