Multi-directional Gradient Research Articles

A high capacity reversible data hiding through multi-directional gradient prediction, non-linear regression analysis and embedding selection

The technique of reversible data hiding enables an original image to be restored from a stego-image with no loss of host information, and it is known as a reversible data hiding algorithm (RDH). Our goal is to design a method to predict pixels effectively, because the more accurate the prediction is, the more concentrated the histogram is, and it minimizes shifting to avoid distortion. In this paper, we propose a new multi-directional gradient prediction method to generate more accurate prediction results. In embedding stage, according to the embedding capacity of information, we generate the best decision based on non-linear regression analysis, which can differentiate between embedding region and non-embedding region to reduce needless shifting. Finally, we utilize the automatic embedding range decision. With sorting by the amount of regional variance, the easier predicted region is prior for embedding, and the quality of the image is improved after embedding. To evaluate the proposed reversible hiding scheme, we compared other methods on different pictures. Results show that the proposed scheme can embed much more data with less distortion.

Finite Element Analysis of Nylon Based 3D Printed Autonomous Underwater Vehicle Propeller

The recent developments in the additive manufacturing to manufacture complex cost effective profiles is gaining popularity to test the strength of developed products through finite element method (FEM). Finite element analysis (FEA) is a potent tool for mechanical analysis. The combination of 3D printing and FEA is opening new opportunities to go further in the complexity of the product geometry. The autonomous underwater vehicle (AUV) propeller blade has a complex profile with multi-directional gradient and twist, which requires a multi-stage operation to manufacture, including the hubs. The AUV propeller is required to withstand the applied load and generate the required thrust to move AUV at the desired speed. The current study explores the performance of AUV propeller prepared by additive synthesis using Nylon 6 material. The design of the propeller blade was developed using SOLID WORKS and integrated to the CUBPRO (DUO) to obtain the required 3D printing parameters. A comparative investigation is made for Nylon material within the dimensional conformance with the 3D printed propeller blade. The stress-strain analysis of the Nylon AUV propeller is carried with the FEM. The analysis of error and the stress show that the Nylon material meets the performance criteria for AUV propeller.

Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

Multidirectional edge detection based on gradient ghost imaging

Gradient ghost imaging (GGI) is a new imaging method that can directly obtain the edge information of an unknown object from the correlation of light intensity fluctuations. However, this technique faces the problems of poor directionality and continuity. In this paper, we propose a multidirectional gradient ghost imaging scheme (MGGI), where Kirsch operator with 8-directional masks is first used to generate structured illuminations, and then a fast algorithm based on the connection of the 8 masks is applied in the sampling process. Compared with GGI based on Sobel operator, both simulation and experimental results demonstrate that the proposed method has the advantages of imaging quality and edge continuity even in a noisy environment. Furthermore, the MGGI does not increase the imaging time and thus promotes the practical development of ghost imaging.

Performance of sparse-view CT reconstruction with multi-directional gradient operators.

To further reduce the noise and artifacts in the reconstructed image of sparse-view CT, we have modified the traditional total variation (TV) methods, which only calculate the gradient variations in x and y directions, and have proposed 8- and 26-directional (the multi-directional) gradient operators for TV calculation to improve the quality of reconstructed images. Different from traditional TV methods, the proposed 8- and 26-directional gradient operators additionally consider the diagonal directions in TV calculation. The proposed method preserves more information from original tomographic data in the step of gradient transform to obtain better reconstruction image qualities. Our algorithms were tested using two-dimensional Shepp–Logan phantom and three-dimensional clinical CT images. Results were evaluated using the root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). All the experiment results show that the sparse-view CT images reconstructed using the proposed 8- and 26-directional gradient operators are superior to those reconstructed by traditional TV methods. Qualitative and quantitative analyses indicate that the more number of directions that the gradient operator has, the better images can be reconstructed. The 8- and 26-directional gradient operators we proposed have better capability to reduce noise and artifacts than traditional TV methods, and they are applicable to be applied to and combined with existing CT reconstruction algorithms derived from CS theory to produce better image quality in sparse-view reconstruction.

