In this paper we present a novel technique for wavelet-based corner detection using singular value decomposition (SVD). Here SVD facilitates the selection of global natural scale in discrete wavelet transform. We define natural scale as the level associated with most prominent (dominant) eigenvalue. Eigenvector corresponding to dominant eigenvalue is considered as the natural scale. The corners...

The objective of the paper is to embed a watermark digital image using discrete wavelet transform. The utmost thrust is to satisfy robustness of watermarked image. For this, singular value decomposition (SVD) method is used. SVD slightly change the singular values of cover image but do not affect the visual perception of the cover image. Genetic algorithm is also used to optimize the result. PS...

In this paper we present a novel technique for wavelet-based corner detection using singular value decomposition (SVD). Here SVD facilitates the selection of global natural scale in discrete wavelet transform. We de®ne natural scale as the level associated with most prominent (dominant) eigenvalue. Eigenvector corresponding to dominant eigenvalue is considered as the optimal scale. The corners ...

The main objective of super-resolution (SR) imaging is to reconstruct a high-resolution (HR) image of a scene from one or more low-resolution images of the scene. In resolution enhancement of images, the main loss is on the high frequency components (edges) of the image. This is due to the smoothing caused by interpolation. Hence in order to enhance the quality of the super resolved image, pres...

In this paper, we have implemented singular value decomposition to effectively update the decomposition, including the basis images. We will use two dimensional discrete wavelet transform (2D-DWT) and singular value decomposition (SVD). Hybrid method with SVD and DWT will help us to store the images with less storage requirements and will keep the level of the error that must be acceptable in a...

BACKGROUND Noise reduction techniques play an essential role in EEG signal processing applications. A variety of methods are currently in use, including those based on linear filtering and adaptive noise cancellation, as well as subspace-based methods using singular value decomposition (SVD). SVD offers a robust method to decompose the data matrix into signal and noise subspaces. However, the S...