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Established in 1997, the International Machine Vision and Image Processing (IMVIP) conferences bring together theoreticians, practitioners, industrialists and academics, from the numerous related disciplines involved in the processing and analysis of image-based information. These events provide a platform for communication and exchange between participants whereby cutting edge research and advances within the field can be communicated, discussed and information exchanged. IMVIP events are hosted annually by different universities on the island of Ireland. These proceedings reflect the manuscripts selected for oral presentation at the 14th instalment of the series hosted by the University of Limerick, Ireland in 2010 in association with the Irish Pattern Recognition and Classification Society (IPRCS), a member organisation of the International Association of Patten Recognition (IAPR).
This book constitutes the refereed proceedings of the 14th International Conference on Pattern Recognition and Information Processing, PRIP 2019, held in Minsk, Belarus, in May 2019. The 25 revised full papers were carefully reviewed and selected from 120 submissions. The papers of this volume are organized in topical sections on pattern recognition and image analysis; information processing and applications.
The book provides insights into the Second International Conference on Computer Vision & Image Processing (CVIP-2017) organized by Department of Computer Science and Engineering of Indian Institute of Technology Roorkee. The book presents technological progress and research outcomes in the area of image processing and computer vision. The topics covered in this book are image/video processing and analysis; image/video formation and display; image/video filtering, restoration, enhancement and super-resolution; image/video coding and transmission; image/video storage, retrieval and authentication; image/video quality; transform-based and multi-resolution image/video analysis; biological and perceptual models for image/video processing; machine learning in image/video analysis; probability and uncertainty handling for image/video processing; motion and tracking; segmentation and recognition; shape, structure and stereo.
The two volume set LNCS 6854/6855 constitutes the refereed proceedings of the International Conference on Computer Analysis of Images and Patterns, CAIP 2011, which took place in Seville, Spain, August 29-31, 2011. The 138 papers presented together with 2 invited talks were carefully reviewed and selected from 286 submissions. The papers are organized in topical section on: motion analysis, image and shape models, segmentation and grouping, shape recovery, kernel methods, medical imaging, structural pattern recognition, Biometrics, image and video processing, calibration; and tracking and stereo vision.
Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.
The two volume set LNCS 3686 and LNCS 3687 constitutes the refereed proceedings of the Third International Conference on Advances in Pattern Recognition, ICAPR 2005, held in Bath, UK in August 2005. The papers submitted to ICAPR 2005 were thoroughly reviewed by up to three referees per paper and less than 40% of the submitted papers were accepted. The first volume includes 73 contributions related to Pattern Recognition and Data Mining (which included papers from the tracks of pattern recognition methods, knowledge and learning, and data mining); topics addressed are pattern recognition, data mining, signal processing and OCR/ document analysis. The second volume contains 87 contributions related to Pattern Recognition and Image Analysis (which included papers from the applications track) and deals with security and surveillance, biometrics, image processing and medical imaging. It also contains papers from the Workshop on Pattern Recognition for Crime Prevention.
Since 1994, the European Commission has undertaken various actions to expand the use of Earth observation (EO) from space in the Union and to stimulate value-added services based on the use of Earth observation satellite data.' By supporting research and technological development activities in this area, DG XII responded to the need to increase the cost-effectiveness of space derived environmental information. At the same time, it has contributed to a better exploitation of this unique technology, which is a key source of data for environmental monitoring from local to global scale. MAVIRIC is part of the investment made in the context of the Environ ment and Climate Programme (1994-1998) to strengthen applied techniques, based on a better understanding of the link between the remote sensing signal and the underlying bio- geo-physical processes. Translation of this scientific know-how into practical algorithms or methods is a priority in order to con vert more quickly, effectively and accurately space signals into geographical information. Now the availability of high spatial resolution satellite data is rapidly evolving and the fusion of data from different sensors including radar sensors is progressing well, the question arises whether existing machine vision approaches could be advantageously used by the remote sensing community. Automatic feature/object extraction from remotely sensed images looks very attractive in terms of processing time, standardisation and implementation of operational processing chains, but it remains highly complex when applied to natural scenes.
This two-volume set of LNCS 12188 and 12189 constitutes the refereed proceedings of the 14th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2020, held as part of the 22nd International Conference, HCI International 2020, which took place in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic. The total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. UAHCI 2020 includes a total of 80 regular papers which are organized in topical sections named: Design for All Theory, Methods and Practice; User Interfaces and Interaction Techniques for Universal Access; Web Accessibility; Virtual and Augmented Reality for Universal Access; Robots in Universal Access; Technologies for Autism Spectrum Disorders; Technologies for Deaf Users; Universal Access to Learning and Education; Social Media, Digital Services, Inclusion and Innovation; Intelligent Assistive Environments.
This book constitutes the refereed proceedings of the 15th Scandinavian Conference on Image Analysis, SCIA 2007, held in Aalborg, Denmark in June 2007. It covers computer vision, 2D and 3D reconstruction, classification and segmentation, medical and biological applications, appearance and shape modeling, face detection, tracking and recognition, motion analysis, feature extraction and object recognition.
Annotation Embedded vision systems such as smart cameras have been rapidly developed recently. Vision systems have become smaller and lighter, but their performance has improved. The algorithms in embedded vision systems have their specifications limited by frequency of CPU, memory size, and architecture. The goal of this e-book is to provide a an advanced reference work for engineers, researchers and scholars in the field of robotics, machine vision, and automation and to facilitate the exchange of their ideas, experiences and views on embedded vision system models. The effectiveness for all methods is emphasized in a practical sense for systems presented in this e-book.