Optimal discrete fractional fourier transform descriptors for image retrieval

Ou, Guanwen; Xie, Langxiong; Ling, Bingo Wing‐Kuen; Lun, Daniel Pak-Kong; Cai, Nian; Dai, Qingyun

doi:10.1109/csndsp.2014.6923828

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…An advanced method was put forward by Wang [18], which were more effective and accurate to recognize shape. In recent years, Guanwen Ou et al [19] had come up an optimized algorithm called Discrete Fractional Fourier Transform (DFrFT). Vassilios Solachidis et al [20] proposed a watermark embedding algorithm using Fourier descriptor in 2004, of which watermarks were embedded in polygonal lines to describe image contours.…”

Section: A Fourier Descriptormentioning

confidence: 99%

A High Capacity Watermarking Scheme Based on Fourier descriptor and Sudoku

Zhang¹,

Zheng²

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

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Digital watermark is a type of technology to hide significant information which is used to protect the copyright of digital products. In this paper, a high capacity watermarking method, in which the Fourier descriptor is applied to pre-process the watermark and a Sudoku puzzle is used as a reference matrix in embedding and extraction process, is proposed. The storage of memory can be saved efficiently by applying Fourier descriptor. Furthermore, the security of watermarks can be guaranteed by using Sudoku puzzle. Unlike previous algorithms applying Sudoku puzzle in spatial domain, the proposed algorithm works in transformed domain by applying LWT2. In addition, the proposed algorithm can detect the temper location accurately. The experimental results demonstrate that the proposed watermarking scheme has a pleasurable preference.

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Section: A Fourier Descriptormentioning

confidence: 99%

A High Capacity Watermarking Scheme Based on Fourier descriptor and Sudoku

Zhang¹,

Zheng²

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

View full text Add to dashboard Cite

show abstract

“…According to the mentioned semantic parsing and kernel clustering methodologies, the overall similarity definition is shown in the following formula [26][27][28][29].…”

Section: The Semantic Parsing and Kernel Clustering Based Algorithmmentioning

confidence: 99%