Contourlet

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

Remote sensing image interpolation via the nonsubsampled contourlet transform

Abstract: A novel nonsubsampled contourlet Bi-linear interpolation algorithm for remote sensing image is proposed, which is based on the estimation of detail nonsubsampled contourlet coefficients at high resolution scales. The method employs Bi-linear interpolation algorithm to anticipate the high frequency component on high resolution from the high frequency component on low resolution. The experiment results demonstrate that the algorithm can get a higher PSNR and keep more image detail information than traditional interpolation algorithms.

Method for detecting image edge by nonsubsampled contourlet transform (NSCT)

Abstract: The invention discloses a method for detecting an image edge by nonsubsampled contourlet transform (NSCT). The method comprises the following steps of: performing NSCT on an input noise-containing image to decompose the image into a low-frequency coefficient and a high-frequency coefficient, performing multi-directional micromotion on a low-frequency coefficient matrix and each directional sub-band coefficient matrix to acquire a plurality of micromotion modulated images, subtracting each micromotion modulated image from a primary sub-band image to acquire a plurality of micromotion changed images, introducing a visual competition mechanism, taking the modulus maximum value to compete to acquire reinforced sub-band edge images, setting a proper threshold to remove noise from each sub-bandedge image, superposing a low-frequency sub-band thick edge image and each directional sub-band edge within the same scale to acquire multi-scale thick edge images, thinning the centre of the thick edge images to acquire a low-frequency sub-band thin edge image and multi-scale thin edge images, and performing OR operation to fuse the low-frequency sub-band thin edge image and the multi-scale thinedge images to acquire the finally fused edge image. The method provided by the invention has the advantages that: noise adaptability is high, and the edge is completely detected and accurately positioned.

Texture Directionality-Based Digital Watermarking in Nonsubsample Shearlet Domain

Abstract: Digital watermarking is a technique used to protect an author’s copyright and has become widespread due to the rapid development of multimedia technologies. In this paper, a novel watermarking algorithm using the nonsubsample shearlet transform is proposed, which combines the directional edge features of an image. A shearlet provides an optimal multiresolution and multidirectional representation of an image based on distributed discontinuities such as edges, which ensures that the embedded watermark does not blur the image. In the proposed algorithm, the nonsubsample shearlet transform is used to decompose the cover image into directional subbands, where different directional subbands represent different directional and textured features. The subband whose texture directionality is strongest is selected to carry the watermark and is thus suitable for the human visual system. Next, singular value decomposition is performed on the selected subband image. Finally, the watermark is embedded in the singular value matrix, which is beneficial for the watermarking robustness and invisibility. In comparison with related watermarking algorithms based on discrete wavelet transforms and nonsubsample contourlet transform domains, experimental results demonstrate that the proposed scheme is highly robust against scaling, cropping, and compression.

A new approach of facial expression recognition based on Contourlet Transform

Abstract: Contourlet Transform provides a flexible multiresolution, local and directional image expansion and a sparse representation for two dimensional piecewise smooth signal resembling images. It overcomes the weakness of wavelet transform in dealing with high dimensional signals. In this paper, the theory of Contourlet Transform is introduced and a new approach of facial expression recognition based on Contourlet Transform is proposed. Locally Linear Embedding is then applied for feature dimensionality reduction, and Vector Support Machine is used to classify the seven expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) of JAFFE database. Both Wavelet Transform and Principal Component Analysis (PCA) algorithm are carried out to compare with the approach proposed. Experimental results show that the proposed approach gets better recognition rate than both Wavelet Transform and Principal Component Analysis. The facial expression recognition based on Contourlet Transform is an effective and feasible algorithm.

Road Sign Detection and Recognition from Video Stream Using HSV, Contourlet Transform and Local Energy Based Shape Histogram

Abstract: This paper describes an efficient approach towards road sign detection and recognition. The proposed system is divided into three sections namely; Colour Segmentation of the road traffic signs using the HSV colour space considering varying lighting conditions, Shape Classification using the Contourlet Transform considering occlusion and rotation of the candidate signs and the Recognition of the road traffic signs using features of a Local Energy based Shape Histogram (LESH). We have provided three experimental results and a detailed analysis to justify that the algorithm described in this paper is robust enough to detect and recognize road signs under varying weather, occlusion, rotation and scaling conditions using video stream.