M. Radhika Mani

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Textures are one of the important features in computer vision for many applications. In the literature, most of the attention has been focused on the texture features with minimal consideration of the noise models and window selection. To overcome this, in the present paper the features are constructed on preprocessed methods applied on the texture image by(More)
Currently, image steganography methods are becoming popular in the field of data authentication and image processing. This provides efficient way of communication between sender and receiver without any loss in the originality of the cover image. So, the present paper proposes a novel method called as pattern based image steganography. The proposed method(More)
The problem of texture classification arises in several disciplines such as remote sensing, computer vision, and image analysis. The present paper presents a feature extraction method for the classification of textures using GMRF model on linear wavelets. The Seven features are extracted using least square error estimation method on third order markov(More)
Progress in shape based object recognition methods involving either the boundary based or region based methods and their relative popularity is presented. Prevalence of boundary in almost of types and their shapes discriminating features are discussed. Limitations to boundary based methods viz. sensitivity to noise and variations are detailed. The present(More)
An overview of state of art in computerized object recognition techniques regarding digital images is revised. Advantages of shape based techniques are discussed. Importance of ―Fourier Descriptor‖ (FD) for the shape based object representation is described. A survey for the available shape signature assignment methods with Fourier descriptors is presented.(More)
—Diffusion geometry plays a vital role in shape analysis and object recognition. It evokes from propagation of heat on the object surface. This derives the intrinsic or invariant features of the surface. The heat kernel signature (HKS) based on heat diffusion suffers with the problem of scale sensitivity. This is resolved by the scale invariant heat kernel(More)
In this work, we revisit multi-resolution analysis (MRA) methods for object recognition. We find an optimal sparse representation of an image using a second-generation Fast Discrete Curvelet Transform (FDCT) and present a novel curvelet approach based on thin plate splines (TPS). Measurement of local deformation at each FDCT coefficient is detailed.(More)