Image completion using prediction concept via support vector regression

被引：2

作者：

Shih, Cho-Wei ^{[1
]}

Lai, Tsung-Hsuan ^{[1
]}

Chu, Hui-Chuan ^{[2
]}

Chen, Yuh-Min ^{[1
]}

机构：

[1] Natl Cheng Kung Univ, Inst Mfg Informat & Syst, Tainan 70101, Taiwan

[2] Nation Univ Tainan, Dept Special Educ, Tainan, Taiwan

来源：

MACHINE VISION AND APPLICATIONS | 2013年 / 24卷 / 04期

关键词：

Image completion; Support vector regression; Structure prediction; Image inpainting; OBJECT REMOVAL;

D O I：

10.1007/s00138-012-0438-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Image completion is a widely used method for automatically removing objects or repairing the damaged portions of an image. However, information of the original image is often lacking in reconstructed structures; therefore, images with complex structures are difficult to restore. This study proposes a prediction-oriented image completion mechanism (PICM), which applies the prediction concept to image completion using numerous techniques and methods. The experiment results indicate that under normal circumstances, our PICM not only produces good inpainting quality but it is also easy to use.

引用

页码：753 / 768

页数：16

共 50 条

[31] Prediction of subthalamic stimulation efficacy on isolated dystonia via support vector regression
Wu, Yunhao
Li, Yan
Li, Hongxia
Wang, Tao
Huang, Peng
Wu, Yiwen
Sun, Bomin
Pan, Yixin
Li, Dianyou
HELIYON, 2024, 10 (10)
[32] Prediction of Crude Oil Asphaltene Precipitation Using Support Vector Regression
Na'imi, Seyyed Reza
Gholami, Amin
Asoodeh, Mojtaba
JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY, 2014, 35 (04) : 518 - 523
[33] Cyclosporine concentration prediction using clustering and support vector regression methods
Camps-Valls, G
Soria-Olivas, E
Pérez-Ruixo, JJ
Pérez-Cruz, F
Figueiras-Vidal, AR
Artés-Rodríguez, A
ELECTRONICS LETTERS, 2002, 38 (12) : 568 - 570
[34] Prediction of daily maximum temperature using a support vector regression algorithm
Paniagua-Tineo, A.
Salcedo-Sanz, S.
Casanova-Mateo, C.
Ortiz-Garcia, E. G.
Cony, M. A.
Hernandez-Martin, E.
RENEWABLE ENERGY, 2011, 36 (11) : 3054 - 3060
[35] Intelligent Prediction for Time Series Using Smooth Support Vector Regression
Wang, Xiaoh
Wu, Deh
2008 IEEE INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING WORKSHOP PROCEEDINGS, VOLS 1 AND 2, 2008, : 1157 - 1160
[36] Greenhouse Heat Load Prediction Using a Support Vector Regression Model
Coelho, Joao Paulo
Cunha, Jose Boaventura
Oliveira, Paulo de Moura
Pires, Eduardo Solteiro
SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS, 2010, 73 : 111 - +
[37] Cable Tunnel Thermal Rating Prediction using Support Vector Regression
Huang, R.
Pilgrim, J. A.
Lewin, P. L.
Scott, D.
Morrice, D.
2014 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2014,
[38] Prediction of the solar radiation on the Earth using support vector regression technique
Piri, Jamshid
Shamshirband, Shahaboddin
Petkovic, Dalibor
Tong, Chong Wen
Rehman, Muhammad Habib Ur
INFRARED PHYSICS & TECHNOLOGY, 2015, 68 : 179 - 185
[39] Surface Hardness Intelligent Prediction in Milling Using Support Vector Regression
Wu, Deh
ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS, 2008, : 188 - 192
[40] Magnetic properties prediction of NdFeB magnets by using support vector regression
Cheng, Wende
MODERN PHYSICS LETTERS B, 2014, 28 (23):

← 1 2 3 4 5 →