Computer Vision Applications Third Workshop, WCVA 2018, Held in Conjunction with ICVGIP 2018, Hyderabad, India, December 18, 2018, Revised Selected Papers /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Arora, Chetan (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Mitra, Kaushik (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Communications in Computer and Information Science, 1019
• Θέματα: Optical data processing.; Machine learning.; Pattern recognition.; Computer organization.; Education-Data processing.; Application software.; Image Processing and Computer Vision.; Machine Learning.; Pattern Recognition.; Computer Systems Organization and Communication Networks.; Computers and Education.; Computer Appl. in Social and Behavioral Sciences.

 This book constitutes the refereed proceedings of the third Workshop on Computer Vision Applications, WCVA 2018, held in Conjunction with ICVGIP 2018, in Hyderabad, India, in December 2018. The 10 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers focus on computer vision; industrial applications; medical applications; and social applications. 

 Depth augmented Semantic Segmentation networks for Automated Driving -- Optic Disc Segmentation in Fundus Images Using Anatomical Atlases with Nonrigid Registration -- Bird Species Classification using Transfer Learning with Multistage Training -- A Deep Learning Paradigm for Automated Face Attendance -- Robust Detection of Iris Region Using an Adapted SSD Framework -- Dynamic Image Networks for Human Fall Detection in 360-degree Videos -- Image segmentation and geometric feature based approach for fast video summarization of surveillance videos -- Supervised Hashing for Retrieval of Multimodal Biometric data -- Pose estimation for Distracted Driver Detection using Deep Convolutional Neural Networks -- AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral Image Classification.