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Abstract-In this paper, we describe a novel spatial domain method for steganography in RGB images where a secret message is embedded in the blue layer of certain blocks. In this algorithm, each block first chooses a unique t 1 xt 2 matrix of pixels as a "matrix pattern" for each keyboard character, using the bit difference of neighbourhood pixels. Next, a secret message is embedded in the remaining part of the block, those without any role in the "matrix pattern" selection procedure. In this procedure, each pattern sums up with the blue layer of the image. For increasing the security, blocks are chosen randomly using a random generator. The results show that this algorithm is highly resistant against the frequency and spatial domain attacks including RS, Sample pair, X 2 and DCT based attacks. In addition, the proposed algorithm could provide more than 84.26 times of capacity comparing with a competitive method. Moreover, the results indicated that stego-image has almost 1.73 times better transparency than the competitive algorithm.

Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of "Wavelet transform" matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT's power in extracting approximation coefficients of signal, which contain the main behaviour of signal, and abandon the redundant information in hyperspectral image data.

Three dimensional modeling of organs plays a crucial role in the treatment of cancer and radio vascular diseases. The purpose of this work is 3D modeling of breast vessels using only two uncalibrated two-dimensional mammography images in order to have the patient less exposed to X-ray radiation. In the proposed method, we first optimize the internal and external parameters using a nonlinear optimization framework. To this end, we use the data stored in the header of files and key features in the mammography images. Using the optimized parameters, 3D active contours is proposed for 3D modeling of the vessels. Then using the parameters obtained from the previous step, an initial active curve gradually evolves until the energy of active curve is minimized. The surface reconstruction of the vessels is done by employing the methods converting a set of surface points to lattice surface. The proposed method is implied for a set of mammography images. Assuming optimized parameters are achieved, the method can yield promising 3D reconstruction.

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In recent years, active contour models (ACM) have been considered as powerful tools for image segmentation and object tracking in computer vision and image processing applications. This article presents a new tracking method based on parametric active contour models. In the proposed method, a new pressure energy called ' 'texture pressure energy' ' is added to the energy function of the parametric active contour model to detect and track a texture target object in a texture background. In this scheme, the texture features of the contour are calculated by a moment-based method. Then, by comparing these features with texture features of the target object, the contour curve is expanded or contracted to be adapted to the object boundaries. Experimental results show that the proposed method is more efficient and accurate in the tracking of objects compare to the traditional ones, when both object and background are textures in nature.

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