Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.

Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint

Abstract: Palmprint based personal verification is an accepted biometric modality due to its reliability, ease of acquisition and user acceptance. This paper presents a novel palmprint based identification approach which draw on the textural information available on the palmprint by utilizing a combination of Contourlet and Non Subsampled Contourlet Transforms. Center of the palm is computed using the Distance Transform whereas the parameters of best fitting ellipse help determine the alignment of the palmprint. ROI of 256X256 pixels is cropped around the center, and subsequently it is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed using Contourlet and Non Subsampled Contourlet Transforms. The proposed algorithm captures global details in a palmprint as compact fixed length palm codes for Contourlet and NSCT respectively which are further concatenated at feature level for identification using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS Hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results were compiled for individual transforms as well as for their optimized combination at feature level. CT based approach demonstrated the Decidability Index of 2.6212 and Equal Error Rate (EER) of 0.7082% while NSCT based approach depicted Decidability Index of 2.7278 and EER of 0.5082%. The feature level fusion achieved Decidability Index of 2.7956 and EER of 0.3112%.

Contourlet Domain Feature Extraction for Image Content Authentication

Abstract: The achievement of multimedia content authentication by means of digital watermarking, while never easy, is further complicated by the continuing investigation of different ways of generating authentication signatures which survive specific acceptable manipulations. In this paper, we present a novel approach which takes advantage of a multiscale framework and directionality to extract the significant features of an image from its redundant contourlet transform. The introduced redundancy brings simplicity and accuracy for feature calculation. We also describe the applied post-processing steps with the aim of stabilizing those features under acceptable image manipulations, namely lossy JPEG compression and additive noise corruption. Many experiments and comparative studies are performed to show the effectiveness of our technique in generating content signatures based on invariant image features, as well as to demonstrate its superiority when compared with a redundant wavelet approach.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

Abstract: The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhanceme...

Palmprint Identification Using Contourlet Transform

Abstract: Palmprint based personal verification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a novel palmprint based identification approach which uses the textural information available on the palmprint by employing the Contourlet Transform (CT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrums is divided into fine slices, using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. Palmprint matching is then performed using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 7752 palm images, acquired from the standard database of Polytechnic University of Hong Kong. The experimental results demonstrated the feasibility of the proposed system by exhibiting genuine acceptance rate of 88.91%, decidability index of 2.7748 and equal ierror rate of 0.2333%.