A Gray-Level Image Segmentation Method

1989 ◽  
Author(s):  
Paolo Puliti ◽  
Guido Tascini
Keyword(s):  
Image Segmentation ◽  
Gray Level ◽  
Segmentation Method ◽  
Gray Level Image

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 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.

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.

Efficient image segmentation performance of gray-level image using normalized graph cut based neutrosophic membership function

2021 ◽  
Vol 30 (04) ◽  
Author(s):  
Palanisamy Karthick ◽  
Samayan Narayanamoorthy ◽  
Sengottaiyan Maheswari ◽  
Suriyakumaran Sowmiya
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Graph Cut ◽  
Gray Level ◽  
Gray Level Image

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.

An Edge Detection Method Based on Adjacent Dispersion

2016 ◽  
Vol 30 (10) ◽  
pp. 1655026
Author(s):  
Qindong Sun ◽  
Yimin Qiao ◽  
Hua Wu ◽  
Jiamin Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Detection Method ◽  
Superior Performance ◽  
Gray Level ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gray Level Image ◽  
Novel Method ◽  
Edge Detection Method

Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.

scholarly journals Gray Level Image Threshold Using Neutrosophic Shannon Entropy

2019 ◽  
Author(s):  
Vasile Patrascu
Keyword(s):  
Shannon Entropy ◽  
Experimental Results ◽  
New Method ◽  
Gray Level ◽  
Segmentation Method ◽  
Separation Threshold ◽  
Multiple Thresholds ◽  
Gray Level Image ◽  
Information Components ◽  
Grayscale Images

This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.

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.

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