Fusion of infrared and visible images using empirical mode decomposition and spatial opponent processing

Sissinto, Paterne; Ladeji-Osias, Jumoke

doi:10.1109/aipr.2011.6176358

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods for fusing visible and infrared videos are discussed in [152] and [163], where the two images are aligned and combined using an overlay image with the heat information. In [133] two fusion methods are compared, one named a general fusion model (pixel level) and the other method named a combination module (feature level).…”

Section: Image Fusionmentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

608

269

View full text Add to dashboard Cite

Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras.This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

show abstract

Section: Image Fusionmentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

608

269

View full text Add to dashboard Cite

show abstract

“…Various sensors have been leveraged to exploit the benefits of different modalities for the extraction of meaningful information conveyed by a user’s hand gesture. Many studies have demonstrated the apparent advantages of using multiple modalities to improve various tasks [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Lately, the diffusion of relatively low-cost thermal sensors (e.g., a portable camera or a smart phone module) has promoted research on the benefits of thermal information.…”

Section: Introductionmentioning

confidence: 99%

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter

Kim

Ban

Lee

2017

Sensors

View full text Add to dashboard Cite

The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor’s stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.

show abstract

Spectrum Characteristics Preserved Visible and Near-Infrared Image Fusion Algorithm

Zhang

et al. 2021

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Fusion of infrared and visible images using empirical mode decomposition and spatial opponent processing

Cited by 3 publications

References 11 publications

Thermal cameras and applications: a survey

Thermal cameras and applications: a survey

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter

Spectrum Characteristics Preserved Visible and Near-Infrared Image Fusion Algorithm

Contact Info

Product

Resources

About