A Crack Segmentation Approach Using the Combination of Gray Thresholds and Fractal Feature

2012 ◽  
Vol 487 ◽  
pp. 622-626 ◽  
Author(s):  
Song Yang ◽  
Long Tan Shao ◽  
Xiao Xia Guo ◽  
Xiao Liu ◽  
Bo Ya Zhao
Keyword(s):  
Image Segmentation ◽  
Fractal Geometry ◽  
Contour Detection ◽  
Crack Identification ◽  
Gray Level ◽  
Segmentation Method ◽  
Constraint Condition ◽  
Segmentation Approach ◽  
Initial Segmentation ◽  
Crack Contour

A segmentation method of combining gray-level threshold and fractal feature for crack images is proposed, and the fractal law for the perimeter and area of the target is introduced as the constraint condition for the image segmentation of crack. At first, Otsu algorithm is used for the initial segmentation of the crack image, and then the edge of crack is optimized in accordance with fractal law. At last, boundary of crack is determined, and the final result of the image segmentation is obtained. This method makes full use of the fractal geometry law and image information, to effectively solve the problems such as crack contour detection, regional connection and cross crack identification. Several typical examples are analyzed, and the results show that this method has a good segmentation effect on crack images, and it can also be used to identify the other images which have fractal feature.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

Performance Assessment of Sky Segmentation Approaches for UAVs

2019 ◽  
Vol 19 (04) ◽  
pp. 1950023
Author(s):  
Ahmed S. Mashaly
Keyword(s):  
Image Segmentation ◽  
Performance Assessment ◽  
General Purpose ◽  
Segmentation Method ◽  
Research Fields ◽  
Different Types ◽  
Segmentation Approach ◽  
Aerial Vehicle ◽  
Computer Vision Systems ◽  
Overall Performance

Image segmentation is one of the most challenging research fields for both image analysis and interpretation. The applications of image segmentation could be found as the primary step in various computer vision systems. Therefore, the choice of a reliable and accurate segmentation method represents a non-trivial task. Since the selected image segmentation method influences the overall performance of the remaining system steps, sky segmentation appears as a vital step for Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance missions. In this paper, we are going to introduce a comprehensive literature survey of the different types of image segmentation methodology followed by a detailed illustration of the general-purpose methods and the state-of-art sky segmentation approaches. In addition, we introduce an improved version of our previously published work for sky segmentation purpose. The performance of the proposed sky segmentation approach is compared with various image segmentation approaches using different parameters and datasets. For performance assessment, we test our approach under different situations and compare its performance with commonly used approaches in terms of several assessment indexes. From the experimental results, the proposed method gives promising results compared with the other image segmentation approaches.

Research on Image Segmentation Technology with Tissue Section Cell Segmentation Algorithm

2014 ◽  
Vol 1046 ◽  
pp. 88-91
Author(s):  
Chun Bao Huo ◽  
Shuai Tong ◽  
Li Hui Zhao ◽  
Xiang Yun Li
Keyword(s):  
Image Segmentation ◽  
Tissue Section ◽  
Threshold Value ◽  
Cell Segmentation ◽  
Gray Level ◽  
Segmentation Method ◽  
Cell Edge ◽  
Cell Image ◽  
Original Picture ◽  
Maximum Interval

Generally, the effect of cell image that segmented via the threshold value method is not ideal generally; the found cell boundary cannot conform to the cell edge in the original picture well. In this paper, the threshold value segmentation method is improved; apply the judging criterion of gray level difference maximum interval to be the minimum, and conduct secondary treating on the image, and the image’s segmentation effect is more ideal.

scholarly journals Applying Onicescu Information Energy for Gray Level Image Segmentation

2020 ◽  
Author(s):  
Vasile Patrascu
Keyword(s):  
Image Segmentation ◽  
Local Maximum ◽  
Energy Calculation ◽  
Gray Level ◽  
Segmentation Method ◽  
Gray Level Image ◽  
Information Energy