Discriminative sparsity graph embedding based on histogram of rotated princial orientation gradients

The unconstrained face images collected in the real environments include many complicated and changeable interference factors, and sparsity preserving projections (SPP) cannot well obtain the low-dimensional intrinsic structure embedded in the high-dimensional samples, which is important for subsequent sparse representation classifier (SRC). To deal with this problem, in this paper we propose a new method named discriminative sparsity graph embedding based on histogram of rotated principal orientation gradients (DSGE-HRPOG). Firstly, it extracts multi-scale and multi-directional gradient features of unconstrained face images by HRPOG feature descriptor and incorporates them into a discriminative feature dictionary of sparse representation classifier. Secondly, it seeks an optimal subspace of HRPOG feature dictionary in which the atoms in intra-classes are as compact as possible, while the atoms in inter-classes are as separable as possible by adopting the proposed DSGE dimensionality reduction method. Finally, an optimal algorithm is presented in which the low-dimensional projection and the sparse graph construction are iteratively updated, and the accuracy of unconstrained face recognition is further improved. Extensive experimental results on AR, Extended Yale B, LFW and PubFig databases demonstrate the effectiveness of our proposed method.

Three-dimensional Reconstruction of Peripheral Nerve Internal Fascicular Groups.

Peripheral nerves are important pathways for receiving afferent sensory impulses and sending out efferent motor instructions, as carried out by sensory nerve fibers and motor nerve fibers. It has remained a great challenge to functionally reconnect nerve internal fiber bundles (or fascicles) in nerve repair. One possible solution may be to establish a 3D nerve fascicle visualization system. This study described the key technology of 3D peripheral nerve fascicle reconstruction. Firstly, fixed nerve segments were embedded with position lines, cryostat-sectioned continuously, stained and imaged histologically. Position line cross-sections were identified using a trained support vector machine method, and the coordinates of their central pixels were obtained. Then, nerve section images were registered using the bilinear method, and edges of fascicles were extracted using an improved gradient vector flow snake method. Subsequently, fascicle types were identified automatically using the multi-directional gradient and second-order gradient method. Finally, a 3D virtual model of internal fascicles was obtained after section images were processed. This technique was successfully applied for 3D reconstruction for the median nerve of the hand-wrist and cubital fossa regions and the gastrocnemius nerve. This nerve internal fascicle 3D reconstruction technology would be helpful for aiding peripheral nerve repair and virtual surgery.

Experimental Characterization of the Tridimensional Gradient of Microstructure Induced by RCF in the Rolling Band of Rails

The aim of this study is to increase understanding of the relationship between RCF cracking and plastic deformation of the pearlitic microstructure. Following a multi-technical and multi-scale characterization of the worn rail surface, the multidirectional gradient of mechanical properties below the running band is studied with a microstructural point of view. In order to achieve the characterization of the running band and to quantify the influence of the microstructural/mechanical gradient on the global fatigue behavior, this experimental methodology has been based on the combination of several microstructural investigations by optical and scanning electron microscopy (FEG-SEM) in both rolling and transverse directions, microindentation performed on the gradient profile and determination of quantitative data on the pearlitic aggregates through the gradient by Electron backscatter Diffraction (EBSD). Due to the severe plastic deformation, material experiences progressively work hardening and the pearlitic microstructure is modified. In the middle of the running band where the contact patch envelope is more frequent, the pearlitic colonies below the worn rail surface are fractured by accumulation of cyclic and severe shear strain. The resulting lamellar structure of ferrite and cementite is elongated and aligned in the direction of shear strain as the interlamellar spacing decreases. On the contrary, at both sides of the running band where the contact with wheel is rare, the surface is rougher, the pearlitic colonies remain unaffected and the lamellae are not oriented preferentially. The examination of the running band and the microstructure gradient by quantitative analysis will contribute to improve the modeling of rail failure by RCF by enabling to include microstructural data.

