Contourlet Transform Based Texture Analysis for Smoke and Fog Classification

2011 ◽  
Vol 88-89 ◽  
pp. 537-542 ◽  
Author(s):  
Yang Zhao ◽  
Jian Hui Zhao ◽  
Jing Huang ◽  
Shi Zhong Han ◽  
Cheng Jiang Long ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Local Binary Pattern ◽  
Fire Detection ◽  
Contourlet Transform ◽  
Support Vector ◽  
Smoke Detection ◽  
Accuracy Rate ◽  
Practical Applications ◽  
Detection Algorithms ◽  
Important Research Topic

Fire detection has long been an important research topic in image processing and pattern recognition, while smoke is a vital indication of fire’s existence. However, current smoke detection algorithms are far from meeting the requirements of practical applications. One major reason is that the existing methods can not distinguish smoke from fog because their colors and shapes are both very similar. This paper proposes a novel texture analysis based algorithm which has the ability to classify smoke and fog more efficiently. First the texture images are decomposed using Contourlet Transform (CT), and then we extract the feature vector from Contourlet coefficients, finally we make use of Support Vector Machine (SVM) to classify the textures. Experiments are performed on the sample images of smoke and fog taking accuracy rate of classification as evaluation criterion, and the accuracy rate of our algorithm is 97%. To illustrate its performance, our method has also been compared with the algorithms using Gray Level Co-occurrence Matrixes (GLCM), Local Binary Pattern (LBP) and Wavelet Transform (WT).

scholarly journals Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

2019 ◽  
Vol 11 (12) ◽  
pp. 3261 ◽  
Author(s):  
Jesus Olivares-Mercado ◽  
Karina Toscano-Medina ◽  
Gabriel Sánchez-Perez ◽  
Aldo Hernandez-Suarez ◽  
Hector Perez-Meana ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Fire Detection ◽  
Candidate Region ◽  
Support Vector ◽  
False Alarms ◽  
Detection Accuracy ◽  
Multi Layer Perceptron ◽  
System Efficiency ◽  
Dynamic Texture ◽  
Early Fire Detection

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.

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.

Quantitative identification of coal texture using the support vector machine with geophysical logging data: A case study using medium-rank coal from the Panjiang, Guizhou, China

2020 ◽  
Vol 8 (4) ◽  
pp. T753-T762
Author(s):  
Zhenghui Xiao ◽  
Wei Jiang ◽  
Bin Sun ◽  
Yunjiang Cao ◽  
Lei Jiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Texture Analysis ◽  
Quantitative Methods ◽  
Hard Coal ◽  
Support Vector ◽  
Linear Kernel ◽  
Accuracy Rate ◽  
Geophysical Logging ◽  
Soft Coal ◽  
Natural Gamma

Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane (CBM) exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector machine (SVM), a machine-learning method, has received great interest due to its remarkable generalization performance, and it has been used to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference (AC), caliper log (CAL), density (DEN), and natural gamma (GR), were used for coal texture analysis. Hard coal (undeformed and cataclastic coal) exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest (97%) when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be used to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions.

scholarly journals A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection

2020 ◽  
Author(s):  
Luiz Antonio Buschetto ◽  
Felipe Vieira Roque ◽  
Luan Casagrande ◽  
Tiago Oliveira Weber ◽  
Cristian Cechinel
Keyword(s):  
Texture Analysis ◽  
Local Binary Pattern ◽  
Search Algorithm ◽  
Support Vector ◽  
Fabric Defect Detection ◽  
Processing Approach ◽  
Extraction Step ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Grid Search Algorithm

The quality control is an essential step in fabric industries. Detectdefects in the early stages can reduce costs and increase the qualityof the products. Currently, this task is mainly done by humans,whose judgment can be affected by fatigue. Computer vision-basedtechniques can automatically detect defects, reducing the need forhuman intervention. In this context, this work proposes an imageblock-processing approach, where we compare the Segmentation-Based Fractal Texture Analysis, Gray Level Co-Occurrence Matrix,and Local Binary Pattern in the feature extraction step. Aimingto show the efficiency of this approach for the problem, these resultswere compared with the same algorithms without the blockprocessingapproach. A Support Vector Machine optimized by Grid-Search Algorithm was used to classify the fabrics. The databaseused, which is available online, is composed of 479 images fromsamples with defects and without it. The results show that thisblock processing approach can improve the classification results,achieving 100% in this work.

scholarly journals Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 97 ◽  
Author(s):  
Jianyong Zheng ◽  
Hongbo Fan ◽  
Zhining Li ◽  
Qi Zhang
Keyword(s):  
Support Vector Machine ◽  
Magnetic Anomaly ◽  
Local Binary Pattern ◽  
Simulation Analysis ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Accuracy Rate ◽  
Feature Vectors ◽  
Flux Gate

In order to identify the orientation or recognize the attitude of small symmetric magnetic anomaly objects at shallow depth, we propose a method of extracting local binary pattern (LBP) features from denoised magnetic anomaly signals and classifying symmetric magnetic objects that have different orientations based on support vector machine (SVM). First, nine component signals, such as magnetic gradient tensor matrix, total magnetic intensity (TMI), and so forth, are calculated from the original signal detected by the flux gate sensors. The nine component signals are processed by discrete wavelet transform (DWT), which aims to reduce noise and make the signal’s features clear. Then we extract LBP texture features from the denoised nine component signals. From the simulation analysis, we can conclude that the LBP texture features of the nine component signals have good interclass discrimination and intraclass aggregation, which can be used for pattern recognition. Finally, the LBP texture features are constructed into feature vectors. The orientations of symmetric ferromagnetic objects underground are identified by SVM based on the feature vectors. Through experiments, we can conclude that the orientation recognition accuracy rate reaches 90%. This suggests that we can obtain the details of magnetic anomalies through our method.

scholarly journals Improving Image Steganalyser Performance using Second Order SPAM Features Extracting through Contourlet Transform

2019 ◽  
Vol 9 (1S3) ◽  
pp. 137-143
Keyword(s):  
Detection Rate ◽  
Image Features ◽  
Second Order ◽  
Contourlet Transform ◽  
Support Vector ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Stego Image ◽  
Natural Noise ◽  
Feature Based

The major challenge posed by feature based blind steganalysers is the scheming of useful image features, which offers true existence of the stego noise rather than the natural noise in the images. Despite hundreds of features being applied in the real time implementation, only low detection accuracy could be achieved. Hence, this paper proposes a new model for detecting the stego image coupled with an examination of the task by applying a two-step process. (a) Extraction of the second order SPAM (Subtractive Pixel Adjacency Matrix) as features and the second order SPAM features of coefficients and co-occurrence matrices of sub band images from the contourlet transform. (b) Implementation of the system, based on an efficient classifier, Support Vector Machine which is capable of providing the higher detection rate than the existing classifers. Full- fledged experimentation with huge database of clean and steganogram images produced from seven steganographic schemes with varying embedding rates, and using five steganalysers were carried out in this study. The study shows that the proposed paradigm enhances the detection accuracy rate substantially and validates its efficiency with its better performance even at low embedding rates.

Image fire detection algorithm based on support vector machine

2010 ◽  
Vol 30 (4) ◽  
pp. 1129-1131
Author(s):  
Na-juan YANG ◽  
Hui-qin WANG ◽  
Zong-fang MA
Keyword(s):  
Support Vector Machine ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

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