A Novel Synchronisation Approach for Digital Image Watermarking Based on Scale Invariant Feature Point Detector

2006 ◽  
Author(s):  
Natasa Terzija ◽  
Walter Geisselhardt
Keyword(s):  
Digital Image ◽  
Image Watermarking ◽  
Feature Point ◽  
Digital Image Watermarking ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Scale Invariant Feature ◽  
Point Detector

scholarly journals SURF Points Versus SIFT Points in Identification of Medicinal Plants

2019 ◽  
Vol 9 (2) ◽  
pp. 602-607
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Digital Image ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

scholarly journals A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanlun Li ◽  
Aiwu Zhang ◽  
Shaoxing Hu
Keyword(s):  
Phase Correlation ◽  
Feature Point ◽  
Reference Image ◽  
Feature Points ◽  
Scale Invariant ◽  
Matching Problem ◽  
Invariant Feature ◽  
Significant Difference ◽  
Scale Invariant Feature ◽  
Band Image

In the past few years, many multispectral systems which consist of several identical monochrome cameras equipped with different bandpass filters have been developed. However, due to the significant difference in the intensity between different band images, image registration becomes very difficult. Considering the common structural characteristic of the multispectral systems, this paper proposes an effective method for registering different band images. First we use the phase correlation method to calculate the parameters of a coarse-offset relationship between different band images. Then we use the scale invariant feature transform (SIFT) to detect the feature points. For every feature point in a reference image, we can use the coarse-offset parameters to predict the location of its matching point. We only need to compare the feature point in the reference image with the several near feature points from the predicted location instead of the feature points all over the input image. Our experiments show that this method does not only avoid false matches and increase correct matches, but also solve the matching problem between an infrared band image and a visible band image in cases lacking man-made objects.

scholarly journals Image Forgery Recognition using SWT Method

2019 ◽  
Vol 8 (6) ◽  
pp. 293-296
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Feature Transformation ◽  
Scale Invariant ◽  
Image Forgery ◽  
Invariant Feature ◽  
Scale Invariant Feature

Using SWT (Stationary Wavelet Change) & SIFT (Scale Invariant Feature Transformation) we attempted to increase the number of features recognized & matched with digital image for forgery identification. Digital image received preferable match for forged area. We collected the forgery area using SIFT& SURF for identification of forgery. We used DWT (Discrete Wavelet Transform) w.r.t. SIFT & SW to subdue absence of translation invariance..

Geometric Distortions-Invariant Digital Watermarking Using Scale-Invariant Feature Transform and Discrete Orthogonal Image Moments

2010 ◽  
pp. 57-110
Author(s):  
Shiraz Ahmad ◽  
Zhe-Ming Lu
Keyword(s):  
Image Watermarking ◽  
Image Features ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Geometric Attacks ◽  
Geometric Distortions ◽  
Invariant Feature ◽  
Discrete Orthogonal ◽  
Feature Transform ◽  
Scale Invariant Feature

Many proposed digital image watermarking techniques are sensitive to geometric attacks, such as rotation, scaling, translation, or their composites. Geometric distortions, even by slight amounts, can inevitably damage the watermark and/or disable the capability of the watermark detector to reliably perform its function. In this chapter, the authors exploit the invariant image features to design geometric distortions-invariant watermarking system, and present two watermarking techniques. First technique utilizes the bounding box scale-invariant feature transform and discrete orthogonal Hahn moments to embed the watermark into the selective image patches, and the second technique uses only the Hahn moments to globally embed watermark into the whole image. First technique is non-blind and uses the original image during detection. While exhibiting excellent resistance against different geometric distortions, this technique also has fairly good resistance to image cropping like attacks. However, this technique exhibits a reduced data payload. The second technique is designed to be blind and the watermark is blindly extracted using the independent component analysis. For this technique an improved data payload is achieved but with a little compromise on resistance against cropping like attacks. The implementations are supported with thorough discussions and the experimental results prove and demonstrate the effectiveness of the proposed schemes against several kinds of geometric attacks.

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