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Computer Vision and Image Processing: 5th International Conference, Cvip 2020, Prayagraj, India, December 4-6, 2020, Revised Selected Papers, Part III, Singh Satish Kumar, Roy Partha, Raman Balasubramanian


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Автор: Singh Satish Kumar, Roy Partha, Raman Balasubramanian
Название:  Computer Vision and Image Processing: 5th International Conference, Cvip 2020, Prayagraj, India, December 4-6, 2020, Revised Selected Papers, Part III
ISBN: 9789811611025
Издательство: Springer
Классификация:

ISBN-10: 9811611025
Обложка/Формат: Paperback
Страницы: 543
Вес: 0.78 кг.
Дата издания: 07.05.2021
Язык: English
Размер: 23.39 x 15.60 x 2.90 cm
Ссылка на Издательство: Link
Поставляется из: Германии
Описание:

U-Net-Based Approach for Segmentation of Tables from Scanned Pages.- Air Writing: Tracking and Tracing.- Mars Surface Multi-Decadal Change Detection using ISROs Mars Color Camera (MCC) and Viking Orbiter Images.- Deep Over and Under Exposed Region Detection.- DeepHDR-GIF: Capturing Motion in High Dynamic Range Scenes.- Camera Based Parking Slot Detection For Autonomous Parking.- Hard-Mining Loss based Convolutional Neural Network for Face Recognition.- Domain Adaptive Egocentric Person Re-identification.- Scene Text recognition in the wild with motion deblurring using deep networks.- Vision based Autonomous Drone Navigation through enclosed spaces.- Deep Learning-based Smart Parking Management System and Business Model.- Design and Implementation of Motion Envelope for a Moving Object using Kinect for Windows.- Software Auto Trigger Recording for Super Slow Motion Videos using Statistical Change Detection.- Using Class Activations to Investigate Semantic Segmentation.- Few Shots Learning: Caricature to Image Recognition using Improved Relation Network.- Recognition of Adavus in Bharatanatyam Dance.- Digital Borders: Design of an Animal Intrusion Detection System based on Deep Learning.- Automatic On-Road Object Detection in LiDAR-Point Cloud Data using Modified VoxelNet Architecture.- On the Performance of Convolutional Neural Networks under High and Low Frequency Information.- A Lightweight Multi-Label Image Classification Model Based on Inception Module.- Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles.- L2PF - Learning to Prune Faster.- Efficient Ensemble Sparse Convolutional Neural Networks with Dynamic Batch Size.- Inferring Semantic Object Affordances from Videos.- An Unsupervised Approach for Estimating Depth of Outdoor Scenes from Monocular Image.- Age and Gender Prediction using Deep CNNs and Transfer Learning.- One Shot Learning Based Human Tracking in Multiple Surveillance Cameras.- Fast road sign detection and recognition using colour-based thresholding.- Dimensionality Reduction by Consolidated Sparse Representation and Fisher Criterion with Initialization for Recognition.- Deep Learning and Density Based Clustering Methods for Road Traffic Prediction.- Deep learning based Stabbing Action Detection in ATM Kiosks for intelligent Video Surveillance Applications.- An algorithm for semantic vectorization of video scenes.- Applications to Retrieval and Anomaly detection.- Meta-tracking and Dominant Motion Patterns at the Macroscopic Crowd Level.- Digital Video Encryption by Quasigroup on System on Chip (SoC).- Detection based Multipath Correlation Filter for Visual Object Tracking.- Graph-based depth estimation in a monocular image using constrained 3D wireframe models.- AE-CNN based Supervised Image Classification.- Ensemble based Graph Convolutional Network for Semi supervised learning.- Regularized Deep Convolutional Generative Adversarial Network.- A Novel Approach for Video Captioning based on Semantic Cross Embedding and Skip-Connection.- Dual Segmentation Technique for Road Extraction on Unstructured Roads for Autonomous Mobile Robots.- Edge based Robust and Secure Perceptual Hashing Framework.- Real-Time Driver Drowsiness Detection Using GRU with CNN Features.- Detection of Concave Points in Closed Object Boundaries Aiming at Separation of Overlapped Objects.- High Performance Ensembled Convolutional Neural Network for Plant Species Recognition.




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