Mudar Sarem

A novel algorithm for triangle non-symmetry and anti-packing pattern representation model of gray images

The triangle packing problem has yielded many significant theories and applications such as textile cutting and container stuffing. Although the representation method of the popular linear quadtree has many merits, it puts too much emphasis upon the symmetry of image segmentation. Therefore, it is not the optimal representation method. In this paper, inspired by the concept of the triangle packing problem, we present a Triangle Nonsymmetry and Anti-packing pattern representation Model (TNAM). Also, we propose a novel algorithm for the TNAM of the gray images. By comparing the algorithm for the TNAM with that for the linear quadtree, the theoretical and experimental results show that the former is much more effective than the latter and is a better method to represent the gray images. The algorithm for the TNAM of the gray images is valuable for the theoretical research and potential business foreground.

A Novel Algorithm for Multi-valued Image Representation

Although the representation methods of the quadtree for the multi-valued image have many important applications in different fields, they put too much emphasis upon the symmetry of image segmentation. Therefore, they are not the optimal representation methods. In this paper, inspired by the concept of the packing problem, we present a novel algorithm of binary-bit plane decomposition based on the non-symmetry and anti-packing pattern representation model (BPD-based NAM) for the multi-valued image representation. The theoretical analyses and experimental results presented in this paper show that when the algorithm of BPD-based NAM is compared with that of the popular quadtree, the former can reduce the data storage much more effectively than the latter and it is a better method to represent the multi-valued image. This novel algorithm of BPD-based NAM opens a new research area with respect to the multi-valued image representation and is valuable for the further theoretical research and potential business foreground.

A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm

A high-resolution image is obtained by fusing the information derived from blurred, sub-pixel shifted, and noisy low-resolution observations. In this paper, a novel regularization model based on an Anisotropic Fractional Order Adaptive (AFOA) norm is proposed and then we apply the AFOA model into the Super-Resolution Reconstruction technology. Compared with the existing models, the proposed AFOA model can remove the noise and protect the edges adaptively according to the local features of the images. Meanwhile, the proposed AFOA model can avoid the staircase effect effectively in the smooth region. To obtain the solution to the proposed AFOA model, the Gradient Descent Method is used in this paper. Finally, the experimental results show that the proposed method has much improvement than the existing methods in the respect of the Peak Signal-to-Noise Ratio and the visual quality.

Nonnegative sparse locality preserving hashing

It is a NP-hard problem to optimize the objective function of hash-based similarity search algorithms, such as Spectral Hashing and Self-Taught Hashing. To make the problem solvable, existing methods have relaxed the constraints on hash codes from binary values (discrete) to real values (continuous). Then eigenvalue decomposition was employed to achieve the relaxed real solution. The main problem is that the signs of the relaxed continuous solution are mixed. Such results may deviate severely from the true solution, which has lead to significant semantic loss. Moreover, eigenvalue decomposition confronts singularity problem when the dimension of the data is larger than the sample size. To address these problems, we propose a novel method named Nonnegative Sparse Locality Preserving Hashing (NSLPH). Nonnegative and sparse constraints are imposed for a more accurate solution which preserves semantic information well. Then, we have applied nonnegative quadratic programming and multiplicative updating to solve the optimization problem, which successfully avoids the singularity problem of the eigenvalue decomposition. The extensive experiments presented in this paper demonstrate that the proposed approach outperforms the state-of-the-art algorithms.

A novel gray image representation using overlapping rectangular NAM and extended shading approach

In this paper, inspired by the idea of overlapping rectangular region coding of binary images, we extend the SDS design, which is based on overlapping representation from binary images to gray images based on the non-symmetry and anti-packing model (NAM). A novel gray image representation is proposed by using the overlapping rectangular NAM (RNAM) and the extended Gouraud shading approach, which is called ORNAM representation. Also, we present an ORNAM representation algorithm of gray images. The encoding and the decoding of the proposed algorithm can be performed in O(n logn) time and O(n) time, respectively, where n denotes the number of pixels in a gray image. The wrong decoding problem of the hybrid matrix R for the overlapping RNAM representation of gray images is solved by using the horizontal, vertical, and isolated matrices, i.e., H, V and I, respectively, which are used to identify the vertex types. Also, we put forward four criteria of anti-packing homogeneous blocks. In addition, by redefining a codeword set for the three vertices symbols, we also propose a new coordinate data compression procedure for coding the coordinates of all non-zone elements in the three matrices H, V and I. By taking some idiomatic standard gray images in the field of image processing as typical test objects, and by comparing our proposed ORNAM representation with the conventional S-Tree Coding (STC) representation, the experimental results in this paper show that the former has higher compression ratio and less number of homogeneous blocks than the latter whereas maintaining a satisfactory image quality, and therefore it is a better method to represent gray images.