This article presents a method of segmenting images with gray levels that uses Onicescu's information energy calculated in the context of the neutrosophic theory. Starting from the information energy calculation for complete neutrosophic information, it is shown how to extend its calculation for incomplete and inconsistent neutrosophic information. The segmentation method is based on calculation of thresholds for separating the gray levels using the local maximum points of the Onicescu information energy.

scholarly journals An Advanced Road Structure Image Segmentation Method using Swt in Cnn

2019 ◽  
Vol 9 (2S3) ◽  
pp. 114-118
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Speed ◽  
Learning Phase ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Stroke Width ◽  
Segmentation Approach

In this paper, the design of advanced road structure image segmentation approach using stroke width transformation (SWT) in convolution neural network (CNN) is proposed. The main intent of the proposed system is to acquire the aerial images for the vehicle. Basically, this image segmentation performs its operation in two forms they are operating phase and learning phase. Here the aerial image has enhanced by using the SWT transformation. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The SWT will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other.

Segmentasi Citra menggunakan Support Vector Machine (SVM) dan Ellipsoid Region Search Strategy (ERSS) Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

2016 ◽  
Vol 2 (1) ◽  
pp. 11
Author(s):  
Lukman Hakim ◽  
Siti Mutrofin ◽  
Evy Kamilah Ratnasari
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Color Image ◽  
Support Vector ◽  
Gray Level ◽  
Segmentation Method ◽  
Local Homogeneity ◽  
Segmented Image ◽  
Svm Model ◽  
Occurrence Matrix

Abstrak Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM) yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik. Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri. Abstract Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM) classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity. Firstly, the pixel-level color feature and texture feature of the image, which is used as input to SVM model (classifier), are extracted via the local homogeneity and Gray Level Co-Occurrence Matrix (GLCM). Then, determine class of classifier using Arimoto based ERSS thresholding. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation result less satisfied segmented image with 69 % accuracy. Feature reduction is needed to get an effective image segmentation. Key word: image segmentation, support vector machine, ERSS Arimoto Entropy, feature extraction.

scholarly journals Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures

2020 ◽  
pp. paper31-1-paper31-10
Author(s):  
Varvara Tikhonova ◽  
Elena Pavelyeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Principal Curvatures ◽  
Mathematical Methods ◽  
Iris Image ◽  
Segmentation Approach

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments

2012 ◽  
Vol 24 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Yoji Kuroda ◽  
◽  
Masataka Suzuki ◽  
Teppei Saitoh ◽  
Eisuke Terada
Keyword(s):  
Image Segmentation ◽  
Long Range ◽  
Laser Scanner ◽  
Estimation Method ◽  
Urban Environments ◽  
Road Surface ◽  
University Campus ◽  
Segmentation Method ◽  
Segmentation Approach ◽  
Standard Classification

In this paper, we propose a long-range road estimation method for autonomousmobile robots in unstructured urban environments. Near-range road surface conditions are estimated by using remission value as reflectivity of a laser scanner. Graph cut algorithm is applied to estimate road region robustly also in complicated environments. Moreover, we propose a novel image segmentation method to estimate long-range road surface. A compact texture/color feature is integrated with level-set method to estimate precise road boundaries robustly. Our proposed image segmentation approach gives better performance compared with standard classification approach. Finally, we run our autonomous mobile robot in “Tsukuba Challenge 2009” and our university campus, and experimental results have shown a marked increase accuracy in road estimation over standard methods.

scholarly journals Applying Onicescu information energy for gray level image segmentation

2021 ◽  
Author(s):  
Vasile Patrascu
Keyword(s):  
Image Segmentation ◽  
Local Maximum ◽  
Gray Level ◽  
Segmentation Method ◽  
Gray Level Image ◽  
Information Energy

This article presents a method of segmenting images with gray levels that uses Onicescu's information energy calculated in the context of the neutrosophic theory. Starting from the information energy calcula-tion for complete neutrosophic information, it is shown how to extend its calculation for incomplete and inconsistent neutrosophic information. The segmentation method is based on calculation of thresholds for separating the gray levels using the local maximum points of the Onicescu information energy.

Sign in / Sign up

Export Citation Format

Share Document