An image encryption scheme based on three-dimensional Brownian motion and chaotic system

At present, many chaos-based image encryption algorithms have proved to be unsafe, few encryption schemes permute the plain images as three-dimensional(3D) bit matrices, and thus bits cannot move to any position, the movement range of bits are limited, and based on them, in this paper we present a novel image encryption algorithm based on 3D Brownian motion and chaotic systems. The architecture of confusion and diffusion is adopted. Firstly, the plain image is converted into a 3D bit matrix and split into sub blocks. Secondly, block confusion based on 3D Brownian motion(BCB3DBM)is proposed to permute the position of the bits within the sub blocks, and the direction of particle movement is generated by logistic-tent system(LTS). Furthermore, block confusion based on position sequence group(BCBPSG) is introduced, a four-order memristive chaotic system is utilized to give random chaotic sequences, and the chaotic sequences are sorted and a position sequence group is chosen based on the plain image, then the sub blocks are confused. The proposed confusion strategy can change the positions of the bits and modify their weights, and effectively improve the statistical performance of the algorithm. Finally, a pixel level confusion is employed to enhance the encryption effect. The initial values and parameters of chaotic systems are produced by the SHA 256 hash function of the plain image. Simulation results and security analyses illustrate that our algorithm has excellent encryption performance in terms of security and speed.

A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

Encoding Methods of Spectral Vector in Hyperspectral Romote Sensing Image

Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quad-value encoding, decimal encoding is proposed. It is proved that quad-value encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection/absorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and discussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quad-value encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.

Image Series Segmentation and Improved MC Algorithm

A semiautomatic segmentation method based on active contour is proposed for computed tomography (CT) image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial contour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes (MC) algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by experimental results.

Active Appearance Model Based Hand Gesture Recognition

This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model (AAM). For this work, the proposed algorithm is composed of constructing AAMs and fitting the models to the interest region. In training stage, according to the manual labeled feature points, the relative AAM is constructed and the corresponding average feature is obtained. In recognition stage, the interesting hand gesture region is firstly segmented by skin and movement cues. Secondly, the models are fitted to the image that includes the hand gesture, and the relative features are extracted. Thirdly, the classification is done by comparing the extracted features and average features. 30 different gestures of Chinese sign language are applied for testing the effectiveness of the method. The Experimental results are given indicating good performance of the algorithm.

Hand Gesture Recognition Based on Improved FRNN

The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the problem of hand gesture recognition, Fuzzy-Rough based nearest neighbour (FRNN) algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory (FRNNC) is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data's fuzziness and the roughness into account, so the compute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN (K nearest neighbor)and Fuzzy-KNN (Fuzzy K nearest neighbor).

A Novel Approach for Unsupervised Segmentation of Homogeneous Regions in Gray-scale Images

An improved approach for JSEG is presented for unsupervised segmentation of homogeneous regions in gray-scale images. Instead of intensity quantization, an automatic classification method based on scale space-based clustering is used for nonparametric clustering of image data set. Then EM algorithm with classification achieved by space-based classification scheme as initial data used to achieve Gaussian mixture modelling of image data set that is utilized for the calculation of soft J value. Original region growing algorithm is then used to segment the image based on the multiscale soft J-images. Experiments show that the new method can overcome the limitations of JSEG successfully.

Relationships between Pitzer's ion interaction coefficients and ionic parameters of electrolyte solutions

Pattern recognition methods were used to treat the experimentally measured data of Pitzer’s coefficients of 107 electrolytes, to find the relationships between the ionic structural parameters of these electrolytes and Pitzer’s coefficients. It is found that these relationships can be approximately expressed as linear equations of four dimensionless numbers, (R_+/R_-), (R_++R_-)/Z_+Z_-, (Z_+/Z_-) and (R_t/R_l), where R_+ and R_- are the cationic and anionic radii respectively; Z_+ and Z_- are the cationic and anionic charge numbers respectively, and (R_t/R_l) denotes the nonsphericity of some non-spherical ions. Besides, it is found that the difference of the nuclear magnetic resonance measured rotational relaxation time of water molecules around cations and anions, |Δτ|, has good correlation with Pitzer’s coefficients. The relationships can be interpreted by the theory of corresponding states of ionic solutions. Based on the relationships, an example of application to some hydrometallurgical process was discussed.

