The development of component tree structure for grayscale image segmentation

2003 ◽  
Author(s):  
V. Mosorov ◽  
T.M. Kowalski
Keyword(s):  
Image Segmentation ◽  
Tree Structure ◽  
Grayscale Image ◽  
Component Tree

scholarly journals Bidirectional labeling and registration scheme for grayscale image segmentation

2005 ◽  
Vol 14 (12) ◽  
pp. 2073-2081 ◽  
Author(s):  
Lei Ma ◽  
Xiao-Ping Zhang ◽  
J. Si ◽  
G.P. Abousleman
Keyword(s):  
Image Segmentation ◽  
Grayscale Image ◽  
Registration Scheme

scholarly journals A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation

2014 ◽  
Vol 12 (06) ◽  
pp. 1450044 ◽  
Author(s):  
Jérôme Gilles ◽  
Kathryn Heal
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Scale Space ◽  
Space Representation ◽  
Grayscale Image ◽  
Local Minima ◽  
Clustering Problem ◽  
Space Curves ◽  
Space Approach

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.

scholarly journals A Novel Method for Grayscale Image Segmentation by Using GIT-PCANN

2011 ◽  
Vol 3 (5) ◽  
pp. 12-18
Author(s):  
Haiyan Li ◽  
Lei Guo ◽  
Yufeng Zhang ◽  
Xinling Shi ◽  
Jianhua Chen
Keyword(s):  
Image Segmentation ◽  
Grayscale Image ◽  
Novel Method

scholarly journals SEGMENTASI CITRA DIGITAL IKAN MENGGUNAKAN METODE THRESHOLDING

2013 ◽  
Vol 13 (1) ◽  
pp. 74 ◽  
Author(s):  
Max R Kumaseh ◽  
Luther Latumakulita ◽  
Nelson Nainggolan
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Contour Plot ◽  
Segmentation Result ◽  
Grayscale Image ◽  
Detection Process ◽  
Thresholding Method ◽  
Whole Process ◽  
Local Thresholding ◽  
Segmentation Image

SEGMENTASI CITRA DIGITAL IKAN MENGGUNAKAN METODE THRESHOLDINGABSTRAK Untuk mengenal jenis-jenis ikan berdasarkan ciri-cirinya, telah dibuat suatu sistem untuk memisahkan objek mata ikan menggunakan metode thresholding. Prosesnya dimulai dengan menginput citra digital ikan, selanjutnya dikonversi ke citra grayscale. Kemudian dilakukan proses segmentasi terhadap citra grayscale. Selanjutnya, dipilih hasil segmentasi dan ditandai dengan proses deteksi tepi menggunakan operator Canny yang dipertajam dengan proses dilasi. Proses terakhir adalah membuat plot contour terhadap hasil proses dilasi dan citra grayscale. Hasil segmentasi berhasil memisahkan objek mata ikan dengan menggunakan metode thresholding local. Keseluruhan proses ini dilakukan dengan menggunakan MATLAB R2012a. Kata kunci : Mata Ikan, Segmentasi Citra, Thresholding DIGITAL FISH IMAGE SEGMENTATION BY THRESHOLDING METHOD ABSTRACT A system of fish eyelets seperation has been conducted to identify types of fish acording to their characteristics, by using thresholding method. The process start by inserting digital fish image then convert it to grayscale image. Next step is to process segmentation the grayscale image. Choosed the segmentation result then marked it by edge detection process using Canny operation which has been sharpened by dilation process. The last step is to make contour plot to dilation result and grayscale image. The result of the segmentation shows that the fish eyelets can be separated using local thresholding method. The whole process is conducted by using MATLAB R2012a. Keywords : Fish Eyelets, Segmentation Image, Thresholding

Grayscale Image Segmentation With Quantum-Inspired Multilayer Self-Organizing Neural Network Architecture Endorsed by Context Sensitive Thresholding

2021 ◽  
pp. 197-227
Author(s):  
Pankaj Pal ◽  
Siddhartha Bhattacharyya ◽  
Nishtha Agrawal
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Activation Function ◽  
Grayscale Image ◽  
Threshold Values ◽  
Neural Network Architecture ◽  
Context Sensitive ◽  
Segmentation Process ◽  
Sigmoidal Activation Function

A method for grayscale image segmentation is presented using a quantum-inspired self-organizing neural network architecture by proper selection of the threshold values of the multilevel sigmoidal activation function (MUSIG). The context-sensitive threshold values in the different positions of the image are measured based on the homogeneity of the image content and used to extract the object by means of effective thresholding of the multilevel sigmoidal activation function guided by the quantum superposition principle. The neural network architecture uses fuzzy theoretic concepts to assist in the segmentation process. The authors propose a grayscale image segmentation method endorsed by context-sensitive thresholding technique. This quantum-inspired multilayer neural network is adapted with self-organization. The architecture ensures the segmentation process for the real-life images as well as synthetic images by selecting intensity parameter as the threshold value.

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