This project will involve the exploration of a directional extension of multidimensional wavelet transforms, called “contourlets”, to perform pattern recognition. First, the general concept of a directional extension vs. a regular multidimensional wavelet transform will be discussed and explain the reasoning behind the directional extension. Then, a comparison will be done using sample images b...

Content Based Image Retrieval (CBIR) system using Contourlet Transform (CT) based features with high retrieval rate and less computational complexity is proposed in this paper. Unique properties of CT like directionality and anisotropy made it a powerful tool for feature extraction of images in the database. Improved results in terms of computational complexity and retrieval efficiency are obse...

In this paper we study the nonsubsampled contourlet transform. We address the corresponding filter design problem using the McClellan transformation. We show how zeroes can be imposed in the filters so that the iterated structure produces regular basis functions. The proposed design framework yields filters that can be implemented efficiently through a lifting factorization. We apply the constr...

This paper describes a novel approach to finger print matching using a new mathematical transform: the contourlet transform. Contourlet improves traditional wavelet based approaches by incorporating a directional component. It finds a direct discrete-space construction and is therefore computationally efficient. In this paper, 2-D spectrum is divided into fine slices using iterated directional ...

This paper describes a non-blind, imperceptible and highly robust hybrid Medical Image Watermarking (MIW) technique for a range of medical data management issues. The method simultaneously addresses medical information security, content authentication, safe archiving and controlled access retrieval. We propose the use of Contourlet Transform (CLT) followed by the Discrete Cosine Transform (DCT)...

Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance (MR) images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. ...