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 Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target

2011 ◽  
Vol 135-136 ◽  
pp. 50-55
Author(s):  
Yuan Bin Hou ◽  
Yang Meng ◽  
Jin Bo Mao
Keyword(s):  
Image Segmentation ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Immune Algorithm ◽  
Ant Colony ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Self Recognition ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

scholarly journals Segmentasi Citra Ikan Tuna dengan Mahalanobis Histogram Thresholding dan Mahalanobis Fuzzy C-Means

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Andi Baso Kaswar ◽  
Agus Zainal Arifin ◽  
Arya Yudhi Wijaya
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Accuracy Improvement ◽  
Tuna Fish ◽  
Fuzzy C Means ◽  
Membership Value ◽  
Number Of Iterations

Abstract. Fuzzy C-Means segmentation algorithm based on Mahalanobis distance can be utilized to segment tuna fish image. However, initialization of pixels membership value and clusters centroid randomly cause the segmentation process become inefficient in terms of iteration and time of computation. This paper proposes a new method for tuna fish image segmentation by Mahalanobis Histogram Thresholding (M-HT) and Mahalanobis Fuzzy C-Means (MFCM). The proposed method consists of three main phases, namely: centroid initialization, pixel clustering and accuracy improvement. The experiment carried out obtained average of iteration amount is as many as 66 iteration with average of segmentation time as many as 162.03 second. While the average of Accuracy is 98.54%, average of Missclassification Error is 1.46%. The result shows that the proposed method can improve the efficiency of segmentation method in terms of number of iterations and time of segmentation. Besides that, the proposed method can give more accurate segmentation result compared with the conventional method.Keywords: Tuna Fish Image, Segmentation, Fuzzy Clustering, Histogram Thresholding, Mahalanobis Distance. Abstrak. Algoritma segmentasi Fuzzy C-Means berbasis jarak Mahalanobis dapat digunakan untuk mensegmentasi ikan tuna. Namun, inisialisasi derajat keanggotaan piksel dan centroid klaster secara random mengakibatkan proses segmentasi menjadi tidak efisien dalam hal iterasi dan waktu komputasi. Penelitian ini mengusulkan metode baru untuk segmentasi citra ikan tuna dengan Mahalanobis Histogram Thresholding (M-HT) dan Mahalanobis Fuzzy C-Means (MFCM). Metode ini terdiri atas tiga tahap utama, yaitu: inisialisasi centroid, pengklasteran piksel dan peningkatan akurasi. Berdasarkan hasil ekseprimen, diperoleh rata-rata jumlah iterasi sebanyak 66 iterasi dengan rata-rata waktu segmentasi 162,03 detik. Rata-rata Akurasi 98,54% dengan rata-rata tingkat Missclassification Error 1,46%. Hasil yang diperoleh menunjukkan bahwa metode yang diusulkan dapat meningkatkan efisiensi metode segmentasi dalam hal jumlah iterasi dan waktu segmentasi. Selain itu, metode yang diusulkan dapat memberikan hasil segmentasi yang lebih akurat dibandingkan dengan metode konvensional.Kata Kunci: Citra Ikan Tuna, Segmentasi, Fuzzy Clustering, Histogram Thresholding, Jarak Mahalanobis.

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.

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

Fabric Defect Detection Based on SRG-PCNN

2010 ◽  
Vol 148-149 ◽  
pp. 1319-1326 ◽  
Author(s):  
Xiao Shu Si ◽  
Hong Zheng ◽  
Xue Min Hu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Defect Detection ◽  
Focal Point ◽  
Region Growing ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Defect Image ◽  
Validation Tests

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

An Improved Watershed Segmentation Method of Medical Image

2015 ◽  
Vol 719-720 ◽  
pp. 1009-1012
Author(s):  
Yu Bin Jiao ◽  
Yan Lei Xu ◽  
Chao Feng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Result Show

The image segmentation is very important in medical image processing. The paper studies the watershed segmentation, and over-segmentation is the main problem of watershed. Based on this, the paper proposed an improved watershed medical image segmentation method. And the corresponding simulation is done and the result show that the method can resolve the over-segmentation of watershed and can achieve good segmentation.

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.

Calculation of flexible printed circuit boards (FPC) global and local defect detection based on computer vision

Circuit World ◽  
2016 ◽  
Vol 42 (2) ◽  
pp. 49-54 ◽  
Author(s):  
Liya Wang ◽  
Yang Zhao ◽  
Yaoming Zhou ◽  
Jingbin Hao
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
New Method ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Content Type ◽  
Printed Circuit ◽  
Flexible Printed Circuit ◽  
Human Operators

Purpose The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection. Design/methodology/approach This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed. Findings Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results. Research limitations/implications The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm. Originality/value This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.

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