Recurrence Indicators for the Estimation of Characteristic Size and Frequency of Spatial Patterns

2013 ◽  
pp. 209-217
Author(s):  
Chiara Mocenni ◽  
Angelo Facchini
Keyword(s):  
Dynamical System ◽  
Image Segmentation ◽  
Spatial Patterns ◽  
Characteristic Size ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Typical Structure

In this chapter, the authors propose a method for the estimation of the characteristic size and frequency of the typical structure in systems showing two dimensional spatial patterns. In particular, they use several indicators caught from the nonlinear framework for identifying the small and large scales of the systems. The indicators are applied to the images corresponding to the instantaneous realization of the system. The method assumes that it is possible to capture the main system’s properties from the distribution of the recurring patterns in the image and does not require the knowledge of the dynamical system generating the patterns neither the application of any image segmentation method.

Research on Aluminum-Plastic Blister Drug Image Segmentation Method Based on Improved Otsu Theory

2014 ◽  
Vol 962-965 ◽  
pp. 2797-2800
Author(s):  
Hui Jun Yu ◽  
Wu Wan ◽  
Chen Yun ◽  
Cai Biao Chen
Keyword(s):  
Image Segmentation ◽  
Defect Detection ◽  
Segmentation Result ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Computing Complexity ◽  
Integral Effect ◽  
Result Show ◽  
Computing Speed ◽  
Improved Algorithm

In the digital image processing, Otsu algorithm uses the criterion of maximum between-cluster to make image segmentation. In this paper, combined with drug defect detection requirements, the new threshold output functions is put forward which studies on the existing two-dimensional Otsu algorithm in a deep way from the computing complexity and integral effect. The improved algorithm improves the computing speed of the algorithm and optimizes the segmentation effect which is a good segmentation algorithm. The effectiveness of the proposed algorithm has been proved by relevant experiments, and the medicine image segmentation result show that the improved algorithm has a good application prospect in drug defect detection.

A two-dimensional image segmentation method based on genetic algorithm and entropy

Optik ◽  
2017 ◽  
Vol 131 ◽  
pp. 414-422 ◽  
Author(s):  
S. Abdel-Khalek ◽  
Anis Ben Ishak ◽  
Osama A. Omer ◽  
A.-S.F. Obada
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Dimensional Image ◽  
Two Dimensional Image

scholarly journals An improved method of microscopic image segmentation

2018 ◽  
Vol 176 ◽  
pp. 01041
Author(s):  
Zhang Feng Shou ◽  
Dong Fang ◽  
Liu Jian Ting ◽  
Meng Xin
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Microscopic Image ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Improved Method ◽  
Image Optimization ◽  
Segmentation Image

In order to improve the effectiveness and accuracy of image processing in modern medical inspection, a segmentation image optimization algorithm of improved two-dimensional maximum entropy threshold based on genetic algorithm combined with mathematical morphology is proposed, in view of the microscopic cell images characteristic and the shortcomings of the traditional segmentation algorithm. Through theoretical analysis and contrast test, the segmentation method proposed is superior to the traditional threshold segmentation method in microscopic cell images, and the average segmentation time of the improved algorithm is 73% and 44% higher than the traditional two-dimensional maximum entropy threshold and the improved two-dimensional maximum entropy threshold.

KEYWORDS SPECIFICATION FOR IMAGES USING SANDGLASS-TYPE NEURAL NETWORKS

2004 ◽  
Vol 04 (02) ◽  
pp. 143-152 ◽  
Author(s):  
SEIJI ITO ◽  
SIGERU OMATU
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Factor Analysis ◽  
Image Segmentation ◽  
Dimensional Space ◽  
Training Dataset ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Testing Dataset ◽  
Specification Method

We propose a keyword specification method for images, which can be used to retrieve an image by a keyword. In order to specify a keyword for a sub-region of the image, images are segmented in some regions. Here, we consider ten keywords to specify the regions. The image segmentation method consists of the maximum-distance algorithm, labeling, and merging the small regions. We provide training regions for each keyword. Important features of the keyword are selected using the Factor Analysis (FA). The features are compressed into a two-dimensional space using a Sandglass-type Neural Network (SNN). We train the SNN using the training dataset. Then 60 samples of the testing dataset are evaluated by keywords extraction.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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