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Record Nr. |
UNINA9910437603603321 |
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Titolo |
Semantic analysis and understanding of human behavior in video streaming / / Alberto Amato, Vincenzo Di Lecce, Vincenzo Piuri |
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Pubbl/distr/stampa |
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New York, : Springer, 2013 |
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ISBN |
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1-283-90947-2 |
1-4614-5486-7 |
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Descrizione fisica |
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1 online resource (110 p.) |
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Altri autori (Persone) |
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AmatoAlberto |
LecceVincenzo Di |
PiuriVincenzo |
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Disciplina |
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Soggetti |
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Human behavior |
Psychology - Research - Technological innovations |
Behavioral assessment - Technological innovations |
Behaviorism (Psychology) |
Mass media - Social aspects |
Streaming video - Data processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- Sensors for Human Behavior Analysis -- Related Works -- Sensor Data Interpretation for Symbolic Analysis -- Semantic Analysis -- Evaluation of the proposed methodology -- Conclusions. |
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Sommario/riassunto |
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Semantic Analysis and Understanding of Human Behaviour in Video Streaming investigates the semantic analysis of the human behaviour captured by video streaming, and introduces both theoretical and technological points of view. Video analysis based on the semantic content is in fact still an open issue for the computer vision research community, especially when real-time analysis of complex scenes is concerned. This book explores an innovative, original approach to human behaviour analysis and understanding by using the syntactical symbolic analysis of images and video streaming described by means of strings of symbols. A symbol is associated to each area of the analyzed scene. When a moving object enters an area, the |
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corresponding symbol is appended to the string describing the motion. This approach allows for characterizing the motion of a moving object with a word composed by symbols. By studying and classifying these words we can categorize and understand the various behaviours. The main advantage of this approach lies in the simplicity of the scene and motion descriptions so that the behaviour analysis will have limited computational complexity due to the intrinsic nature both of the representations and the related operations used to manipulate them. Besides, the structure of the representations is well suited for possible parallel processing, thus allowing for speeding up the analysis when appropriate hardware architectures are used. A new methodology for design systems for hierarchical high semantic level analysis of video streaming in narrow domains is also proposed. Guidelines to design your own system are provided in this book. Designed for practitioners, computer scientists and engineers working within the fields of human computer interaction, surveillance, image processing and computer vision, this book can also be used as secondary text book for advanced-level students in computer science and engineering. . |
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Record Nr. |
UNINA9910580215503321 |
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Autore |
Stella Massimo |
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Titolo |
Knowledge Modelling and Learning through Cognitive Networks |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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Descrizione fisica |
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1 online resource (240 p.) |
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Soggetti |
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Information technology industries |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of |
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knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot. |
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