Computer Analysis of Images and Patterns

Computer Analysis of Images and Patterns

19th International Conference, CAIP 2021, Virtual Event, September 28-30, 2021, Proceedings, Part I

Theocharides, Theo; Vento, Mario; Panayides, Andreas; Lanitis, Andreas; Tsapatsoulis, Nicolas; Pattichis, Constantinos

Springer Nature Switzerland AG

11/2021

500

Mole

Inglês

9783030891275

15 a 20 dias

795

Descrição não disponível.
3D Vision.- Simultaneous Bi-Directional Structured Light Encoding for Practical Uncalibrated Profilometry.- Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans.- Fast Projector-Driven Structured Light Matching in Sub-Pixel Accuracy using Bilinear Interpolation Assumption.- Pyramidal Layered Scene Inference with Image Outpainting for Monocular View Synthesis.- Out of the Box: Embodied Navigation in the Real World.-Toward a novel LSB-based collusion-secure fingerprinting schema for 3D video.- A Combinatorial Coordinate System for the Vertices in the Octagonal C4C8 ( R) Grid.- Bilingual Speech Recognition by Estimating Speaker Geometry from Video Data.- Cost-efficient Color Correction Approach on Uncontrolled Lighting Conditions.- HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching.- MTStereo 2.0: accurate stereo depth estimation via Max-tree matching.- Biomedical Image and Pattern Analysis.- H-OCS: a hybrid optic cup segmentation of retinal images.- Retinal Vessel Segmentation using Blending-based Conditional Generative Adversarial Networks.- U-shaped densely connected Convolutions for Left ventricle segmentation from CMR images.- Deep Learning approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images.- Shape Analysis Approach towards Assessment of Cleft Lip Repair Outcome.- MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction.- Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics.- An Adaptive Semi-Automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network.- A Three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions.- Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings.- Invariant Moments, Textural and Deep features for Diagnostic MR and CT Image Retrieval.- Toward multiwavelet Haar-Schauder entropy for biomedical signal reconstruction.- Machine Learning.- Handling Missing Observations with an RNN-based Prediction-Update Cycle.- eGAN: Unsupervised approach to class imbalance using transfer learning.- Progressive Contextual Excitation for Smart Farming Application.- Fine-Grained Image Classification for Pollen Grain Microscope Images.- Adaptive Style Transfer Using SISR.- Object-Centric Anomaly Detection using Memory Augmentation.- Document Language Classification: Hierarchical Model With Deep Learning Approach.- Parsing Digitized Vietnamese Paper Documents.- EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task.- When Deep Learners Change Their Mind: Learning Dynamics for Active Learning.- Learning to Navigate in the Gaussian Mixture Surface.- ADeep Hybrid Approach For Hate Speech Analysis.- On improving generalization of CNN-based image classification with delineation maps using the CORF push-pull inhibition operator.- Fast Hand Detection in Collaborative Learning Environments.- Assessing the Role of Boundary-level Objectives in Indoor Semantic Segmentation.- Skin lesion classification using convolutional neural networks based on Multi-Features Extraction.- Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing.- Layer-wise Relevance Propagation based Sample Condensation for Kernel Machines.-
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artificial intelligence;computer systems;computer vision;deep learning;digital image;engineering;image analysis;image matching;image processing;image quality;image reconstruction;image segmentation;imaging systems;machine learning;neural networks;object recognition;pattern recognition;signal processing