Chuanbo Chen

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.

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

Saliency modeling via outlier detection

Abstract. Based on the fact that human attention is more likely to be attracted by different objects or statistical outliers of a scene, a bottom-up saliency detection model is proposed. Our model regards the saliency patterns of an image as the outliers in a dataset. For an input image, first, each image element is described as a feature vector. The whole image is considered as a dataset and an image element is classified as a saliency pattern if its corresponding feature vector is an outlier among the dataset. Then, a binary label map can be built to indicate the salient and the nonsalient elements in the image. According to the Boolean map theory, we compute multiple binary maps as a set of Boolean maps which indicate the outliers in multilevels. Finally, we linearly fused them into the final saliency map. This saliency model is used to predict the human eye fixation, and has been tested on the most widely used three benchmark datasets and compared with eight state-of-the-art saliency models. In our experiments, we adopt the shuffled the area under curve metric to evaluate the accuracy of our model. The experimental results show that our model outperforms the state-of-the-art models on all three datasets.

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.

A NAM representation method for data compression of binary images

A representation method using the non-symmetry and anti-packing model (NAM) for data compression of binary images is presented. The NAM representation algorithm is compared with the popular linear quadtree and run length encoding algorithms. Theoretical and experimental results show that the algorithm has a higher compression ratio for both lossy and lossless cases of binary images and better reconstructed quality for the lossy case.

A Novel Algorithm Using Non-symmetry and Anti-packing Model with K-Lines for Binary Image Representation

In this paper, we propose a novel algorithm using the NAMK (Non-symmetry and Anti-packing pattern representation Model with K-lines) for the binary image representation. By comparing the algorithm using the NAMK with that using the popular linear quadtree, the theoretical and experimental results presented in this paper show that the former can reduce the data storage much more effectively than the latter and it is a better method to represent the binary image. The algorithm using the NAMK for the binary image representation presented in this paper is valuable for the theoretical research and potential business foregrounds such as decreasing the storage space, increasing the transmission speed, quickening the process procedure, and so forth.

The NAMlet transform

A novel image sparse representation method, named NAMlet transform, is proposed by fully considering the structure of the nature images.The NAMlet transform is based on the asymmetric homogeneous blocks, which could preserve the detail information of the images.The NAMlet transform can remove the restrictions of the size of the images. Image sparse representation methods have been widely applied in many image processing fields, such as computer vision, image de-noising, super resolution, and visual tracking. An efficient sparse representation method can improve the accuracy. However, few of the traditional representation methods consider from the point of the anti-packing problem. Thus, these methods are not only restricted by the size of the image, but also lose a great amount of detail information by using a symmetric blocking method. In this paper, we have proposed an image sparse representation method, called NAMlet Transform. The NAMlets are haar-type wavelets, which are based on the non-symmetric homogeneous blocks obtained by the non-symmetry and anti-packing model. In homogeneous blocks, all the pixels are in the same bit-plane. The NAMlet transform can reduce the lost detail information and remove the restrictions of image size. The experiment results show the strong superiority of the NAMlet transform for image representation in comparison with some state-of-the-art image sparse representation methods.

A Direct Non-Symmetry and Anti-Packing Model for Color Images

The representation methods of the hierarchical data structures have been widely applied in many important fields, such as computer visualization, robotics, computer graphics, image processing, and pattern recognition. Since these methods put too much emphasis upon the symmetry of segmentation, they are not the optimal representation methods. In this paper, we present a direct non-symmetry and anti-packing model (DNAM) for color images. Also, we propose a novel algorithm of the DNAM for color images and analyze the data amount of the proposed algorithm. By taking a rectangle subpattern for example, we implement the proposed algorithm and make a comparison with the algorithm of the popular linear quadtree. The theoretical and experimental results presented in this paper show that our proposed algorithm can reduce the data storage much more effectively than that of the linear quadtree and it is a better method to represent color images.