Contourlet

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

High-Resolution SAR Change Detection Based on ROI and SPP Net

Abstract: With the resolution increasing, the structure information becomes more and more abundant in Synthetic Aperture Radar (SAR) images. The speckle noise generated by the coherent imaging mechanism, has a great influence on the detection accuracy and detection difficulty accordingly in high-resolution SAR change detection. In this paper, a multivariate change detection framework based on non-subsampled contourlet transform (NSCT), deep belief networks (DBN), fuzzy c-means (FCM) clustering, and global-local spatial pyramid pooling (SPP) net is proposed. NSCT decomposes the image into multiple scales and DBN is used for extracting feature of the decomposed coefficient matrix. FCM converges the similarity matrix of the initial features by DBN into two classes as a pseudo-label for global-local SPP net training data. The global-local SPP net consists of a large-scale region of interest (ROI) SPP net and a small-scale change detection SPP net. The combination of ROI and the SPP net, as well as the fusion between different scales, weakens the interference of the unchanged information and effectively eliminates a large number of redundant information. The experimental results show that our proposed method can effectively remove speckle noise and improve the robustness of high-resolution SAR change detection.

Speckle noise modeling in the contourlet transform domain

Abstract: Speckle noise is an inherent phenomenon in medical ultrasound (US) images. Since it degrades an ultrasound image quality and reduces its diagnostic value, reduction of speckle noise is a very important pre-processing step in ultrasound image processing. For this purpose, the knowledge of the statistics of speckle noise is necessary; especially in the multi-resolution transform domain due to their sparse and efficient representation of images. In this paper a Bessel K-Form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is presented for estimating the parameters of the BKF pdf. The appropriateness of the BKF pdf in modeling the speckle is studied for different noise levels in the contourlet transform domain, in addition the suitability of BKF model is investigated for the case of real US images. It is shown that, in general the BKF can model the statistics of the contourlet transform coefficients corresponding to log-transformed speckle better than the traditional Gaussian and normal inverse Gaussian pdfs.

Curvelet Based Feature Extraction

Abstract: Designing a completely automatic and efficient face recognition system is a grand challenge for biometrics, computer vision and pattern recognition researchers Generally, such a recognition system is able to perform three subtasks: face detection, feature extraction and classification We’ll put our focus on feature extraction, the crucial step prior to classification The key issue here is to construct a representative feature set that can enhance system-performance both in terms of accuracy and speed At the core of machine recognition of human faces is the extraction of proper features Direct use of pixel values as features is not possible due to huge dimensionality of the faces Traditionally, Principal Component Analysis (PCA) is employed to obtain a lower dimensional representation of the data in the standard eigenface based methods [Turk and Pentland 1991] Though this approach is useful, it suffers from high computational load and fails to well-reflect the correlation of facial features The modern trend is to perform multiresolution analysis of images This way, several problems like, deformation of images due to in-plane rotation, illumination variation and expression changes can be handled with less difficulty Multiresolution ideas have been widely used in the field of face recognition The most popular multiresolution analysis tool is the Wavelet Transform In wavelet analysis an image is usually decomposed at different scales and orientations using a wavelet basis vector Thereafter, the component corresponding to maximum variance is subjected to ‘further operation’ Often this ‘further operation’ includes some dimension reduction before feeding the coefficients to classifiers like Support Vector Machine (SVM), Neural Network (NN) and Nearest Neighbor This way, a compact representation of the facial images can be achieved and the effect of variable facial appearances on the classification systems can also be reduced The wide-spread popularity of wavelets has stirred researchers’ interest in multiresolution and harmonic analysis Following the success of wavelets, a series of multiresolution, multidimensional tools, namely contourlet, curvelet, ridgelet have been developed in the past few years In this chapter, we’ll concentrate on Digital Curvelet Transform First, the theory of curvelet transform will be discussed in brief Then we'll talk about the potential of curvelets as a feature descriptor, looking particularly into the problem of image-based face recognition Some experimental results from recent scientific works will be provided for ready reference 3

Heteroscedastic watermark detector in the contourlet domain

Abstract: A new contourlet domain image watermark detector is proposed in the present study. As the performance of the detector completely depends on the accuracy of the statistical model, the contourlet coefficients and statistical properties are studied first. The heavy-tailed distribution and heteroscedasticity of these coefficients are demonstrated in this study. These characteristics cannot be captured simultaneously by the models, which are proposed previously. A two-dimensional generalised autoregressive conditional heteroscedasticity (2D GARCH) model is suggested to overcome this problem. Dependencies of the contourlet coefficients can be explained by the efficient structure provided by this model. A 2D GARCH model-based contourlet domain watermark detector is designed and its performance analysed by computing the receiver operating characteristics. The high accuracy of the proposed detector, its robustness under several types of attacks, and its outperformance compared to alternative watermarking methods are verified by the obtained experimental results.