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 An Improved Hybrid Segmentation Method for Remote Sensing Images

2019 ◽  
Vol 8 (12) ◽  
pp. 543
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Yongji Wang ◽  
Qingwen Qi
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Schedule Algorithm ◽  
Remote Sensing Image ◽  
Segmentation Method ◽  
Common Boundary ◽  
Remote Sensing Image Classification ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Split And Merge

Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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.

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.

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.

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

An Improved Image Segmentation Method Based Onmulti-Resolution Analysis and Watershed Transformation

2012 ◽  
Vol 500 ◽  
pp. 709-715
Author(s):  
Yan Wang ◽  
Yan Ma
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Input Image ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Watershed Transformation ◽  
Inverse Transform ◽  
Multi Resolution Analysis ◽  
Segmentation Accuracy ◽  
Resolution Analysis

This paper presents an improved image segmentation method based on multi-resolution analysis of wavelet transform and watershed transformation. In the marked-controlled watershed segmentation, we not only enhance the contours of low-resolution input image to acquire segmentation function image, but also use minima imposition technology to apply filters to input image to acquire marked function image. In order to improve segmentation accuracy, we use regional fusion and coarse-fine segmentation in wavelet inverse transform. The experimental results show that the proposed image segmentation method can efficiently reduce over-segmentation, as well as improve the effect of image segmentation. In addition, the proposed method is robust.

A New Medical Image Segmentation Method Based on Chan-Vese Model

2014 ◽  
Vol 513-517 ◽  
pp. 3750-3756 ◽  
Author(s):  
Yuan Zheng Ma ◽  
Jia Xin Chen
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Level Sets ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Segmentation Method ◽  
Accuracy Requirement ◽  
Segmentation Accuracy ◽  
Segmentation Problem

The traditional segmentation method for medical image segmentation is difficult to achieve the accuracy requirement, and when the edges of the image are blurred, it will occurs incomplete segmentation problem, in order to solve this problem, we propose a medical image segmentation method which based on Chan-Vese model and mathematical morphology. The method integrates Chan-Vese model, mathematical morphology, composite multiphase level sets segmentation algorithm, first, through iterative etching operation to extract the outline of the medical image, and then the medical image is segmented by the Chan-Vese model based on the complex multiphase level sets, finally the medical image image is dilated iteratively by using morphological dilation to restore the image. The experimental results and analysis show that, this method improves the multi-region segmentation accuracy during the segmentation of medical image and solves the problem of incomplete segmentation.

Research on CCD Infrared Image Threshold Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1292-1297
Author(s):  
Xing Ma ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Infrared Image ◽  
Experimental System ◽  
Segmentation Method ◽  
Gray Scale ◽  
Image Detection ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Characteristics Analysis

In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

Sign in / Sign up

Export Citation Format

Share Document