Background Skeleton Research Articles

3D CFD-DEM study on fine particle migration in packed proppant layers

Fine particle migration is a crucial issue in hydraulic fracturing-based production from unconventional reservoirs and can significantly affect the effective conductivity of the packed proppant layers. In this study, to investigate the conveying behavior of fine particles, a Eulerian-Lagrangian method, namely the computational fluid dynamics–discrete element method (CFD-DEM), is adopted for 3D simulations. Two types of particles are involved in this process: larger proppant particles and fine particles. Proppant particles form a background skeleton, and a two-phase flow of fine particles and fluid is constrained in the connected pore networks. We adopt a unified CFD-DEM framework to resolve the non-slip boundary condition on the proppant particle surface and track fine particles using a four-way coupling strategy. In particular, a forcing term based on the fictitious domain method is embedded in a regular volume-averaged Navier-Stokes equation to solve the fluid motion. A perfectly packed skeleton of a face-centered cube is designed for subsequent numerical studies. The results show that the fluid and particle flow velocities are higher near the fracture surfaces than inside the interlayers. In addition, it is found that as the fine particle concentration increases, the average horizontal velocity increases, whereas the settling velocity tends to decrease.

Generated Pseudo-Labels Guided by Background Skeletons for Overcoming Under-Segmentation in Overlapping Particle Objects

Unlike general image segmentation, highly complex particle images have significant challenges in labeling and segmentation due to the information occlusion and texture disturbance. Aiming at the highly under-segmentation problem caused by complex particle image segmentation, this paper proposes a Semi-supervised Hybrid-training Particle Segmentation framework (SHPS) based on skeleton-guided pseudo-labels. First, a pre-trained model is obtained by training a popular segmentation algorithm on partially labeled data. Then, a Background Skeleton-guided Pseudo-label generation algorithm (BSP) is proposed to generate pseudo-labels closer to the ground truth in terms of structural integrity based on coarse segmentation. The final segmentation model is obtained by training a mixed dataset consisting of labeled data and pseudo-labels from another partition on the pre-trained model. The skeleton differences of pseudo-labels and coarse segmentation are added to the loss function. Experimental results show that our method achieves 84.4% accuracy on mIoU with uniform label data distribution, which is 2.1% higher than the accuracy of UNet and reduces the degree of under-segmentation.

Brown Rice Germ Integrity Identification Based on Deep Learning Network

For the quality inspection of brown rice, the segmentation of connected brown rice and the identification of germ integrity are very important. However, there is no better traditional algorithm to achieve better segmentation and recognition results. This paper improves the brown rice (BR) segmentation algorithm based on background skeleton. The candidate matching points are obtained by the background skeleton method, and the optimal matching points are found by the ant colony algorithm. Experimental results show that the proposed segmentation algorithm achieves 96% accuracy, indicating that it can effectively suppress the interference from the endosperm surface. After segmentation is complete, identification of embryo integrity is performed. Firstly, a convolutional neural network (CNN) is built to identify the germ direction; then, the germ direction is normalized; finally, an improved Inception-v3 network is built to identify the germ integrity. On the basis of the Inception-v3 network, additional branches are added to improve the detection accuracy of small objects. In addition, mutual-channel loss and mlpconv are added to enable the model to better approximate the abstraction of the latent space. The experimental results show that the comprehensive recognition accuracy of the proposed algorithm is as high as 94.83%, which is significantly higher than the current mainstream recognition algorithms.

A coarse-to-fine scene text detection method based on Skeleton-cut detector and Binary-Tree-Search based rectification

Scene text detection has been a long standing hot and challenging research topic in pattern recognition. In this paper a novel coarse-to-fine text detection method is proposed to solve edge-adhesion problem. In coarse detection stage, Skeleton-cut detector is proposed. At first,8-Neighborhoods-Search is applied on skeletons map to find the adhesion junctions between text and background skeletons. Then junctions in disordered skeletons are picked out by hysteresis selection and cut to separate text skeletons from background. And the text skeletons are verified through a two-stage classifier to obtain the coarse detection result. In fine detection stage, bounding boxes of all these filtered skeletons are weighted accumulated to obtain the Static Skeleton Response(SSR). Then many finer text lines candidates can be calculated through the gradient operation to the SSR’s horizontal projection. And the text rectification based on Binary-Tree-Search is proposed to find a path from text lines’ search space to the fine detection result. Experimental results on ICDAR dataset, SVT dataset and MSRA-TD500 dataset demonstrate that our algorithm achieves state of art performance in scene text detection.

