Nonsubsampled Contourlet Transform Subbands Research Articles

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

A novel multisource pig-body multifeature fusion method based on Gabor features

The multi-source image fusion has been a hot topic during recent years because of its higher segmentation accuracy rate. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect the pig-body feature, a novel infrared and visible image fusion method for pig-body segmentation and temperature detection is proposed in non-subsampled contourlet transform (NSCT) domain, named as NSCT-GF. Firstly, the visible and infrared images were decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information, the Gabor energy map was extracted by Gabor filter with even-symmetry, and the low-frequency coefficients were fused by the maximum of Ordinal encoding. Then, to preserve the more coarse-scale and edge detail information, Gabor filter with odd-symmetry was employed to fuse high-frequency NSCT sub-bands and the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Next, the pig-body shape was obtained by Ostu automatic threshold segmentation and optimized by morphological processing. Finally, the pig-body temperature was extracted based on shape segmentation. Experimental results showed that the proposed segmentation method was capable of achieving 1.84–3.89% higher average segmentation accuracy rate than the prevailing conventional methods and also improved efficiency in terms of time consumption. It lays a foundation for accurately measuring the temperature of pig-body.

NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

Depth-image-based rendering (DIBR), where arbitrary views are synthesized from a center image and depth image, has received much attention in the three-dimensional (3D) research field. With advances in depth-acquisition techniques and the proliferation of 3D glasses and 3D display devices, there is a growing demand for schemes to protect the copyrights of DIBR 3D images. Digital watermarking is a typical protection technology and designing a watermarking method for DIBR 3D images is a challenging task because the synchronization of watermarks can easily be broken in the process of generating synthetic images. To address this issue, we propose a non-subsampled contourlet transform (NSCT)-based blind watermarking for DIBR 3D images. To ensure the proposed method has properties of robustness against the DIBR process, we conduct an analysis of robustness for NSCT subbands against DIBR attacks. Based on the analysis results, we select subbands that are robust against DIBR attacks and embed watermark in the low coefficients of NSCT subbands using quantization-based embedding. While ensuring robustness, the proposed method also improves the imperceptibility of watermarks by adjusting their embedding strength according to computed perceptual masking values. Through experiments, we show that the proposed method is robust against both desynchronization attacks of the DIBR process and common attacks including signal processing operations and geometric distortions. The high imperceptibility of our method is also verified by several evaluation metrics in a subjective and objective manner.

Zernike moments-based fingerprint recognition using weighted-support vector machine

Moments play an important role in image analysis and invariant pattern recognition. There are two types: orthogonal moments and non-orthogonal moments. Orthogonal moments perform better than non-orthogonal moments, they have properties such as robustness to image noise and geometrical invariant properties such as scale, rotation and translation. In this paper, an improvement in fingerprint recognition is done by using the Non-subsampled contourlet transform (NSCT) and the Zernike moments (ZMs). NSCT decomposes the fingerprint images into NSCT subbands. Thereafter, ZMs are used to evaluate the features of fingerprint images. Thereafter, feature selection technique is applied to select potential features from the obtained features using coefficient of determination. Thereafter, a well-known weighted-support vector machine is also used to train and test the evaluated features. Extensive experiments reveal that the proposed technique achieves significant improvement over the existing techniques in terms of accuracy, sensitivity, specificity, [Formula: see text]-measure, kappa statistics and execution time.

Interval type-2 fuzzy set and human vision based multi-scale geometric analysis for text-graphics segmentation

This paper presents a novel method for texture-based text-graphic segmentation in a text embedded image. In the method, features are computed applying Multi-scale Geometric Analysis(MGA). The MGA of the image is done by Nonsubsampled contourlet transform(NSCT). The NSCT sub-bands help to generate the features which represent textures of the text portions and graphics portions of the image. In a segmentation process, the uncertainties arise mainly for two reasons: one is the ambiguity in gray level and other is the spatial ambiguity. Here the uncertainties are managed by interval type2 fuzzy set (IT2FS). The human vision model called human psychovisual phenomenon (HVS) is incorporated in the process for generating the interval type-2 fuzzy membership functions (IT2FMF). The efficiency of the proposed scheme is measured on the benchmark dataset. The robustness and performance bound of the proposed technique under noise corruption are measured statistically using modified Cramer-Rao bound. We found that effectiveness of the features by NSCT in combination with the IT2FS are quite promising in comparison to the state-of-the-arts methods.

Speech and music classification using spectrogram based statistical descriptors and extreme learning machine

This article proposes a novel feature extraction approach for speech/music classification based on generalized Gaussian distribution descriptors extracted from IIR-CQT spectrogram representation. IIR-CQT spectrogram visual representation provides superior temporal resolution at high frequencies and better spectral resolution for low frequencies compared to the conventional short-time Fourier transform analysis which provides uniform frequency resolution. Multi-level decomposition of the spectrogram image is then performed using the Nonsubsampled Contourlet Transform (NSCT) which a fully shift-invariant, multi-scale, and multi-direction expansion that can preserve the edges of the textural pattern of speech and music. The generalized Gaussian distribution (GGD) parameters are produced using maximum likelihood estimation (MLE) from the NSCT subbands to create the image feature descriptor. Chaos crow search algorithm is employed to chose the most relevant feature sub-set and to discard redundant features and finally the extreme learning machine classifier categorizes input audio segment into speech/music. The experimental results show that the proposed feature descriptor is effective and performs better compared to the existing approaches in the speech/music classification. In addition, mismatched training and testing results are also presented.

Contourlet based seismic reflection data non-local noise suppression

Abstract In this paper, we propose a non-local, transform domain noise suppression framework to improve the quality of seismic reflection data. The original non-local means (NLM) algorithm measures similarities in the data domain and we generalize it in the nonsubsampled contourlet transform (NSCT) domain. NSCT gives a multiscale, multiresolution and anisotropy representation of the noisy input. The redundancy information in NSCT subbands can be utilized to enhance the structures in the original seismic data. Like the wavelet transform, NSCT coefficients in each subband follow the generalized Gaussian distribution and the parameters can be estimated using appropriate techniques. These parameters are used to construct our proposed NSCT domain filtering algorithm. Applications for synthetic and real seismic data of the proposed algorithm demonstrate its effectiveness on seismic data random noise suppression.

A Separation Method of 3D Engineering Surface with Correlation Analysis of NSCT Sub-Bands

The 3D engineering surfaces are comprised of a range of spatial frequency components, such as form, waviness and roughness. Filtering techniques are commonly adopted to separate the different components. To overcoming the shortcomings of traditional filtering method, a new separation method is proposed with the correlation analysis of sub-bands in nonsubsampled contourlet transform (NSCT) domain. The 3D engineering surface topography is decomposed into different sub-bands by NSCT, and the correlation coefficients of NSCT sub-bands with its parent and children sub-bands are calculated by Pearson correlation method. Then the roughness, waviness and form of 3D real surface topography are restructured respectively by the inverse NSCT based on the NSCT sub-bands which belong to different components. Finally, a group of 3D engineering surfaces are separated into different components, and the result shown that the proposed method can separate 3D engineering surface effectively.