The method of normalization to determine invariants

Rothe, Irene; Süße, Herbert; Voss, Klaus

doi:10.1109/34.491618

Cited by 130 publications

(56 citation statements)

References 25 publications

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“…Indeed, similar representations exist in three dimensions and have been reported in the literature (Rothe et al 1996). In fact, all the discussion associated with our two-dimensional representation, and its relevance with respect to the BCF mapping behaviour, remains valid for its three-dimensional counterpart.…”

Section: (B) Shape Normalization and Conformal Elementsmentioning

confidence: 72%

Hydrodynamic object recognition using pressure sensing

2010

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Hydrodynamic sensing is instrumental to fish and some amphibians. It also represents, for underwater vehicles, an alternative way of sensing the fluid environment when visual and acoustic sensing are limited. To assess the effectiveness of hydrodynamic sensing and gain insight into its capabilities and limitations, we investigated the forward and inverse problem of detection and identification, using the hydrodynamic pressure in the neighbourhood, of a stationary obstacle described using a general shape representation. Based on conformal mapping and a general normalization procedure, our obstacle representation accounts for all specific features of progressive perceptual hydrodynamic imaging reported experimentally. Size, location and shape are encoded separately. The shape representation rests upon an asymptotic series which embodies the progressive character of hydrodynamic imaging through pressure sensing. A dynamic filtering method is used to invert noisy nonlinear pressure signals for the shape parameters. The results highlight the dependence of the sensitivity of hydrodynamic sensing not only on the relative distance to the disturbance but also its bearing.

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Section: (B) Shape Normalization and Conformal Elementsmentioning

confidence: 72%

Hydrodynamic object recognition using pressure sensing

2010

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“…The parameter is determined in a way that the resulting image denoted by , achieves 0 [12]. The image , is the normalized image that is used in our watermarking method.…”

Section: Image Normalizationmentioning

confidence: 99%

“…For image normalization, five different affine transforms are applied to the original image f(x,y) by which the geometric distortions of the image are eliminated [11,12]. The normalization procedure for a given image , consists of five steps.…”

Section: Image Normalizationmentioning

confidence: 99%

A Robust Zero-Watermark Copyright Protection Scheme Based on DWT and Image Normalization

Shakeri

Jamzad

2011

Advances in Image and Video Technology

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Abstract. Recently, protecting the copyright of digital media has become an imperative issue due to the growing illegal reproduction and modification of digital media. A large number of digital watermarking algorithms have been proposed to protect the integrity and copyright of images. Traditional watermarking schemes protect image copyright by embedding a watermark in the spatial or frequency domain of an image. However, these methods degrade the quality of the original image in some extend. In recent years, a new approach called zero-watermarking algorithms is introduced. In these methods, the watermark does not require to be embedded into the protected image but is used to generate a verification map which is registered to a trusted authority for further protection. In this paper a robust copyright proving scheme based on discrete wavelet transform is proposed. It uses a normalization procedure to provide robustness against geometric distortions and a cellular automaton for noise robustness. Experimental results on images with different complexity demonstrate that our proposed scheme is robust against common geometric and non geometric attacks including blurring, JPEG compression, noise addition, sharpening, scaling, rotation, and cropping. In addition, our experimental results obtained on images with different complexities showed that our method could outperform the related methods in most cases.

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“…Whether and how these new algorithms and texture features can be developed for invariant texture analysis should also receive more attention in the future work. It is worth pointing out that a great deal of work [50,56,59,80,95,97,105,106,112] has been done on geometric invariants for object recognition from perspective images. Whether and how such geometric invariants can be used for perspective invariant texture analysis is an interesting, open question.…”

Section: Conclusion and Possible Future Workmentioning

confidence: 99%

Brief review of invariant texture analysis methods

Zhang¹,

Tan

2002

Pattern Recognition

378

171

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This paper considers invariant texture analysis. Texture analysis approaches whose performances are not a ected by translation, rotation, a ne, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classiÿed into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented ÿrst. Each approach is reviewed according to its classiÿcation, and its merits and drawbacks are outlined. The focus of possible future work is also suggested. ?

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The method of normalization to determine invariants

Cited by 130 publications

References 25 publications

Hydrodynamic object recognition using pressure sensing

Hydrodynamic object recognition using pressure sensing

A Robust Zero-Watermark Copyright Protection Scheme Based on DWT and Image Normalization

Brief review of invariant texture analysis methods

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