High, J., Gennery, D., Saund, E. et al. · NASA STI Repository (National Aeronautics and Space Administration)
The field of computer vision is surveyed and assessed, key research issues are identified, and possibilities for a future vision system are discussed. The problems of descriptions of two and three dimensional worlds are discussed. The representation of such features as texture, edges, curves, and corners are detailed. Recognition methods are described in which cross correlation coefficients are maximized or numerical values for a set of features are measured. Object tracking is discussed in terms of the robust matching algorithms that must be devised. Stereo vision, camera control and calibration, and the hardware and systems architecture are discussed.
Cited by 0Open access
Boal Martín-Larrauri, Jaime · Comillas Repository (Comillas Pontifical University) · 2016
Grado en Ingeniería Electromecánica, Máster Universitario en Ingeniería de Telecomunicación, Máster Universitario en Ingeniería Industrial
Cited by 0Open access
Van Droogenbroeck, Marc, Cioppa, Anthony · Open Repository and Bibliography (University of Liège) · 2015
Cited by 0Open access
Richard Szeliski · Texts in computer science · 2022
Cited by 757
Paul Breiding, Kathlén Kohn, Bernd Sturmfels · Oberwolfach Seminars · 2024
Abstract The field of computer vision studies how computers can gain understanding from images and videos, similar to human cognitive abilities. One of the classical challenges is to reconstruct a 3D object from images taken by several unknown cameras. While the resulting questions in multiview geometry [80] have a long history in computer vision, recent years have seen a confluence with ideas and algorithms from algebraic geometry.
Cited by 2Open access
Gianfranco Doretto · The Research Repository @ WVU (West Virginia University) · 2024
Cited by 0
Song‐Chun Zhu, Ying Wu · 2023
This book presents a unifying mathematical foundation for the study of visual perception and cognition. The proposed foundation integrates four regimes
Cited by 106Open access
V.V. Mamrega · SYNCHROINFO JOURNAL · 2022
This article explores the subject of computer vision systems – a technology that allows vehicles to identify, track, and also classify objects on the roadway. The objectives of the study are to consider the principle of operation of these automated systems, their advantages in comparison with modern road regulation, as well as the problems of implementation and development of these systems. The research was carried out on the basis of the analysis of information from open information resources. The statistics of accidents at work are presented, the high rates of which are due to large volumes of production and an outdated system for monitoring compliance with safety rules and the availability of personal protective equipment for employees. The scheme of interaction of the components of a computer vision system is considered, which will allow monitoring of events occurring in production during operation, monitoring the situation at the enterprise for the occurrence of a potentially dangerous situation for personnel and equipment, and, accordingly, this system will be able to prevent an emergency, as well as avoid personal injury by reacting even to minor deviations from operating parameters. The research was carried out on the basis of the study and analysis of materials published in open information sources.
Cited by 2Open access
M. Mitchell Waldrop · Science · 1984
Cited by 4
2000
Cited by 0
George Stockman, Linda G. Shapiro · 2001
From the Publisher:
Computer Vision presents the necessary theory and techniques for students and practitioners who will work in fields where significant information must be extracted automatically from images. It will be a useful resource automatically from images. It will be a useful resource book for professionals and a core text for both undergraduate and beginning graduate computer vision and imaging courses.
Features
Topics include image databases an virtual and augmented reality in addition to classical topics.
Offers a complete view of two real-world systems that use computer vision.
Contains applications from industry, medicine, land use, multimedia, and computer graphics.
Includes over 250 exercises and programming projects, 48 separately defined algorithms, and 360 figures.
The companion website features include image archive, sample
Cited by 1,712
2021
Cited by 1
Honbin Zha, Xilin Chen, Liang Wang et al. · Communications in computer and information science · 2015
Cited by 5
Dr.AvinashChiranjeev Prof.AkshayaKumarNayak, Anshul Nayak · 2010
Cited by 1
Shukla, Pancham 19XX-, Aluvalu, Rajanikanth 19XX-, Gite, Shilpa et al. · 2023
This book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.
Cited by 8
Tomaso Poggio · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1987
The paper gives an hyperbrief review of computer vision, concen-trating on representative achievements in early vision. The account attempts to stress the underlying unity of its scientific foundations and intellectual achievements.
Cited by 0
Mrs. Arjoo Pandey · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: Computer vision is a field of artificial intelligence that focuses on enabling computers to understand and interpret visual information from images or videos. It involves developing algorithms and techniques to extract meaningful insights, patterns, and knowledge from visual data, mimicking human visual perception capabilities. The abstract of computer vision encompasses a range of fundamental tasks and objectives, including: Image Classification: Classifying images into predefined categories or classes, such as distinguishing between different objects, animals, or scenes. Object Detection and Recognition: Locating and identifying specific objects within an image or video, often through the use of bounding boxes or pixel-level segmentation. Semantic Segmentation: Assigning semantic labels to each pixel in an image to distinguish between different objects or regions.
Cited by 4Open access
Richard Szeliski · Texts in computer science · 2010
Cited by 1,259
Ler, Dylan · Zenodo (CERN European Organization for Nuclear Research) · 2018
Los Angeles, California
Cited by 0Open access
2012
Cited by 0