Narrow Results

Multiphase flows occurring in circular curved pipes exhibit important physical phenomena.They are characterized by a large pressure drop and are composed of different phases. In the past, erosion–corrosion was measured through the use of experimental methods. Today numerical simulation models provide a more in depth look into the problem of erosion. Solid particle erosion is of major concern in the industrial engineering sector. In this study, erosion occurring in a (90)-degree elbow has been simulated. The generated two-dimensional data was done through the use of the Commercial software ANSYS Fluent. The primary idea comes from the petrochemicals industry. To overcome this problem, counter measures are proposed in this paper to the piping setup in order to protect pumps from unwanted excessive sand concentrations. Note that the physical properties of the simulated fluid mixture are taken the same as for the real-studied sample.

The spectral gap of the Ising model on the lattice fractal (lattice Sierpinski carpet) with the plus boundary condition is considered. In the absence of an external field and at the supercritical condition, we show a lower bound of the spectral gap of the Ising model.

The absence of phase transitions in one-dimensional Widom–Rowlinson model with long-range interaction is established in the non-symmetric case when different particles have different activity parameters.

In this paper, we consider a one-dimensional long range Widom–Rowlinson model when particle activity parameters are periodic and biased. We show that if the interaction is sufficiently large versus particle activities then the model does not exhibit a phase transition at low temperatures.

This study has developed an object detection and segmentation technique for processing cytoplasm and cell nucleus on ThinPrep-cervical smear images at various magnifications. Both edge detection techniques and region growing for adaptive threshold were applied to a segment cell nucleus, a cytoplasm, and backgrounds using a cervical cell image.

To validate the accuracy and feasibility of the proposed method, we took a variety of cervical cell images to perform a series of experiments. The images were of superficial cells, intermediate cells, and abnormal cells, with each taken from ThinPrep smears at various magnifications. The results indicate that the proposed method can automatically segment cell nucleus and cytoplasm regions while accurately extracting object contours. These results can serve as a reference for examiners of cell pathologies.

In this paper we propose a thinning methodology applicable to character images. It is novel in terms of its ability to adapt to local character shape while constructing the thinned skeleton. Our method does not produce many of the distortions in the character shapes which normally result from the use of existing thinning algorithms. The proposed thinning methodology is based on the medial axis of the character. The skeleton has a width of one pixel. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. We have conducted experiments with printed and handwritten characters in several scripts such as English, Bengali, Hindi, Kannada and Tamil. We obtain less spurious branches compared to other thinning methods. Our method does not use any kind of post processing.

The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is presented using linear projection. The result of the iteration is the contour of the object region. Lastly, addressing the problem that direct projections probably cannot find correct projection points, an effective discrete ray-projection method is presented. Comparisons with other contour deformation algorithms show that the algorithm in the present paper is very robust with respect to the shapes of the object regions. Numerical tests show that time consumption is primarily concentrated on convex hull computation, and the implementation efficiency of the program can satisfy the requirement of interactive operations.

In synthetic aperture sonar (SAS) image, the underwater shipwreck targets are often buried by sediment or badly damaged. Only a part of the characteristics of artificial objects is retained. In this paper, firstly, based on the analysis of the ocean buried background, the Meanshift filtering is used to smooth the original image and convert the color image into binary one. Secondly, the residual contour of artificial target is extracted through the modified Canny edge detection algorithm. Thirdly, the Region Growing method is taken to remove the discrete interference and keep the intact edge of the line. Consideration with the principle of line alignment, the contours of shipwreck targets are gradually connected and aggregated. Finally, a large amount of measured practical SAS images are tested. The experimental results verified that the proposed algorithm can accurately detect the shipwreck target based on residual contour information, meanwhile with an acceptable timeliness for large size sonar image data.