Co-Evolutionary Implementation of Edge-Detection Algorithm Based on Multidirectional Gradient Edge Detection Predictor

This paper proposes dynamic threshold control algorithm by Co-evolutionary implementation base on a multidirectional gradient edge detection predictor (MGEDP) template. First, the image cut into four equal parts, these parts will be calculate simultaneously the error values by MGEDP predictor template, in the four sub images using, dynamic threshold evolution; Use these feedback values, to get the error image, and to calculate the threshold values by collaborative computing OTSU threshold, and so on, to classify the edges of error image, The result was convinced that this model could not only accelerated the image processing by Co-evolutionary implementation but get the more details, clearer edges, and better visual sense image.

GPU parallel implementation of edge-detection algorithm based on multidirectional linear gradient adjusted predictor

Concerning the fixed direction and monotony of lossless image compression template of Gradient Adjusted Predictor(GAP),according to the characteristic of the actual edge with the same linear increments,this paper proposed Multidirectional Linear Gradient Adjusted Predictor(MLGAP) template.Firstly the image was cut into four sub-images from the center to the periphery.With the application of Graphics Processor Unit(GPU) parallel technology operation,the predictive value was calculated by MLGAP template in each sub-image,and then error feedback was used to construct prediction error image.The threshold was calculated by OSTU algorithm,and error image edge was classified.At last the edge was thinned by Hilditch algorithm.The simulation results show that using the proposed method can get clear,complete and precise edges.In addition,the GPU parallel technology accelerates the image processing.

Fast image edge detection algorithm based on multidirectional gradient edge detection predictor

By using Gradient Adjusted Predictor(GAP) and Gradient Edge Detection(GED) predictors of lossless image encoding for reference,with an improved one,a new image edge detection algorithm of the dynamic threshold control based on Multidirectional Gradient Edge Detection Predictor(MGEDP) template was proposed.The image was cut into four equal parts,and these parts would be executed simultaneously by MGEDP template in different direction of four opposite ways to calculate the error values by the parallel technology.From these feedback values,the algorithm created error image,calculated the threshold values by Otsu algorithm,classified the edges of error image,thinned the edges,and composed the last edge image.The experimental results show that the algorithm using parallel technology not only decreases the time complexity,but also gets the clearer edges,more details,and better visual image.

Contour Map Registration Using Fourier Descriptors of Gradient Codes

To estimate the position of a subpicture P1 (with unknown angular misaligment) in a contour map P2, a method based on Fourier descriptors of multidirectional gradient codes is suggested. It is assumed that P2 is characterized by a set of magnitudes at equally spaceddiscrete points over a rectangular area; and P1 is described by a set of magnitudes at discrete points on directional axes emanated from a point with magnitude c*. Using the measurements of P1, the multidirectional gradient or successive-gradient codes and their Fourier descriptors are generated. A contour map for P2 having c* as one of the isopleth values is then obtained. For each point on all c*-isopleths, a two-level classifier, utilizing information derived from the Fourier descriptors and the phase correlation function, is used to estimate the possible location of P1 in P2. Simulation has indicated that in many cases the angular misalignment and the position of P1 with respect to P2 can be determined.

Position determination using generalized multidirectional gradient codes

With the assumptions that P1 is a subpicture of P2 (in particular, P2 covers a much larger area than P1), and P1, P2 are contour maps each characterized by a set of magnitudes at regularly spaced discrete points over a rectangular area, a method of determining the possible location of P1 in P2 is described. The method is based on first deriving generalized multidirectional gradient or conjugate-gradient codes for P1; a contour map for P2 having the magnitude (c*) of the center point of P1 as one of the isopleth values is then generated. For each point on all c*-isopleths, a 2-level classifier is used for determining the possible location of P1 in P2.

Position determination using generalized multi-directional gradient codes: K. P. Lam. Division of Electrical Engineering, National Research Council Canada, Ottawa, Canada K1A 0R8. Received February 26, 1984

Position determination using generalized multi-directional gradient codes: K. P. Lam. Division of Electrical Engineering, National Research Council Canada, Ottawa, Canada K1A 0R8. Received February 26, 1984