Image Matching Algorithm for Thermal Panorama Image Construction Adaptable for Fire Disasters

Gwak, Dong-Gi; Kim, Dong Hwan

doi:10.5302/j.icros.2016.16.0154

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The associate editor coordinating the review of this manuscript and approving it for publication was Mostafa Rahimi Azghadi . passive infrared imaging, which uses real-time acquisition of the difference in infrared thermal radiation intensity of different parts of natural objects to form images [2], [3], [4], [5], [6], so it is widely used in electric power prevention and detection [7], industrial temperature measurement [8], medical quarantine [9], auto-auxiliary driving [10], fire search and rescue [11], security monitoring [12] and other fields. In these special fields, the addition of an infrared thermal imaging system avoids the defects of the original visible imaging system and promotes the rapid development of related fields.…”

Section: Introductionmentioning

confidence: 99%

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

Mei

Wang²,

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

A fast and robust template matching scheme, called Matching by Slice Transform Matrix Mapping (MSTMM), is proposed for the matching difficulty under occluded scenarios caused by nonlinear intensity differences and structure differences between visible and thermal infrared images. The first step in the MSTMM scheme was to extract information about the distribution of pixels with the same gray level through the developed Expanded Slice Transform with Adaptive Gray Level (EST-AGL). After completing the construction of the EST-AGL matrix for all image patches, different EST-AGL matrices were mapped to different integers or floats through the traditional special integer mapping mechanism or the neural network mapping mechanism. Finally, template matching between visible and thermal infrared images was achieved by evaluating the similarity of correlation mapping surface images through the Normalized Cross Correlation (NCC) algorithm. The proposed EST-AGL method can overcome the nonlinear intensity differences between visible and thermal infrared images by extracting the structural features of the image. The mapping mechanism of the MSTMM scheme can reduce the structural differences between the normal template image and the query image under an occluded scenario by increasing the similarity between the normal image patches and the image patches with occlusion. The proper mapping mechanism ensures the high performance of the MSTMM scheme by using only the simple NCC algorithm instead of other time-consuming anti-occlusion dense feature algorithms in the similarity evaluation stage. The three main experimental results of the MSTMM scheme are as follows: (1) the scheme of MSTMM can achieve template matching in only 0.015 seconds when a 64 × 64 template image slides on a 256 × 256 query image on a hardware platform with limited resources; (2) the matching success rate of the MSTMM scheme can reach up to 75% among 2107 experimental samples; and (3) the neural network training in the neural network mapping mechanism only takes at least 104.4 seconds on the CPU.INDEX TERMS Template matching, multimodal image, heterogeneous image, multisource image, visible and infrared image, image matching, neural network.

show abstract

Section: Introductionmentioning

confidence: 99%

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

Mei

Wang²,

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Scholars at home and abroad have achieved a lot in the recent research of fire image monitoring systems [8][9][10]. For example, Du and Fan [11] extracted several dynamic and static features of flames (e.g., color, shape, and moving speed), and constructed a forest fire recognition system based on support vector machine (SVM), which improves the ability of realtime fire recognition.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Classification of Image Features for Fire Recognition Based on Convolutional Neural Network

Qi¹,

Liu²

2021

View full text Add to dashboard Cite

Fire image monitoring systems are being applied to more and more fields, owing to their large monitoring area. However, the existing image processing-based fire detection technology cannot effectively make real-time fire warning in actual scenes, and the relevant fire recognition algorithms are not robust enough. To solve the problems, this paper tries to extract and classify image features for fire recognition based on convolutional neural network (CNN). Specifically, the authors set up the framework of a fire recognition system based on fire video images (FVIFRS), and extracted both static and dynamic features of flame. To improve the efficiency of image analysis, a Gaussian mixture model was established to extract the features from the fire smoke movement areas. Finally, the CNN was improved to process and classify the fire feature maps of the CNN. The proposed algorithm and model were proved to be feasible and effective through experiments.

show abstract

Visible/Infrared Image Registration Based on Region-Adaptive Contextual Multifeatures

Zhao,

Dong,

Liu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Image Matching Algorithm for Thermal Panorama Image Construction Adaptable for Fire Disasters

Cited by 3 publications

References 0 publications

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

Extraction and Classification of Image Features for Fire Recognition Based on Convolutional Neural Network

Visible/Infrared Image Registration Based on Region-Adaptive Contextual Multifeatures

Contact Info

Product

Resources

About