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What is Digital Image Processing

Encyclopedia of Data Science and Machine Learning
The process of applying computer algorithms to digital images to satisfy a visual related computing task.
Published in Chapter:
Machine Learning Approach to Art Authentication
Bryan Todd Dobbs (University of North Carolina at Charlotte, USA) and Zbigniew W. Ras (University of North Carolina at Charlotte, USA)
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-7998-9220-5.ch089
Abstract
The popularity of machine learning algorithms produced numerous applications in computer vision in the past 10 years. One application is art authentication, which assures that a piece of art is created by an artist. The models produced by machine learning algorithms provide an objective measure to authenticate an artist to their artwork collection. This article discusses an experiment using the residual neural network machine learning algorithm. This experiment demonstrates how a computer can distinguish between 34 and 958 artists with various degrees of confidence.
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People Counting System Using Video Camera
The digital image processing is the set of techniques applied to digital images in order to improve quality or facilitate the search for information.
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An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images
Application of computer algorithms for performing image processing on several digital images.
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Importance of Deep Learning Models in the Medical Imaging Field
Computerized processing of digital images to make images clearer and to get information from the image.
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E-Health Applications in Ophthalmic Diseases: Ongoing Developments
The application of mathematical algorithms to digital images is of great interest in improving the quality and detection of edge, segmentation, recognition of patterns, and classification. Digital imaging processing definitively shows the possibility of developing tools that help in diagnosis and complement the studies carried out by medical specialists.
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Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment
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