Image reconstruction from an efficient number of moment-based projections

This paper presents a new technique for computed tomography that is based on moment reconstruction. The proposed technique employs the Fourier and Haar coefficients for spectral and spatial moment-based image analyses, respectively. It provides a new approach to the problem of tomographic image reconstruction, where an X-ray image is obtained from a set of line projections. The experimental evaluation includes reconstructions of standard tomographic images in the presence of blur, caused by uniform linear motions. The results lead to the conclusion that the proposed method is more selective, efficient and robust. Another issue considered is the noise associated with normal transmission.     

Hybrid watermarking of medical images for ROI authentication and recovery

Medical image data require strict security, confidentiality and integrity. To achieve these stringent requirements, we propose a hybrid watermarking method which embeds a robust watermark in the region of non-interest (RONI) for achieving security and confidentiality, while integrity control is achieved by inserting a fragile watermark into the region of the interest (ROI). First the information to be modified in ROI is separated and is inserted into RONI, which later is used in recovery of the original ROI. Secondly, to avoid the underflow and overflow, a location map is generated for embedding the watermark block-wise by leaving the suspected blocks. This avoids the preprocessing step of histogram modification. The image visual quality, as well as tamper localization, is evaluated. We use weighted peak signal to noise ratio for measuring image quality of watermarked images. Experimental results show that the proposed method outperforms the existing hybrid watermarking techniques.     

A weighted H1 seminorm regularization method for Fredholm integral equations of the first kind

Many problems in mathematics and engineering lead to Fredholm integral equations of the first kind, e.g. signal and image processing. These kinds of equations are difficult to solve numerically since they are ill-posed. Therefore, regularization is required to obtain a reasonable approximate solution. This paper presents a new regularization method based on a weighted H¹ seminorm. Details of numerical implementation are given. Numerical examples, including one-dimensional and two-dimensional integral equations, are presented to illustrate the efficiency of the proposed approach. Numerical results show that the proposed regularization method can restore edges as well as details.     

Lossless asymmetric single instruction multiple data codec

This report introduces a new lossless asymmetric single instruction multiple data codec designed for extremely efficient decompression of large satellite images. A throughput in excess of 3GB/s allows decompression to proceed in parallel with asynchronous transfers from fast block devices such as disk arrays. This is made possible by a simple and fast single instruction multiple data entropy coder that removes leading null bits. Our main contribution is a new approach for vectorized prediction and encoding. Unlike previous approaches that treat the entropy coder as a black box, we account for its properties in the design of the predictor. The resulting compressed stream is 1.2 to 1.5 times as large as JPEG‐2000, but can be decompressed 100 times as quickly – even faster than copying uncompressed data in memory. Applications include streaming decompression for out of core visualization. To the best of our knowledge, this is the first entirely vectorized algorithm for lossless compression. Copyright © 2011 John Wiley & Sons, Ltd.     

An ontology of slums for image-based classification

Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework.     

基于骨架的网格模型变形

现有骨架驱动变形算法多以单一骨架驱动变形,且骨架预设十分复杂。为此,提出一种基于骨架的网格模型变形算法。结合多分辨率Reeb图方法提取模型的骨架结构,确定各骨架点对应的局部区域,将骨架点插值构造二次Bézier曲线,通过交互式拖动任意骨架点,计算与其相连多骨架点的动态变化情况,实现模型局部区域的自然形变。实验结果表明,该算法能获得较为自然平滑的变形结果。     

针对超混沌系统图像加密算法的选择明文攻击

超混沌系统图像加密算法HYPER_HIE由于采用初等变换和异或运算作为加密措施,因此很难抵抗各种攻击。针对该算法的3个步骤,即密钥生成、像素置乱、图像扩散与混乱,选择全零矩阵、行相同矩阵和列相同矩阵这3种明文矩阵,在未知加密密钥的前提下对该算法进行选择明文攻击。通过一个实例表明,该攻击方法仅以很小的计算代价破译了密文。