Self-adaptive bridge bare rebar detection algorithm based on local image segmentation and multi-feature filtering

2020 ◽  
Vol 41 (3) ◽  
pp. 508-515
Author(s):  
HE Fuqiang ◽  
◽  
LUO Hong ◽  
YAO Xuelian ◽  
PING An
Keyword(s):  
Image Segmentation ◽  
Detection Algorithm ◽  
Feature Filtering ◽  
Local Image ◽  
Self Adaptive

scholarly journals Early smoke detection of forest fires based on SVM image segmentation

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 150-159
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Smoke Detection ◽  
Large Space ◽  
Merging Algorithm ◽  
Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

Self-Adaptive PCNN Based on the ACO Algorithm and its Application on Medical Image Segmentation

2016 ◽  
Vol 23 (2) ◽  
pp. 303-310 ◽  
Author(s):  
Xinzheng Xu ◽  
Tianming Liang ◽  
Guanying Wang ◽  
Maxin Wang ◽  
Xuesong Wang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Self Adaptive

scholarly journals Implicit Active Contours Driven by Local and Global Image Fitting Energy for Image Segmentation and Target Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaosheng Yu ◽  
Yuanchen Qi ◽  
Ziwei Lu ◽  
Nan Hu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Target Localization ◽  
Numerical Accuracy ◽  
Variational Level Set ◽  
Contour Evolution ◽  
Level Set Formulation ◽  
Local Image

We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages in terms of accuracy, efficiency, and robustness.

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