Big Visual Data Analysis [recurso electrónico] : Scene Classification and Geometric Labeling / by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo.
Tipo de material: TextoSeries SpringerBriefs in Electrical and Computer EngineeringEditor: Singapore : Springer Singapore : Imprint: Springer, 2016Edición: 1st ed. 2016Descripción: X, 122 p. 94 illus., 12 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789811006319Tema(s): Engineering | Image processing | Mathematics | Visualization | Engineering | Signal, Image and Speech Processing | Image Processing and Computer Vision | VisualizationFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TK5102.9TA1637-1638TK7882.S65Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Introduction -- Scene Understanding Datasets -- Indoor/Outdoor classi?cation with Multiple Experts -- Outdoor Scene Classi?cation Using Labeled Segments -- Global-Attributes Assisted Outdoor Scene Geometric Labeling -- Conclusion and Future Work.
This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.