13603559
A new metric based on extended spatial frequency and its application to DWT based fusion algorithms
A new quantitative metric is proposed to objectively evaluate the quality of fused imagery. The measured value of the proposed metric is used as feedback to a fusion algorithm such that the image quality of the fused image can potentially be improved. This new metric, called the ratio of spatial frequency error (rSFe), is derived from the definition of a previous measure termed ''spatial frequency'' (SF) that reflects local intensity variation. In this work, (1) the concept of SF is first extended by adding two diagonal SFs, then, (2) a reference SF (SF"R) is computed from the input images, and finally, (3) the error SF (SF"E) (subtracting the fusion SF from the reference SF), or the ratio of SF error (rSFe=SF"E/SF"R), is used as a fusion quality metric. The rSFe (which can be positive or negative) indicates the direction of fusion error-over-fused (if rSFe>0) or under-fused (if rSFe<0). Thus, the rSFe value can be back propagated to the fusion algorithm (BP fusion), thereby directing further parameter adjustments in order to achieve a better-fused image. The accuracy of the rSFe is verified with other quantitative measurements such as the root mean square error (RMSE) and the image quality index (IQI), as well as with a qualitative perceptual evaluation based on a standard psychophysical paradigm. An advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is implemented with two adjustable parameters-the number of levels of DWT decompositions and the length of the selected wavelet. Results with aDWT were compared to those with a regular DWT and with a Laplacian pyramid. After analyzing several inhomogeneous image groups, experimental results showed that the proposed metric, rSFe, is consistent with RMSE and IQI, and is especially powerful and efficient for realizing the iterative BP fusion in order to achieve a better image quality. Human perceptual assessment was measured and found to strongly support the assertion that the aDWT offers a significant improvement over the DWT and pyramid methods.
11180883
An approach to multimodal biomedical image registration utilizing particle swarm optimization
Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration. A new hybrid particle swarm technique is proposed that incorporates initial user guidance. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity metric were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations than the evolutionary strategies in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.
33641222
Object tracking using the Gabor wavelet transform and the golden section algorithm
The paper presents an object tracking method for object-based video processing which uses a two-dimensional (2D) Gabor wavelet transform (GWT) and a 2D golden section algorithm. An object in the current frame is modeled by local features from a number of the selected feature points, and the global placement of these feature points. The feature points are stochastically selected based on the energy of their GWT coefficients. Points with higher energy have a higher probability of being selected since they are visually more important. The amplitudes of the GWT coefficients of a feature point are then used as the local feature. The global placement of the feature points is determined by a 2D mesh whose feature is the area of the triangles formed by the feature points. In this way, a local feature is represented by a GWT coefficient amplitude vector, and a global feature is represented by a triangle area vector. One advantage of the 2D mesh is that the direction of its triangle area vector is invariant to affine transform. Consequently, the similarity between two local features or two global features can be defined as a function of the angle and the length ratio between two vectors, and the overall similarity between two objects is a weighted sum of the local and global similarities. In order to find the corresponding object in the next frame, the 2D golden section algorithm is employed. Our results show that the method is robust to object deformation and supports object tracking in noisy video sequences.
29740611
Restriction, Terms and Nonlinearity of Boolean Functions
Nonlinear characteristics of (Boolean) functions is one of the important issues both in the design and cryptanalysis of (private key) ciphers or encryption algorithms. This paper studies nonlinear properties of functions from three different but closely related perspectives: maximal odd weighting subspaces, restrictions to cosets, and hypergraphs, all associated with a function. Main contributions of this work include (1) by using a duality property of a function, we have obtained several results that are related to lower bounds on nonlinearity as well as on the number of terms, of the function, (2) we show that the restriction of a function on a coset has a significant impact on cryptographic properties of the function, (3) we identify relationships between the nonlinearity of a function and the distribution of terms in the algebraic normal form of the function, (4) we prove that cycles of odd length in the terms, as well as quadratic terms, in the algebraic normal form of a function play an important role in determining the nonlinearity of the function. We hope that these results contribute to the study of new cryptanalytic attacks on ciphers, and more importantly, of counter-measures against such attacks.
6962958
Characterising user data protection of software components
Proposes a scheme to characterise non-functional security properties that are embedded within the functionality of software components. The security properties may be attached to various aspects of a component, such as resource allocation, user data protection, communication, and so on. In this paper, we are particularly interested in characterising the user data protection of software components. It is often reported that software components usually suffer from security and reliability problems. It is now widely recognised that the characterisation of the security properties of software components is an important issue to boost the confidence and trust in component technology. To address this issue, the characterisation of the security properties of components is the first challenging step. The work proposed in this paper is partially based on the functional requirements defined in the Common Criteria for Information Technology Security Evaluation endorsed by NIST. The applicability of the proposed scheme is demonstrated with a simple example.
14051411
A Framework for the Management of Information Security
Information security is strongly dependent on access control models and cryptographic techniques. These are well established areas of research and practice in the enforcement of technical information security policies but are not capable of supporting development of comprehensive information security within organizations. Therefore, there is a need to study upper level issues to establish organizational models for specifying security enforcement mechanisms and coordinating policies. This paper proposes a model for dealing with high level information security policies. The core is to enforce a continuous refinement of information security requirements aiming at formally deriving technical security policies from high level security objectives. This refinement is carried out by in formation security harmonization functions. Contribution of this paper is on the specification of a notation for expressing information security requirements and on the specification of a mechanism to formulate harmonization functions.
1196113
Feature-based wavelet shrinkage algorithm for image denoising
A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.