scholarly journals THERMAL INFRARED INSPECTION OF ROOF INSULATION USING UNMANNED AERIAL VEHICLES

2015 ◽  
Vol XL-1/W4 ◽  
pp. 381-386 ◽  
Author(s):  
J. Zhang ◽  
J. Jung ◽  
G. Sohn ◽  
M. Cohen
Keyword(s):  
Anomaly Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Thermal Anomaly ◽  
Thermal Infrared ◽  
Throughput Capacity ◽  
Detection Accuracy ◽  
Visual Interpretation ◽  
Markov Random ◽  
Calibration Algorithm

UAVs equipped with high-resolution thermal cameras provide an excellent investigative tool used for a multitude of building-specific applications, including roof insulation inspection. We have presented in this study a relative thermographic calibration algorithm and a superpixel Markov Random Field model to address problems in thermal infrared inspection of roof insulation using UAVs. The relative thermographic radiometric calibration algorithm is designed to address the autogain problem of the thermal camera. Results show the algorithm can enhance the contrast between warm and cool areas on the roof surface in thermal images, and produces more constant thermal signatures of different roof insulations or surfaces, which could facilitate both visual interpretation and computer-based thermal anomaly detection. An automatic thermal anomaly detection algorithm based on superpixel Markov Random Field is proposed, which is more computationally efficient than pixel based MRF, and can potentially improve the production throughput capacity and increase the detection accuracy for thermal anomaly detection. Experimental results show the effectiveness of the proposed method.

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Meenal Suryakant Vatsaraj ◽  
Rajan Vishnu Parab ◽  
Prof.D.S Bade
Keyword(s):  
Anomaly Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Background Subtraction ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Histogram Of Oriented Gradients ◽  
Markov Random ◽  
Wide Range ◽  
Crowded Environment

Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on histogram of oriented gradients and markov random field easily captures varying dynamic of the crowded environment. Histogram of oriented gradients along with well known markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost. To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Meenal Suryakant Vatsaraj ◽  
Rajan Vishnu Parab ◽  
D S Bade
Keyword(s):  
Anomaly Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Background Subtraction ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Anomalous Behavior ◽  
Markov Random ◽  
Wide Range ◽  
Spatio Temporal

Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on a histogram of oriented gradients and Markov random field easily captures varying dynamic of the crowded environment.Histogram of oriented gradients along with well-known Markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost.To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation. 

A Cross-border Detection Algorithm for Agricultural Spraying UAV

2019 ◽  
Vol 35 (2) ◽  
pp. 163-174
Author(s):  
Chaoying TANG ◽  
Xianghui WEI ◽  
Biao WANG ◽  
Shitala PRASAD
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Field Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Border Detection ◽  
Image Processing Algorithm ◽  
Agriculture Sector ◽  
Cross Border ◽  
Markov Random

Abstract.In the agriculture sector, an essential task of spraying uncrewed aerial vehicles (UAVs) is to return as soon as the farmland border is reached. Initially, they need to be manually controlled which is a tedious job. This article presents an efficient image processing algorithm to automatically detect farmland borders based on the images received from the airborne cameras. First, the steerable-filter-based surrounded inhibition method was adopted to detect major borders, and then the images were thinned and binarized using non-maxima suppression (NMS) and hysteresis thresholding, respectively. Secondly, the results with different inhibition coefficients were fused, and the burrs were trimmed. Then the breakpoints were connected using a seed growing method. Finally, an improved Markov Random Field (MRF) model based on line segments was proposed to screen out fake borders. The result of classification depends on the maximum length of the retained segment. The experimental results and offline field tests showed that the proposed algorithm could accurately detect farm borders of different types from a complex farmland image. The average detection accuracy and completeness of the proposed algorithm is 85.6% and 83.6%, respectively. Compared with other methods, the proposed algorithm is highly reliable, robust, and scalable to other applications. Keywords: Agricultural spraying UAVs, Cross-border detection, Markov Random Field (MRF), Steerable filters, Surround suppression.

scholarly journals SAR IMAGE CHANGE DETECTION BASED ON FUZZY MARKOV RANDOM FIELD MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 2371-2374
Author(s):  
J. Zhao ◽  
G. Huang ◽  
Z. Zhao
Keyword(s):  
Change Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Fuzzy Theory ◽  
Detection Accuracy ◽  
Sar Image ◽  
Markov Random ◽  
Image Change Detection ◽  
Image Pixels ◽  
Detection Effect

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Fusion Moves for Markov Random Field Optimization

2010 ◽  
Vol 32 (8) ◽  
pp. 1392-1405 ◽  
Author(s):  
Victor Lempitsky ◽  
Carsten Rother ◽  
Stefan Roth ◽  
Andrew Blake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Markov Random
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