70 research outputs found
Interactive Feature Embedding for Infrared and Visible Image Fusion
General deep learning-based methods for infrared and visible image fusion
rely on the unsupervised mechanism for vital information retention by utilizing
elaborately designed loss functions. However, the unsupervised mechanism
depends on a well designed loss function, which cannot guarantee that all vital
information of source images is sufficiently extracted. In this work, we
propose a novel interactive feature embedding in self-supervised learning
framework for infrared and visible image fusion, attempting to overcome the
issue of vital information degradation. With the help of self-supervised
learning framework, hierarchical representations of source images can be
efficiently extracted. In particular, interactive feature embedding models are
tactfully designed to build a bridge between the self-supervised learning and
infrared and visible image fusion learning, achieving vital information
retention. Qualitative and quantitative evaluations exhibit that the proposed
method performs favorably against state-of-the-art methods
A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition
The fusion of images from the visible and long-wave infrared (thermal) portions of the spectrum
produces images that have improved face recognition performance under varying lighting conditions.
This is because long-wave infrared images are the result of emitted, rather than reflected,
light and are therefore less sensitive to changes in ambient light. Similarly, 3D and 2.5D images
have also improved face recognition under varying pose and lighting. The opacity of glass to
long-wave infrared light, however, means that the presence of eyeglasses in a face image reduces
the recognition performance.
This thesis presents the design and performance evaluation of a novel camera system which is
capable of capturing spatially registered visible, near-infrared, long-wave infrared and 2.5D depth
video images via a common optical path requiring no spatial registration between sensors beyond
scaling for differences in sensor sizes. Experiments using a range of established face recognition
methods and multi-class SVM classifiers show that the fused output from our camera system not
only outperforms the single modality images for face recognition, but that the adaptive fusion
methods used produce consistent increases in recognition accuracy under varying pose, lighting
and with the presence of eyeglasses
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