It is an important way for segmentations of CT images to extract contours of objects slice-by-slice. For such a way, an important idea is analogy. That is to say, correct the contour in current slice (current contour) according to the contour in previous slice (previous contour). The key to properly correct the current contour is the ability to recognize occlusions (or say leaking parts) in the current contour. We present a curve-based curve parameterization method to recognize occlusions. The previous contour is evolved to the current contour using line projections. In the process of evolution, the parameterization is realized, which includes two types of information for every point in the evolved contour: the arc length parameter on the previous contour, and distance moved from the initial position to the present position. Using these two parameters, we are able to recognize occlusions in the current contour. Many experiments indicate that the method can recognize all of the occlusions in a given contour. Consequently, the method is robust and can be used as a part of an algorithm to automatically extract contours for CT images.

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

This paper introduces a novel descriptor technique denoted as Contour-Point Signature (CPS) useful to find correspondences of points selected from the outer contours of two arbitrary shapes, and to establish a relationship to map an ordered sequence of contour points from one shape to another. The proposal has proved to be invariant, to translation, scaling and rotation, it also induces a measure which is proved to be non-negative, unique, symmetric and identity-preserving. Experimental tests were performed in shape detection under noise, with image retrieval from a MPEG-7 database and letter recognition. Numerical results show that the proposal is robust for noise perturbation, as well as, having adequate accuracy and hit rate, even with coarse tuning for its parameters. This makes the method attractive to a wide range of applications.

In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fields (CORF) contour tracing algorithm which extracts the contour image using Difference of Gaussians (DoG) and hysteresis thresholding. Moreover, hysteresis thresholding detects the weak edges from the total pixel information and provides more well-balanced smooth features compared to an absolute one. Second CCGI encodes the spatial and temporal information via color mapping to attain the regularized contour images with fewer outliers. Based on the front view of a human walking pattern, the appearance of cross-view variations would reduce drastically with respect to a change of view angles. This proposed work evaluates the performance analysis of CVGR using Deep Convolutional Neural Network (CNN) framework. CCGI is considered a gait feature for comparing and evaluating the robustness of our proposed model. Experiments conducted on CASIA-B database show the comparisons of previous methods with the proposed method and achieved 94.65% accuracy with a better recognition rate.

This paper addresses the problem of stereo vision from a new viewpoint. The problems of both depth determination and scene segmentation are considered as a whole. Based on the facts from psychophysical observations, we propose a multi-layered representation model for stereo vision and show that the problems of both depth determination and image segmentation can be solved simultaneously. The constraints and algorithms for formulating the model are described. A mechanism of interaction between depth perception and surface completion is used to integrate imperfect disparity data and image data to get the correct solution about depth maps of a scene. The experimental results show that the model is a useful as well as an effective one.

The contour argument was introduced by Peierls for two dimensional Ising model. Peierls benefited from the particular symmetries of the Ising model. For non-symmetric models the argument was developed by Pirogov and Sinai. It is very general and rather difficult. Intuitively clear that the Peierls argument does work for any symmetric model. But contours defined in Pirogov–Sinai theory do not work if one wants to use Peierls argument for more general symmetric models. We give a new definition of contour which allows relatively easier proof to the main result of the Pirogov–Sinai theory for symmetric models. Namely, our contours allow us to apply the classical Peierls argument (with contour removal operation).

Obtaining complete information about the shape of an object by looking at it from a single direction is impossible in general. In this paper, we theoretically study obtaining differential geometric information of an object from orthogonal projections in a number of directions. We discuss relations between (1) a space curve and the projected curves from several distinct directions, and (2) a surface and the apparent contours of projections from several distinct directions, in terms of differential geometry and singularity theory. In particular, formulae for recovering certain information on the original curves or surfaces from their projected images are given.

3D printer becomes popular and has great applications in many areas. An efficient placement algorithm in an available space of a 3D printer to print multiple 3D objects is important for the printing time cost. In this paper, a 3D model placement algorithm base on model’s contour is proposed. First, a robust algorithm is proposed to exacting the contour from 3D model. Second, the proposed algorithm adopts the bottom-left strategy combined with dynamic alter rotation to optimize the contour placement result. Moreover a tabu search is used to guide the search to get better results. Some 3D models are used to evaluate the algorithm, the experiment results show that the proposed algorithm can obtain optimal placement results.