Visibility Graphs for Image Processing (English)

In: IEEE Transactions on Pattern Analysis and Machine Intelligence   ;  42 ,  4  ;  974-987  ;  2020

How to get this document?

Download
Commercial Copyright fee: €28.50 Basic fee: €4.00 Total price: €32.50
Academic Copyright fee: €28.50 Basic fee: €2.00 Total price: €30.50

The family of image visibility graphs (IVG/IHVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such\an operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.

  • Title:
    Visibility Graphs for Image Processing
  • Author / Creator:
  • Published in:
  • Publisher:
    IEEE
  • Year of publication:
    2020
  • Size:
    5918651 byte
  • ISSN:
  • DOI:
  • Type of media:
    Article (Journal)
  • Type of material:
    Electronic Resource
  • Language:
    English
  • Source:
  • Export:
  • ORKG:

Table of contents – Volume 42, Issue 4

Show all volumes and issues

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

765
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
Kuehne, Hilde / Richard, Alexander / Gall, Juergen | 2020
780
Automated Video Face Labelling for Films and TV Material
Parkhi, Omkar M. / Rahtu, Esa / Cao, Qiong / Zisserman, Andrew | 2020
793
Baselines Extraction from Curved Document Images via Slope Fields Recovery
Meng, Gaofeng / Pan, Chunhong / Xiang, Shiming / Wu, Ying | 2020
809
Deep Self-Evolution Clustering
Chang, Jianlong / Meng, Gaofeng / Wang, Lingfeng / Xiang, Shiming / Pan, Chunhong | 2020
824
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Malkov, Yu A. / Yashunin, D. A. | 2020
837
Extracting Geometric Structures in Images with Delaunay Point Processes
Favreau, Jean-Dominique / Lafarge, Florent / Bousseau, Adrien / Auvolat, Alex | 2020
851
Group Maximum Differentiation Competition: Model Comparison with Few Samples
Ma, Kede / Duanmu, Zhengfang / Wang, Zhou / Wu, Qingbo / Liu, Wentao / Yong, Hongwei / Li, Hongliang / Zhang, Lei | 2020
865
Hierarchical Bayesian Inverse Lighting of Portraits with a Virtual Light Stage
Shahlaei, Davoud / Blanz, Volker | 2020
880
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI
Lian, Chunfeng / Liu, Mingxia / Zhang, Jun / Shen, Dinggang | 2020
894
On Detection of Faint Edges in Noisy Images
Ofir, Nati / Galun, Meirav / Alpert, Sharon / Brandt, Achi / Nadler, Boaz / Basri, Ronen | 2020
909
Perspective-Adaptive Convolutions for Scene Parsing
Zhang, Rui / Tang, Sheng / Zhang, Yongdong / Li, Jintao / Yan, Shuicheng | 2020
925
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm
Lu, Canyi / Feng, Jiashi / Chen, Yudong / Liu, Wei / Lin, Zhouchen / Yan, Shuicheng | 2020
939
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning
Gao, Jin / Wang, Qiang / Xing, Junliang / Ling, Haibin / Hu, Weiming / Maybank, Stephen | 2020
956
Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding
Yu, Hong-Xing / Wu, Ancong / Zheng, Wei-Shi | 2020
974
Visibility Graphs for Image Processing
Iacovacci, Jacopo / Lacasa, Lucas | 2020
988
Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets
Clough, James R. / Balfour, Daniel R. / Cruz, Gastao / Marsden, Paul K. / Prieto, Claudia / Reader, Andrew J. / King, Andrew P. | 2020
998
Denoising Autoencoders for Overgeneralization in Neural Networks
Spigler, Giacomo | 2020
1005
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
Veksler, Olga | 2020
1013
Learning Raw Image Reconstruction-Aware Deep Image Compressors
Punnappurath, Abhijith / Brown, Michael S. | 2020
1020
2020 COMPSAC CFP
| 2020
C1
Table of Contents
| 2020
C2
Cover
| 2020