Buch, Englisch, Band 943, 821 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1241 g
Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1
Buch, Englisch, Band 943, 821 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1241 g
Reihe: Advances in Intelligent Systems and Computing
ISBN: 978-3-030-17794-2
Verlag: Springer International Publishing
This book presents a remarkable collection of chapters covering a wide range of topics in the areas of Computer Vision, both from theoretical and application perspectives. It gathers the proceedings of the Computer Vision Conference (CVC 2019), held in Las Vegas, USA from May 2 to 3, 2019. The conference attracted a total of 371 submissions from pioneering researchers, scientists, industrial engineers, and students all around the world. These submissions underwent a double-blind peer review process, after which 120 (including 7 poster papers) were selected for inclusion in these proceedings.
The book’s goal is to reflect the intellectual breadth and depth of current research on computer vision, from classical to intelligent scope. Accordingly, its respective chapters address state-of-the-art intelligent methods and techniques for solving real-world problems, while also outlining future research directions. Topic areas covered include Machine Vision and Learning, Data Science,Image Processing, Deep Learning, and Computer Vision Applications.Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Deep Learning for Detection of Railway Signs and Signals.- 3D Conceptual Design using Deep Learning.- The Effect of Color Channel Representations on the Transferability of Convolutional Neural Networks.- Weakly Supervised Deep Metric Learning for Template Matching.- Deep Learning vs. Traditional Computer Vision.- Deep Cross-modal Age Estimation.- No-reference Image Denoising Quality Assessment.- Plant Leaf Disease Detection using Adaptive Neuro-Fuzzy Classification.- Fusion of CNN- and COSFIRE-based Features with Application to Gender Recognition from Face Images.- Learning of Shape Models from Exemplars of Biological Objects in Images.- Researcher Profile Ontology for Academic Environment.
