Anti-packing Model Research Articles

Removal of Salt-and-Pepper Noise from Bimodal Images Using a Non-Symmetry and Anti-Packing Model

Removal of Salt-and-Pepper Noise from Bimodal Images Using a Non-Symmetry and Anti-Packing Model

Color disease leaf image segmentation using NAMS superpixel algorithm

BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research.OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease.METHODS:First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced.RESULTS:Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge.CONCLUSIONS:The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area.

An improved ORNAM representation of grey images

An efficient image representation can save space and facilitate the manipulation of the acquired images. In order to further enhance the reconstructed image quality and reduce the number of the homogeneous blocks of the overlapping rectangular non-symmetry and anti-packing model (ORNAM) representation, in this paper, we propose an improved overlapping rectangular non-symmetry and anti-packing model representation (IORNAM) of grey images. Compared with most of the up-to-date and the state-of-the-art hierarchical representation methods, the new IORNAM representation is characterised by two properties: 1) it adopts a ratio parameter of the length and the width of a homogenous block to improve the reconstructed image quality; 2) it uses a new expansion method to anti-pack the subpatterns of grey images to further decrease the number of homogenous blocks, which is important for improving the compression ratios of image representation and reducing the complexities of many image manipulation algorithms. The experimental results presented in this paper demonstrate that: 1) the new IORNAM representation is able to achieve high representation efficiency for grey images; 2) the new IORNAM representation outperforms most of the up-to-date and the state-of-the-art hierarchical representation methods of grey images.

Brain MR image segmentation using NAMS in pseudo-color

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

The brain MRI image sparse representation based on the gradient information and the non-symmetry and anti-packing model

Nowadays, sparse representation has been widely used in Magnetic Resonance Imaging (MRI). The commonly used sparse representation methods are based on symmetrical partition, which have not considered the complex structure of MRI image. In this paper, we proposed a sparse representation method for the brain MRI image, called GNAMlet transform, which is based on the gradient information and the non-symmetry and anti-packing model. The proposed sparse representation method can reduce the lost detail information, improving the reconstruction accuracy. The experiment results show the superiority of the proposed transform for the brain MRI image representation in comparison with some state-of-the-art sparse representation methods.

A Novel Algorithm for Color Image Representation Using Non-symmetry and Anti-Packing Model with Squares

A Novel Algorithm for Color Image Representation Using Non-symmetry and Anti-Packing Model with Squares

The NAMlet transform: A novel image sparse representation method based on non-symmetry and anti-packing model

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.

Using Non-symmetry and Anti-packing Representation Model for Object Detection

In this paper, we present a non-symmetry and anti-packing object pattern representation model (NAM) for object detection. A set of distinctive sub-patterns (object parts) is constructed from a set of sample images of the object class; object pattern are then represented using sub-patterns, together with spatial relations observed among the sub-patterns. Many feature descriptors can be used to describe these sub-patterns. he NAM model codes the global geometry of object category, and the local feature descriptor of sub-patterns deal with the local variation of object. By using Edge Direction Histogram (EDH) features to describe local sub-pattern contour shape within an image, we found that richer shape information is helpful in improving recognition performance. Based on this representation, several learning classifiers are used to detect instances of the object class in a new image. The experimental results on a variety of categories demonstrate that our approach provides successful detection of the object within the image.

Accurate Computation of Geometric Moments Using Non-symmetry and Anti-packing Model for Color Images

Accurate Computation of Geometric Moments Using Non-symmetry and Anti-packing Model for Color Images

A Neighbor-finding Algorithm Involving the Application of SNAM in Binary-image Representation

In view of the low execution efficiency and poor practicability of the existing neighbor-finding method, a fast neighbor-finding algorithm is put forward on the basis of Square Non-symmetry and Anti-packing Model (SNAM) for binary-image. First of all, the improved minor-diagonal scanning way is applied to strengthen SNAM’s adaptability to various textures, thus reducing the total number of nodes after coding; then the storage structures for its sub-patterns are standardized and a grid array is used to recover the spatial-position relationships among sub-patterns, so as to further reduce the complexity of the neighbor-finding algorithm. Experimental result shows that this method’s execution efficiency is significantly higher than that of the classic Linear Quad Tree (LQT)-based neighbor-finding method.

A fast region segmentation algorithm on compressed gray images using Non-symmetry and Anti-packing Model and Extended Shading representation

Image segmentation is one of the fundamental steps in image analysis for object identification. The main goal of image segmentation is to recognize homogeneous regions within an image as distinct and belonging to different objects. Inspired by the idea of the packing problem, in this paper, we propose a fast O(Nα(N))-time algorithm for image segmentation by using Non-symmetry and Anti-packing Model and Extended Shading representation, which was called the NAMES-based algorithm, where N is the number of homogenous blocks and α(N) is the inverse of the Ackerman’s function and it is a very slowly growing function. We first put forward four extended Lemmas and two extended Theorems. Then, we present a new scanning method used to process each NAMES block. Finally, we propose a novel NAMES-based data structure used to merge two regions. With the same experimental conditions and the same time complexity, our proposed NAMES-based algorithm, which extends the popular hierarchical representation model to a new non-hierarchical representation model, has about 86.75% and 89.47% average execution time improvement ratio when compared to the Binary Partition Tree (BPT)-based algorithm and the Quadtree Shading (QS)-based algorithm which has about 55.4% execution time improvement ratio when the QS-based algorithm itself is compared to the previous fastest region segmentation algorithm by Fiorio and Gustedt whose O(N2)-time algorithm is run on the original N×N gray image. Further, the NAMES can improve the memory-saving by 28.85% (5.04%) and simultaneously reduce the number of the homogeneous blocks by 49.05% (36.04%) more than the QS (the BPT) whereas maintaining the satisfactory image quality. Therefore, by comparing our NAMES-based algorithm with the QS-based algorithm and the BPT-based algorithm, the experimental results presented in this paper show that the former has not only higher compression ratio and less number of homogenous blocks than the latter whereas maintaining the satisfactory image quality, but also can significantly improve the execution speed for image segmentation, and therefore it is a much more effective algorithm for image segmentation.

