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Bala, Indu
- Source Camera Identification Using Interninsic Fingerprints
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International Journal of Innovative Research and Development, Vol 3, No 3 (2014), Pagination:Abstract
The identification of an image acquisition device becomes a very important in digital image forensics. In this paper main focus is on identification of digital camera and provides a new approach of identification. As digital camera uses many components in image generation process such as lens, sensors, CFA (colour filter array) e.t.c. Each component has its unique characteristics that can be known as fingerprints. By extracting that fingerprint identification of digital camera can be done. In this paper identification is done on the basis of sensor noise and CFA unique characteristics. From sensor photo-response non-uniformity (PRNU) is considered because it was introduced by Luk´aˇs et al. [2] for identification of digital camera. So in this work PRNU (Photo Response Non Uniformity) and 12 features from CFA are used. Classification is done by using SVM classifier (Support Vector Machine). Experimental analysis shows that the proposed method has good potential for identification of digital camera.
Keywords
PRNU (Photo Response Non Uniformity), CFA (color filter array), SVM (Support Vector Machine), Digital Image ForensicsFull Text
- Brain Tumor Detection Using Hard and Soft Computing Techniques
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International Journal of Innovative Research and Development, Vol 3, No 3 (2014), Pagination:Abstract
Medical image segmentation has great significance in the research in the research field of image segmentation. It is the one way to take out important information from medical images. There are many soft computing and hard computing techniques are there which are used for segment the interested area in medical images. Both has its own advantages and disadvantages. In this paper we provide a new approach to medical image segmentation. we use three approaches with the combination of each other. That approaches are K-mean, Fuzzy C-mean and genetic algorithm. So we divide this paper in three phases. In first phase we apply the K-mean algorithm on the image which segment the interested are but this algorithm not gave the satisfactory result. So on the result of this algorithm we apply FCM after this optimization is done with the help of GA. The experimental result is calculated on the basis of classification accuracy. The result is 90.3%.
Keywords
Medical Image Segmentation, K-means, FCM, MRI, GAFull Text