Detail publikace

AUTOMATED BRAIN TUMOR SEGMENTATION USING NOVEL FEATURE POINT DETECTOR AND SEEDED REGION GROWING

Originální název

AUTOMATED BRAIN TUMOR SEGMENTATION USING NOVEL FEATURE POINT DETECTOR AND SEEDED REGION GROWING

Anglický název

AUTOMATED BRAIN TUMOR SEGMENTATION USING NOVEL FEATURE POINT DETECTOR AND SEEDED REGION GROWING

Jazyk

en

Originální abstrakt

The conventional digital watermarking schemes uses a digital pattern like pseudorandom number sequence, a logo image or a digital signature as the watermark which has limitations in proving ownership of the watermark. This paper proposes a proficient digital watermark generation technique from biometric data which will be unique and can be logically owned to prove ownership. The issue of ownership of digital watermark is addressed in this paper. The biometric pattern of fingerprint is used to generate the digital watermark that has a stamp of ownership. The generated watermark has been studied for uniqueness and identification and has been used to watermark audio signals. Discrete cosine transformation is used for embedding the watermark in the image. Experimental results indicate that the watermark can survive the signal processing and maintain the perceptual properties of the host signal and hence satisfies the design requirements of digital watermarking. The extracted biometric based watermark was uniquely identified under signal processing attacks by matching of the feature points.

Anglický abstrakt

The conventional digital watermarking schemes uses a digital pattern like pseudorandom number sequence, a logo image or a digital signature as the watermark which has limitations in proving ownership of the watermark. This paper proposes a proficient digital watermark generation technique from biometric data which will be unique and can be logically owned to prove ownership. The issue of ownership of digital watermark is addressed in this paper. The biometric pattern of fingerprint is used to generate the digital watermark that has a stamp of ownership. The generated watermark has been studied for uniqueness and identification and has been used to watermark audio signals. Discrete cosine transformation is used for embedding the watermark in the image. Experimental results indicate that the watermark can survive the signal processing and maintain the perceptual properties of the host signal and hence satisfies the design requirements of digital watermarking. The extracted biometric based watermark was uniquely identified under signal processing attacks by matching of the feature points.

BibTex


@inproceedings{BUT100835,
  author="Radim {Burget} and Anushikha {Singh} and K. M. {Soni} and Malay Kishore {Dutta} and Kamil {Říha}",
  title="AUTOMATED BRAIN TUMOR SEGMENTATION USING NOVEL FEATURE POINT DETECTOR AND SEEDED REGION GROWING",
  annote="The  conventional  digital  watermarking  schemes uses a digital pattern like pseudorandom number sequence, a logo image or a digital signature as the watermark which has limitations in proving ownership of the watermark. This paper proposes a proficient digital watermark generation technique from biometric data which will be unique and can be logically owned to prove ownership. The issue of ownership of digital watermark is addressed in this paper. The biometric pattern of fingerprint is used to generate the digital watermark that has a stamp  of  ownership.  The  generated  watermark  has  been studied for uniqueness and identification and has been used to watermark  audio  signals.  Discrete  cosine  transformation  is used  for  embedding  the  watermark  in  the  image.  Experimental results indicate that the watermark can survive the  signal processing and  maintain the perceptual properties of the host signal and hence satisfies the design requirements 
of  digital  watermarking.  The  extracted  biometric  based watermark  was  uniquely  identified  under  signal  processing attacks by matching of the feature points.",
  booktitle="36th International Conference on Telecommunications and Signal processing",
  chapter="100835",
  howpublished="print",
  year="2013",
  month="july",
  pages="648--652",
  type="conference paper"
}