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.

A New NAM-based Algorithm for Computing Hu Moments of Binary Images

Hu moments are very important in the fields of image analysis and object representation. In this paper, inspired by the idea of computing moments on a block representation, we put forward a new algorithm for computing the Hu moments on the Non-symmetry and Anti-packing Model (NAM) representation of binary images, which takes O(N ) time where N is the number of NAM blocks. By taking four idiomatic standard binary images ‘Peppers’, ‘Goldhill’, ‘Lena’, and ‘F16’ in the field of image processing as typical test objects, and by comparing our proposed NAM-based algorithm with the traditional and the popular Bintree-based algorithm for computing the Hu moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of our proposed NAM-based algorithm over the Bintree-based algorithm and the traditional algorithm are 92.76% and 99.70%, respectively. Therefore, in the case of computing lower order moments of binary images, our proposed algorithm is much faster than the Bintree-based and the traditional 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 New Image Representation Method Using Nonoverlapping Non-symmetry and Anti-packing Model for Medical Images

Inspired by the idea of the S-Tree Coding (STC) and by extending the popular Gouraud shading approach, in this paper, we propose a new image representation method using the Nonoverlapping Non-symmetry and Anti-packing Model (NNAM) for medical images, which is called the NNAM method. Also, a raster-first strategy for searching a rectangle subpattern in the NNAM method is put forward. During the procedure of scanning a rectangle subpptern, the value of the horizontal ordinate is firstly increased, and then the value of the vertical ordinate is increased until the rectangle subpattern finally becomes a non-homogeneous block. By comparing our proposed NNAM method with the conventional STC method, the experimental results presented in this paper show that the former can significantly reduce the lower bit rate and the number of homogenous blocks than the latter whereas remaining the satisfactory image quality. Also, our proposed NNAM method for medical images, as envisaged in this paper, shows a very strong promise and it has good potential in business applications dealing with image processing, such as reducing storage room, increasing processing speed, and improving pattern match efficiency.

A Novel Gray Image Representation Method Based on NAM Using Nonoverlapping Square Subpatterns

In this paper, we propose a novel gray image representation method based on the non-symmetry and anti-packing model (NAM) by using the nonoverlapping square subpatterns, which is called the square NAM for gray images (SNAMG) representation method. Also, a SNAMG representation algorithm is put forward and the storage structures, the total data amount, and the time complexity of the proposed algorithm are analyzed in detail. By taking some standard gray images, such as ‘F16’ and ‘Peppers’, as the typical test objects, and comparing the proposed algorithm with those of the triangle NAM for gray images (TNAMG) and the classic linear quadtree (LQT), the theoretical and experimental results show that the former is obviously superior to the latter with respect to the numbers of subpatterns (nodes) and the data storage, and therefore it is a better method to represent the gray image pattern.

A Square NAM Representation Method for Binary Images

The Non-symmetry and Anti-packing Model (NAM) is an effective pattern representation model. In this paper, a new NAM image representation method by using the nonoverlapping square subpatterns, which is called the square NAM (SNAM) representation, is proposed. The idea of the SNAM representation is described. The square subpattern has its own significant characteristics when it is compared with other subpatterns such as the triangle and the rectangle, etc. Unlike the general triangle subpattern which needs to record three vertices of the triangle and unlike the rectangle subpattern which needs to record the coordinates of its starting point, length and width, the square subpattern needs to record only its starting point and the side. Therefore, as far as a single record of the square subpattern is concerned, it can save storage space more effectively. The theoretical and experimental results presented in this paper prove the efficiency and the effectiveness of the proposed SNAM representation method for binary images.

An Improved STC Image Compression Method

In this paper, inspired by the idea of the S-Tree Coding (STC), we propose an improved STC image compression method by using the Nonoverlapping Non-symmetry and Anti-packing Model (NNAM) and the extended shading approach, which is called the NNAM Coding (NNAMC) method. In our method, the encoding can be performed in O(n log n) time and the decoding can be performed in O(n) time, where n denotes the number of pixels in a gray image. By comparing our proposed NNAMC method with the conventional STC method, the experimental results presented in this paper show that the former can significantly reduce the lower bit rate and the number of homogenous blocks than the latter whereas remaining the satisfactory image quality.

An Improved RNAMC Image Representation

In this paper, by controlling the ratio of the length and the width of a homogenous block, we proposed an improved algorithm for the gray image representation by using the Rectangular Non-symmetry and Anti-packing Model Coding (RNAMC) and extended shading approach, which is called the IRNAMC image representation method. Also, we present an IRNAMC representation algorithm of gray images. By comparing our proposed IRNAMC method with the conventional S-Tree Coding (STC) method, the experimental results presented in this paper show that the former can significantly reduce the lower bit rate and the number of homogenous blocks than the latter whereas remaining the satisfactory image quality. Also, the experimental results show that by controlling the ratio of the length and the width, we can improve the reconstructed image quality of the RNAMC method.

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.