An efficient segmentation method based on dynamic graph merging

2016 ◽  
Vol 14 (06) ◽  
pp. 1650052 ◽  
Author(s):  
Cun Cheng ◽  
Li Zhang
Keyword(s):  
Image Segmentation ◽  
Energy Functional ◽  
Global Model ◽  
Low Energy ◽  
Segmentation Algorithm ◽  
Large Energy ◽  
Segmentation Method ◽  
Dynamic Graph ◽  
Interactive Image Segmentation ◽  
Segmentation Methods

A novel energy functional based on the Mumford–Shah model is established for performing automatic image segmentation. And in order to optimize the global model using graph-based methods, we develop a localized formula. Then, we propose a merging predicate for determining whether an edge connecting two neighboring pixels or regions merge. The dynamic graph merging (DGM) method is applied based on this merging predicate. That is, those edges with large energy merge and the edges with low energy are remained, such that the energy functional is minimized. Compared with other graph-based segmentation methods, our algorithm based on DGM has an important characteristic which is its ability to produce good segmentation on some complex texture images. Another characteristic is that this segmentation algorithm can avoid the “shrinking bias” problem. We also apply DGM to interactive image segmentation and find the results to be encouraging too.

scholarly journals Object segmentation methods for online model acquisition to guide robotic grasping

2021 ◽  
Author(s):  
Dmitri Ignakov
Keyword(s):  
Object Segmentation ◽  
Vision System ◽  
Geometric Model ◽  
Graph Cuts ◽  
Segmentation Algorithm ◽  
Service Robots ◽  
Segmentation Method ◽  
Robotic Grasping ◽  
Model Acquisition ◽  
Segmentation Methods

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. . First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method also models the distribution of 3D points in space as a distribution of algebraic distances from an ellipsoid fitted to the object, improving the method's ability to predict which points are likely to belong to the object or the background. Experimental validation of all methods is performed. Each method is evaluated in a realistic setting, utilizing scenarios of various complexities. Experimental results have demonstrated the effectiveness of the handle localization method, and the object segmentation methods.

scholarly journals An Ore Image Segmentation Method Based on RDU-Net Model

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4979
Author(s):  
Dong Xiao ◽  
Xiwen Liu ◽  
Ba Tuan Le ◽  
Zhiwen Ji ◽  
Xiaoyu Sun
Keyword(s):  
Image Segmentation ◽  
Production Efficiency ◽  
Fragment Size ◽  
Conveyor Belt ◽  
Segmentation Method ◽  
Residual Structure ◽  
Production Safety ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Main Index

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

scholarly journals TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodong Huang ◽  
Hui Zhang ◽  
Li Zhuo ◽  
Xiaoguang Li ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Positional Information ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Segmentation Methods ◽  
Feature Pyramid

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

PCNN Based Welding Seam Image Segmentation Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 861-866 ◽  
Author(s):  
Bei Ji Zou ◽  
Hao Yu Zhou ◽  
Zai Liang Chen ◽  
Hao Chen ◽  
Guo Jiang Xin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Spatial Proximity ◽  
Variance Ratio ◽  
Segmentation Method ◽  
Welding Seam ◽  
Pulse Coupled Neural Network ◽  
Image Segmentation Algorithm

A new welding seam image segmentation method based on pulse-coupled neural network (PCNN) is presented in this paper. The method segments image by utilizing PCNN’s specific feature that the fire of one neuron can capture firing of its adjacent neurons due to their spatial proximity and intensity similarity. The method can automatically confirm the best iteration times by comparing the maximum of variance ratio and get the best segmentation results. Experimental results show that the proposed method has good performance in both results and execution efficiency.

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

A Kind of Watershed Image Segmentation Method Based on Combination Pre-Processing

2010 ◽  
Vol 121-122 ◽  
pp. 320-324
Author(s):  
Jin Xi Wang ◽  
Lin Xiang Liu ◽  
Xiu Zheng Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Simple Operation ◽  
Processing Steps ◽  
Segmentation Problem ◽  
Image Segmentation Algorithm

The watershed algorithm has been widely used in image segmentation for its characteristics of accurately positioning edge, simple operation and etc. But it also has drawbacks of easy to over-segmentation and loss important outline for the character of sensitive to noise. Aiming at the problem of over-segmentation of watershed algorithm, the paper brought out an improved image segmentation algorithm based on watershed, which can limit the number of existing regions that are allowed with combination pre-processing steps, so that the over-segmentation problem can be better solved. The result of experiment also verifies the correctness and feasibility of the proposed algorithm in the paper.

scholarly journals A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhuqing Yang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Three Dimensional ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods ◽  
Three Dimensional Segmentation

Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

scholarly journals Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Feng-Ping An ◽  
Zhi-Wen Liu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Rapid Development ◽  
Feedback Mechanism ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Image Segmentation Algorithm

With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people’s time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.

Multi-level Image Segmentation using Randomized Spiral-based Whale Optimization Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Basu Dev Shivahare ◽  
S.K. Gupta
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Objective Evaluation ◽  
Whale Optimization Algorithm ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Whale Optimization ◽  
Novel Variant ◽  
Multiple Regions ◽  
Multi Level

Abstract: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is challenging problem. This paper introduces a novel multi-level thresholding based image segmentation method. The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analysis the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods. The segmentation results are validated in terms of subjective and objective evaluation. Experiments arm that the presented method is efficient and competitive than the existing multi-level image segmentation methods

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