A Fractal-based Algorithm for the Metamorphic Animation

The metamorphic technology is one of the most important technologies in the computer animation. This paper presents a new fractal-based algorithm for the metamorphic animation. The conceptual roots of fractals can be traced to the attempts to measure the size of objects for which traditional definitions based on Euclidean geometry or calculus failed. Therefore, the objective of this study is to design a fractal-based algorithm and produce a metamorphic animation based on a fractal idea. The main method we adopted is to weight two IFS (Iterated Function System) codes between the start and the target object by an interpolation function. We test our algorithm on the Matlab software. The experimental results demonstrate that the animation generated according to our method is smooth, natural and fluent. In addition, our algorithm has the merits of stability, efficiency and verisimilitude. This study shows that the fractal idea can be effectively applied in the metamorphic animation. The main feature of our algorithm is that it can deal with a fractal object that the conventional algorithm cannot. In application, our algorithm has many practical values that can improve the efficiency of animation production and simultaneously greatly reduce the cost

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model (NAM) is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding (STC) representation and by using the NAM and extended shading (NAMES) approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O(N) time where N is the number of NAM blocks. By taking three idiomatic standard gray images ‘Lena’, ‘F16’, and ‘Peppers’ in the field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.

A Generalized Detail-Preserving Super-Resolution method

The Super-Resolution (SR) technology, which aims to obtain a high-resolution image by using a set of low-resolution images of the same scene, has become one of the hottest research fields. In this paper, we propose a generalized detail-preserving SR method built on a reasonable observation model and a new image prior model. In order to preserve detail information (i.e., sharp edge and texture information), many SR methods have been established by using the traditional observation models and various image prior models. However, the sensor measurement error, the model error, etc., which are not considered in the existing SR methods, also inevitably make some information get lost from the high-resolution image. In this paper, we use a reasonable observation model that describes the degradation process more fully and exactly for SR reconstruction. Also, we propose an adaptive non-local edge-preserving image prior to model the high-resolution image, which imposes a non-local smoothness constraint on the HR image. Thus, the proposed SR method can better preserve the detail information of an image while avoiding artifacts. The generalized detail-preserving SR method has been tested in artificially generated and real data. The experimental results show that the proposed method can reconstruct higher quality images in both quantitative term and perceptual effect. HighlightsThis paper deeply studied on the SR reconstruction performance.A more reasonable observation model was used in this paper.An adaptive non-local edge-preserving image prior model was proposed.The proposed GDP model can not only preserve the edges but also avoid the artifacts.

A collusion attack optimization framework toward spread-spectrum fingerprinting

Abstract Understanding the weaknesses and the limitations of existing digital fingerprinting schemes and designing effective collusion attacks play an important role in the development of digital fingerprinting. In this paper, we propose a collusion attack optimization framework for spread-spectrum (SS) fingerprinting. Our framework is based upon the closed-loop feedback control theory. In the framework, we at first define a measure function to test whether the fingerprint presents in the attacked signal after collusion. Then, an optimization mechanism is introduced to attenuate the fingerprints from the forgery. We evaluate the performance of the proposed framework for three different SS-based embedding methods. The experimental results show that the proposed framework is more effective than the other examined collusion attacks. About three pieces of fingerprinted content are able to interrupt the fingerprinting system which accommodates about 1000 users, if we require the detection probability to be less than 0.9. Meanwhile, a high fidelity of the attacked content is retained.

A new non-symmetry and anti-packing model and its application to image contrast enhancement

In this paper, a non-symmetry and anti-packing image representation model (NAM) has been proposed. NAM is a hierarchical image representation method and it aims to provide faster operations and less storage requirement. By taking a rectangle sub-pattern, for example, we describe the idea of NAM and its encoding algorithm. In addition, an approach for adaptive area histogram equalization for image contrast enhancement based on a NAM image is presented. The contrast enhancement approach is designed to meet the NAM image representation and it can be duplicated with faster operation. The complexity analysis and the experimental results show that the NAM based algorithm for image contrast enhancement is faster and more effective than that based on matrix image.