Medical Image Dynamic Collaborative Processing on the Distributed Environment

A new trend in the development of medical image processing systems is to enhance the sharing of medical resources and the collaborative processing of medical specialists. This paper presents an architecture of medical image dynamic collaborative processing on the distributed environment by combining the JAVA, CORBA (Common Object Request and Broker Architecture) and the MAS (Multi-Agents System) collaborative mechanism. The architecture allows medical specialists or applications to share records and communicate with each other on the web by overcoming the shortcut of traditional approach using Common Gateway Interface (CGI) and client/server architecture, and can support the remote heterogeneous systems collaboration. The new approach improves the collaborative processing of medical data and applications and is able to enhance the interoperation among heterogeneous system. Research on the system will help the collaboration and cooperation among medical application systems distributed on the web, thus supply high quality medical service such as diagnosis and therapy to practicing specialists regardless of their actual geographic location.

Fast Image Retrieval of Textile Industrial Accessory Based on Multi-Feature Fusion

A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessory coordination) is used to filter the image database to decouple the image search scope. After the accessory contour information and region information are extracted, the fusion multi-feature of the centroid distance Fourier descriptor and distance distribution histogram is adopted to finish image retrieval accurately. All the features above are invariable under translation, scaling and rotation. Results from the test on the image database including 1,000 accessory images demonstrate that the method is effective and practical with high accuracy and fast speed.

A New Algorithm of Sub-pixels Image Matching

This paper discusses a new algorithm of sub-pixels image matching and analyzes the characteristics of resampling and surface fitting methods. In order to meet the matching demands and to alleviate the computation workload, the following improvement algorithms are used. First, resample the model n-times, putt out (2n-1) sub-models, and calculate the NCs between each sub-model and image. Then choose the maximum between the sub-model and the displacement corresponding to this sub-model which requires the sub-pixel displacement. Finally, put forward a new algorithm that combines the resampling with surface fitting methods. Experimental results show the validity of the algorithm.

Three-dimensions Volume Reconstruction of Grayscale Serial Slice Images

This paper focuses on basic geometric and topological access methods, and computational operations implemented by various data objects. It covers such methods as inter-slices point matching, stream slices, sorting of octree blocks, cell operations and experimental results. At first, this paper discusses in detail the feature points matching of inter-slices. Then it introduces stream slices eigenfields and octree data structures theories. Next, it discusses cell operations and its data structure. Finally, it shows an experimental result. The innovations in the paper is the data structure of slices feature and cell feature, and the feature matching methods owns the properties both quickly and exactly.

An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

旅行时间的短期的预报为聪明的交通系统的成功是必要的。在这份报纸，我们考察预报模型的短期的交通的 state-of-art 并且构画出他们每个模型的基本想法，相关工作，优点和劣势。一改进适应指数的变光滑(IAES ) 模型也被建议克服以前的适应指数的变光滑模型的缺点。然后，比较实验在状况和反常交通调节评估在牌照匹配获得的直接旅行时间数据(每分钟行数) 上预报模型的四个主要分支的性能的正常交通下面被执行。实验的结果证明每个模型似乎有它的自己的力量和软弱。IASE 的预报表演比在更突然预报地平线(预报的和二步) 的另外的模型优异， IASE 能够处理各种交通条件。

Liver fibrosis identification based on ultrasound images captured under varied imaging protocols

Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, sub- jective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and support vector machine are employed to test a group of 99 in-vivo liver fibrosis images from 18 patients, as well as other 273 liver images from 18 normal human volunteers.

Face Recognition Using Kernel Discriminant Analysis

Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.

A novel enhancement method for low illumination images based on microarray camera

It is difficult but important to get clear information from the low illumination images. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the different areas of an image with contrast brightness differences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we generalize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well.Experimental results show that the proposed method can get better image enhancement effect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well.The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.

Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks

The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.

A New Method of Color Edge Detection Based on Local Structure Analysis

Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital image processing, image segmentation is an important section for computers to "understand" images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.