MATHEMATICAL MORPHOLOGY BASED CHARACTERIZATION OF BINARY IMAGE

This paper reports the results of a theoretical study on morphological characterization of foreground (X) and background (Xc) of a discrete binary image. Erosion asymmetry and dilation asymmetry, defined to elaborate smoothing of an image respectively by contraction and expansion, are generalized for multiscale smoothing, and their relationships with morphological skeleton and ridge (background skeleton) transformations are discussed. Then we develop algorithms identifying image topology in terms of critical scales corresponding to close-hulls and open-skulls, along with a few other salient characteristics, as respective smoothing by expansion and contraction proceeds. For empirical demonstration of these algorithms, essentially to unravel the hidden characteristics of topological and geometrical relevance, we considered deterministic and random binary Koch quadric fractals. A shape-size based zonal quantization technique for image and its background is introduced as analytical outcome of these algorithms. The ideas presented and demonstrated on binary fractals could be easily extended to the grayscale images and fractals.

Selective Extraction of Entangled Textures via Adaptive PDE Transform.

Texture and feature extraction is an important research area with a wide range of applications in science and technology. Selective extraction of entangled textures is a challenging task due to spatial entanglement, orientation mixing, and high-frequency overlapping. The partial differential equation (PDE) transform is an efficient method for functional mode decomposition. The present work introduces adaptive PDE transform algorithm to appropriately threshold the statistical variance of the local variation of functional modes. The proposed adaptive PDE transform is applied to the selective extraction of entangled textures. Successful separations of human face, clothes, background, natural landscape, text, forest, camouflaged sniper and neuron skeletons have validated the proposed method.

Patch‐type Segmentation of Voxel Shapes using Simplified Surface Skeletons

AbstractWe present a new method for decomposing a 3D voxel shape into disjoint segments using the shape's simplified surface‐skeleton. The surface skeleton of a shape consists of 2D manifolds inside its volume. Each skeleton point has a maximally inscribed ball that touches the boundary in at least two contact points. A key observation is that the boundaries of the simplified fore‐ and background skeletons map one‐to‐one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method segments both noisy shapes and shapes with soft edges which vanish over low‐curvature regions. Multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel‐based implementation of our approach and illustrate it on several realistic examples.

Two-stage segmentation of unconstrained handwritten Chinese characters

Correct segmentation of handwritten Chinese characters is crucial to their successful recognition. However, due to many difficulties involved, little work has been reported in this area. In this paper, a two-stage approach is presented to segment unconstrained handwritten Chinese characters. A handwritten Chinese character string is first coarsely segmented according to the background skeleton and vertical projection after a proper image preprocessing. With several geometric features, all possible segmentation paths are evaluated by using the fuzzy decision rules learned from examples. As a result, unsuitable segmentation paths are discarded. In the fine segmentation stage that follows, the strokes that may contain segmentation points are first identified. The feature points are then extracted from candidate strokes and taken as segmentation point candidates through each of which a segmentation path may be formed. The geometric features similar to the coarse segmentation stage are used and corresponding fuzzy decision rules are generated to evaluate fine segmentation paths. Experimental results on 1000 Chinese character strings from postal mail show that our approach can achieve a reasonable good overall accuracy in segmenting unconstrained handwritten Chinese characters.

Segmentation of single- or multiple-touching handwritten numeral string using background and foreground analysis

An approach of segmenting a single- or multiple-touching handwritten numeral string (two-digits) is proposed. Most algorithms for segmenting connected digits mainly focus on the analysis of foreground pixels. Some concentrated on the analysis of background pixels only and others are based on a recognizer. We combine background and foreground analysis to segment single- or multiple-touching handwritten numeral strings. Thinning of both foreground and background regions are first processed on the image of connected numeral strings and the feature points on foreground and background skeletons are extracted. Several possible segmentation paths are then constructed and useless strokes are removed. Finally, the parameters of geometric properties of each possible segmentation paths are determined and these parameters are analyzed by the mixture Gaussian probability function to decide the best segmentation path or reject it. Experimental results on NIST special database 19 (an update of NIST special database 3) and some other images collected by ourselves show that our algorithm can get a correct rate of 96 percent with rejection rate of 7.8 percent, which compares favorably with those reported in the literature.

A background-thinning-based approach for separating and recognizing connected handwritten digit strings

Most algorithms for segmenting connected handwritten digit strings are based on the analysis of the foreground pixel distributions and the features on the upper/lower contours of the image. In this paper, a new approach is presented to segment connected handwritten two-digit strings based on the thinning of background regions. The algorithm first locates several feature points on the background skeleton of a digit image. Possible segmentation paths are then constructed by matching these feature points. With geometric property measures, all the possible segmentation paths are ranked using fuzzy rules generated from a decision-tree approach. Finally, the top ranked segmentation paths are tested one by one by an optimized nearest neighbor classifier until one of these candidates is accepted based on an acceptance criterion. Experimental results on NIST special database 3 show that our approach can achieve a correct classification rate of 92.5% with only 4.7% of digit strings rejected, which compares favorably with the other techniques tested.