Image watermarking based on scale-space representation

2004 ◽  
Author(s):  
Jin S. Seo ◽  
Chang D. Yoo
Keyword(s):  
Image Watermarking ◽  
Scale Space ◽  
Space Representation

GEOMETRIC ATTACK RESISTANT ROBUST IMAGE WATERMARKING SCHEME

2013 ◽  
Vol 09 (01) ◽  
pp. 1350008
Author(s):  
T. SUGI ◽  
DEJEY ◽  
R. S. RAJESH
Keyword(s):  
Color Image ◽  
Image Watermarking ◽  
Color Space ◽  
Scale Space ◽  
Geometric Distortion ◽  
Space Representation ◽  
Local Characteristic ◽  
Moment Invariant ◽  
Geometric Invariance ◽  
Watermarking Scheme

A new watermarking approach based on affine Legendre moment invariants (ALMIs) and local characteristic regions (LCRs) which allows watermark detection and extraction under affine transformation attacks is presented in this paper. It is a non-blind watermarking scheme. Original image color image is converted into HSV color space and divided into four parts. LCR is constructed and a set of affine invariants are derived on LCRs based on Legendre moments for each part. These invariants can be used for estimating the affine transform coefficients on the LCRs. ALMIs are used for watermark embedding, detection and extraction as they provide synchronization and invariant feature which is necessary for a robust watermarking scheme. The proposed scheme shows resistance to geometric distortion, cropping, filtering, compression, and additive noise than the existing ALMI based scheme [Alghoniemy, M. and Tewfik, A. H. [2004] "Geometric invariance in image watermarking," IEEE Trans. Image Process13(2), 145–153] and affine geometric moment invariant (AGMI) based scheme [Seo, J. S. and Yoo, C. D. [2006] "Image watermarking based on invariant regions of scale-space representation," IEEE Trans. Signal Process. 54(4), 1537–1549].

scholarly journals A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation

2014 ◽  
Vol 12 (06) ◽  
pp. 1450044 ◽  
Author(s):  
Jérôme Gilles ◽  
Kathryn Heal
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Scale Space ◽  
Space Representation ◽  
Grayscale Image ◽  
Local Minima ◽  
Clustering Problem ◽  
Space Curves ◽  
Space Approach

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.

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