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About 10,000 papers for “Digital image processing”

Digital image processing

Ehlers, M., Hannover Univ. (Germany). Inst. fuer Photogrammetrie und Ingenieurvermessungen · OpenGrey (Institut de l'Information Scientifique et Technique) · 1984

TIB: RN 6974 (9) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische Informationsbibliothek

Cited by 0

Digital Image Processing

Julian Besag · Journal of Applied Statistics · 1989

Many of the tasks encountered in image processing can be considered as problems in statistical inference. In particular, they fit naturally into a subjectivist Bayesian framework. In this paper, we describe the Bayesian approach to image analysis. Numerical examples are not included but can be found among the references, in the previous Special Issue of this Journal and elsewhere. It is argued that the Bayesian approach, still in its infancy, has considerable potential for future development.

Cited by 227

DIGITAL IMAGE PROCESSING

Anakhanim Mutallimova Ulkar Huseynova Ulkar Huseynova · PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions · 2024

Digital processing and subsequent picture identification are one of the scientific fields that is now experiencing rapid development. Currently, a lot of technology is focused on developing systems that use graphical images as information, including receiving, processing, storing, and transmitting information. Two primary areas of use for digital image processing methods are of interest: 1. Increasing image quality to enhance human visual perception. 2. Image processing for use in autonomous machine vision systems, including storage, transmission, and presentation. The fundamentals of digital image processing are covered in the first portion, and image operations including quantization, sampling, and alpha compositing are covered in the second. Bitmap storage is covered in the fourth part, and image compression algorithms such as RLE and LZW are covered in the third. The topic of enhancing image quality is covered in the fifth part. The concepts of grouping, segmenting, and object search in an image are covered in the sixth section. The Radon transform is used in the seventh section to discover grid structures and straight lines in an image. Keywords: image processing, quantization, segmenting

Cited by 24Open access

DIGITAL IMAGE PROCESSING

Winnie Y. Lo, Sarah M. Puchalski · Veterinary Radiology & Ultrasound · 2008

Image processing or digital image manipulation is one of the greatest advantages of digital radiography (DR). Preprocessing depends on the modality and corrects for system irregularities such as differential light detection efficiency, dead pixels, or dark noise. Processing is manipulation of the raw data just after acquisition. It is generally proprietary and specific to the DR vendor but encompasses manipulations such as unsharp mask filtering within two or more spatial frequency bands, histogram sliding and stretching, and gray scale rendition or lookup table application. These processing steps have a profound effect on the final appearance of the radiograph, but they can also lead to artifacts unique to digital systems. Postprocessing refers to manipulation of the final appearance of the radiograph by the end-user and does not involve alteration of the raw data.

Cited by 216Open access

Digital Image Processing

Dr.D. Menaka Dr.K.Srividhya Ms.S.Kalyani Ms.L.Anju Ms. K. S. Subhashini · Zenodo (CERN European Organization for Nuclear Research) · 2026

Cited by 0Open access

Digital Image Processing

Stefan G. Stanciu · InTech eBooks · 2012

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further.

Cited by 270

Digital Image Processing

Shriram K. Vasudevan, A. Baskar, R. Muthaiah et al. · 2023

The book provides a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives are to offer an overview to the elementary concepts and practices appropriate to digital image processing as well as to provide theoretical exposition. It starts with an expanded coverage of the fundamentals to provide a more comprehensive and cohesive coverage of the topics including but not limited to: Applications and tools for image processing, and fundamentals with several implementation examples Concepts of image formation OpenCV installation with step-by-step screen shots Concepts behind intensity, brightness and contrast, color models Ways by which noises are created in an image and the possible remedial measures Edge detection, image segmentation, classification, regression, classification algorithms Importance of frequency domain in image processing field Relevant code snippets and the MATLAB® codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understanding The video lectures for specific topics through YouTube enable easy inference for the readers to apply the learnt theory into practice. The addition of contents at the end of each chapter such as quizzes and review questions fully prepare the readers for further study. Graduate students, post graduate students, researchers, and anyone in general interested in image processing, computer vision, machine learning domains etc. can find this book an excellent starting point for information and an able ally.

Cited by 61