Local Region Statistics Research Articles

Secure Halftone Image Steganography Based on Feature Space and Layer Embedding.

Syndrome-trellis codes (STCs) are commonly used in image steganographic schemes, which aim at minimizing the embedding distortion, but most distortion models cannot capture the mutual interaction of embedding modifications (MIEMs). In this article, a secure halftone image steganographic scheme based on a feature space and layer embedding is proposed. First, a feature space is constructed by a characterization method that is designed based on the statistics of 4 ×4 pixel blocks in halftone images. Upon the feature space, a generalized steganalyzer with good classification ability is proposed, which is used to measure the embedding distortion. As a result, a distortion model based on a hybrid feature space is constructed, which outperforms some state-of-the-art models. Then, as the distortion model is established on the statistics of local regions, a layer embedding strategy is proposed to reduce MIEM. It divides the host image into multiple layers according to their relative positions in 4 ×4 blocks, and the embedding procedure is executed layer by layer. In each layer, any two pixels are located at different 4 ×4 blocks in the original image, and the distortion model makes sure that the calculation of pixel distortions is independent. Between layers, the pixel distortions of the current layer are updated according to the previous embedding modifications, thus reducing the total embedding distortion. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high statistical security when resisting the state-of-the-art steganalysis.

Single frame infrared image small target detection via patch similarity propagation based background estimation

The detection and separation an infrared small target in a complex and noisy scene is still a challenging task for infrared-based monitoring and recognition systems. The complex edges in the real world will increase the number of false alarms for the most existing infrared small target detection algorithms. In this paper, we alleviate this problem by using a new background estimation model, which is based on patch similarity propagation, to reduce the edge interference. First, the propagated filter is improved at the patch level for small target background estimation to preserve more background structure information. Then, the Gaussian similarity parameter between patches is adaptively calculated according to the local region statistics. Then, the small target will be effectively extracted after the target component and background component are separated. The experimental results for typical scenarios demonstrate that the proposed background estimation model can simultaneously preserve more complex edges during the background estimation and produce fewer false alarms in the target image than the other baseline methods.

Local region statistics combining multi-parameter intensity fitting module for medical image segmentation with intensity inhomogeneity and complex composition

It is difficult to segment medical image with intensity inhomogeneity and complex composition, because most region-based modules relay on the intensity distributions. In this paper, we propose a novel method which uses local region statistics and multi-parameter intensity fitting as well. By replacing the original local region statistics with the novel local region statistics after bias field correction, the effect of intensity inhomogeneity can be eliminated. Then we devise a maximum likelihood energy function based on the distribution of each local region. Segmentation and bias field estimation can be jointly obtained by minimizing the proposed energy function. Furthermore, in order to characterize the features of each local region effectively, two parameters are used to fit the average intensity inside and outside of the counter, respectively. This can well handle the medical images with complex composition, such as larger gray difference even in the same region. Comparisons with several representative methods on synthetic and medical images demonstrate the superiority of the proposed method over other representative algorithms.

Shape-constrained level set segmentation for hybrid CPU–GPU computers

Due to its intrinsic advantages such as the ability to handle complex shapes, the level set method (LSM) has been widely applied to image segmentation. Nevertheless, the LSM is computationally expensive. In order to improve the performance of the traditional LSM both in terms of efficiency and effectiveness, we propose a novel algorithm based on the lattice Boltzmann method (LBM). Using local region statistics and prior shape, we design an effective and local speed function for the LSM, from which we deduce a shape prior based body force for LBM solver. An NVIDIA graphics processing units (GPU) is used to accelerate the method. Our introduced algorithm has several advantages. First, it is accurate even if there are some geometric transformations (rotation angle, scaling factor and translation vector) between the object to be segmented and the prior shape. Second, it is local and therefore suitable for massively parallel architectures. Third, the use of local region information allows it to deal with intensity inhomogeneities. Fourth, including shape prior allows the method to handle occlusion and noise. Fourth, the model is fast. Finally the algorithm can be used without shape prior by means of minor modification. Intensive experiments demonstrate, objectively and subjectively, the performance of the introduced framework.

Active Contour Driven by Local Region Statistics and Maximum A Posteriori Probability for Medical Image Segmentation

This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD) with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are simultaneously achieved by minimizing the level set formulation. Experimental results demonstrate desirable performance of the proposed method for different medical images with weak boundaries and noise.

Level set segmentation of medical images based on local region statistics and maximum a posteriori probability.

This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

On Local Region Models and a Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional

The Mumford-Shah functional is a general and quite popular variational model for image segmentation. In particular, it provides the possibility to represent regions by smooth approximations. In this paper, we derive a statistical interpretation of the full (piecewise smooth) Mumford-Shah functional by relating it to recent works on local region statistics. Moreover, we show that this statistical interpretation comes along with several implications. Firstly, one can derive extended versions of the Mumford-Shah functional including more general distribution models. Secondly, it leads to faster implementations. Finally, thanks to the analytical expression of the smooth approximation via Gaussian convolution, the coordinate descent can be replaced by a true gradient descent.