Real-time fusion of visible and thermal infrared images in surveillance applications on SoC hardware

2018 ◽  
Author(s):  
Konrad Moren ◽  
Thomas Perschke
Keyword(s):  
Real Time ◽  
Thermal Infrared ◽  
Infrared Images ◽  
Thermal Infrared Images ◽  
Surveillance Applications

scholarly journals Face Alignment in Thermal Infrared Images Using Cascaded Shape Regression

2021 ◽  
Vol 18 (4) ◽  
pp. 1776
Author(s):  
Kent Nagumo ◽  
Tomohiro Kobayashi ◽  
Kosuke Oiwa ◽  
Akio Nozawa
Keyword(s):  
Skin Temperature ◽  
Real Time ◽  
Region Of Interest ◽  
Thermal Infrared ◽  
Appearance Model ◽  
Face Alignment ◽  
Infrared Images ◽  
Time Performance ◽  
Thermal Infrared Images ◽  
Shape Regression

The evaluation of physiological and psychological states using thermal infrared images is based on the skin temperature of specific regions of interest, such as the nose, mouth, and cheeks. To extract the skin temperature of the region of interest, face alignment in thermal infrared images is necessary. To date, the Active Appearance Model (AAM) has been used for face alignment in thermal infrared images. However, computation using this method is costly, and it has a low real-time performance. Conversely, face alignment of visible images using Cascaded Shape Regression (CSR) has been reported to have high real-time performance. However, no studies have been reported on face alignment in thermal infrared images using CSR. Therefore, the objective of this study was to verify the speed and robustness of face alignment in thermal infrared images using CSR. The results suggest that face alignment using CSR is more robust and computationally faster than AAM.

scholarly journals Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5061
Author(s):  
Adam Dlesk ◽  
Karel Vach ◽  
Karel Pavelka
Keyword(s):  
Structure From Motion ◽  
Thermal Infrared ◽  
Processing Method ◽  
Spatial Transformation ◽  
Infrared Images ◽  
Low Resolution ◽  
Thermal Infrared Images ◽  
Rgb Images ◽  
Motion Method

The photogrammetric processing of thermal infrared (TIR) images deals with several difficulties. TIR images ordinarily have low-resolution and the contrast of the images is very low. These factors strongly complicate the photogrammetric processing, especially when a modern structure from motion method is used. These factors can be avoided by a certain co-processing method of TIR and RGB images. Two of the solutions of co-processing were suggested by the authors and are presented in this article. Each solution requires a different type of transformation–plane transformation and spatial transformation. Both types of transformations are discussed in this paper. On the experiments that were performed, there are presented requirements, advantages, disadvantages, and results of the transformations. Both methods are evaluated mainly in terms of accuracy. The transformations are presented on suggested methods, but they can be easily applied to different kinds of methods of co-processing of TIR and RGB images.

scholarly journals Applications of Satellite Thermal Infrared Images for Monitoring North Water during the Periods of Polar Darkness

1980 ◽  
Vol 25 (93) ◽  
pp. 425-438
Author(s):  
B. Dey
Keyword(s):  
Satellite Images ◽  
Open Water ◽  
Thermal Infrared ◽  
Water Area ◽  
Infrared Images ◽  
Baffin Bay ◽  
The North ◽  
Thermal Infrared Images ◽  
North Water

AbstractThe study reported here illustrates the unique value of NOAA thermal infrared (TIR) images for monitoring the North Water area in Smith Sound and northern Baffin Bay during the periods of polar darkness. Wintertime satellite images reveal that, during the months of December through February, open water and thin ice occur in a few leads and polynyas. However, in March, the areas of open water and thin ice decrease to a minimum with a consequent higher concentration of ice. Two ice dams, in northern Kennedy Channel and in northern Smith Sound, regulate the flow of ice into northern Baffin Bay and also determine the areal variations of open water and thin ice in Smith Sound.

Thermal Human Face Recognition for Biometric Security System

2013 ◽  
pp. 1-24 ◽  
Author(s):  
Ayan Seal ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu
Keyword(s):  
Face Recognition ◽  
Thermal Infrared ◽  
Infrared Images ◽  
Human Machine Interaction ◽  
Human Face Recognition ◽  
Face Images ◽  
Thermal Infrared Images ◽  
The Face ◽  
Intensity Images ◽  
Machine Interaction

Automatic face recognition has been comprehensively studied for more than four decades, since face recognition of individuals has many applications, particularly in human-machine interaction and security. Although face recognition systems have achieved a significant level of maturity with some realistic achievement, face recognition still remains a challenging problem due to large variation in face images. Face recognition techniques can be generally divided into three categories based on the face image acquisition methodology: methods that work on intensity images, those that deal with video sequences, and those that require other sensory (like 3D sensory or infra-red imagery) data. Researchers are using thermal infrared images for face recognition. Since thermal infrared images have some advantages over 2D images. In this chapter, an overview of some of the well-known techniques of face recognition using thermal infrared faces are discussed, and some of the drawbacks and benefits of each of these methods mentioned therein are discussed. This chapter talks about some of the most recent algorithms developed for this purpose, and tries to give a brief idea of the state of the art of face recognition technology. The authors propose one approach for evaluating the performance of face recognition algorithms using thermal infrared images. They also note the results of several classifiers on a benchmark dataset (Terravic Facial Infrared Database).

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