Feature-Based Image Watermarking Method Using Scale-Invariant Keypoints

2005 ◽  
pp. 312-324 ◽  
Author(s):  
Hae-Yeoun Lee ◽  
Choong-hoon Lee ◽  
Heung-Kyu Lee ◽  
Jeho Nam
Keyword(s):  
Image Watermarking ◽  
Scale Invariant ◽  
Feature Based

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals SUBTYPE COASTLINE DETERMINATION IN URBAN COAST BASED ON MULTISCALE FEATURES: A CASE STUDY IN TIANJIN, CHINA

2018 ◽  
Vol XLII-3 ◽  
pp. 1565-1570
Author(s):  
Y. Song ◽  
Y. Ai ◽  
H. Zhu
Keyword(s):  
Urban Growth ◽  
Misclassification Error ◽  
Ecological Environment ◽  
Landsat Thematic Mapper ◽  
Crucial Importance ◽  
Scale Invariant ◽  
Coastal City ◽  
Feature Based ◽  
The Mean

In urban coast, coastline is a direct factor to reflect human activities. It is of crucial importance to the understanding of urban growth, resource development and ecological environment. Due to complexity and uncertainty in this type of coast, it is difficult to detect accurate coastline position and determine the subtypes of the coastline. In this paper, we present a multiscale feature-based subtype coastline determination (MFBSCD) method to extract coastline and determine the subtypes. In this method, uncertainty-considering coastline detection (UCCD) method is proposed to separate water and land for more accurate coastline position. The MFBSCD method can well integrate scale-invariant features of coastline in geometry and spatial structure to determine coastline in subtype scale, and can make subtypes verify with each other during processing to ensure the accuracy of final results. It was applied to Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images of Tianjin, China, and the accuracy of the extracted coastlines was assessed with the manually delineated coastline. The mean ME (misclassification error) and mean LM (Line Matching) are 0.0012 and 24.54 m respectively. The method provides an inexpensive and automated means of coastline mapping with subtype scale in coastal city sectors with intense human interference, which can be significant for coast resource management and evaluation of urban development.

Rotation and scale invariant image watermarking based on polar harmonic transforms

Optik ◽  
2019 ◽  
Vol 183 ◽  
pp. 401-414 ◽  
Author(s):  
Hongcai Xu ◽  
Xiaobing Kang ◽  
Yajun Chen ◽  
Yilan Wang
Keyword(s):  
Image Watermarking ◽  
Scale Invariant

scholarly journals Screen-Cam Robust Image Watermarking with Feature-Based Synchronization

2020 ◽  
Vol 10 (21) ◽  
pp. 7494
Author(s):  
Weitong Chen ◽  
Na Ren ◽  
Changqing Zhu ◽  
Qifei Zhou ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Image Watermarking ◽  
Gaussian Function ◽  
Geometric Distortion ◽  
Low Pass ◽  
Feature Based ◽  
Robust Image ◽  
Watermarking Scheme ◽  
Watermark Synchronization ◽  
Desynchronization Attacks ◽  
Traditional Image

The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks.

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