Bridging the Semantic Gap in Image and Video Analysis / / edited by Halina Kwaśnicka, Lakhmi C. Jain

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
• Edizione: 1st ed. 2018.
• Descrizione fisica: 1 online resource (161 pages) : illustrations
• Disciplina: 006
• Soggetto topico: Computational intelligence; Semantics; Artificial intelligence; Signal processing; Image processing; Speech processing systems; Optical data processing; Computational Intelligence; Artificial Intelligence; Signal, Image and Speech Processing; Image Processing and Computer Vision
• Persona (resp. second.): KwaśnickaHalina; JainLakhmi C
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning – a New Era in Bridging the Semantic Gap.
• Sommario/riassunto: This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.
• Titolo autorizzato: Bridging the Semantic Gap in Image and Video Analysis
• ISBN: 3-319-73891-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910299944203321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Systems Reference Library, . 1868-4394 ; ; 145