4,445 research outputs found
Visible and Near Infrared Image Fusion Based on Texture Information
Multi-sensor fusion is widely used in the environment perception system of
the autonomous vehicle. It solves the interference caused by environmental
changes and makes the whole driving system safer and more reliable. In this
paper, a novel visible and near-infrared fusion method based on texture
information is proposed to enhance unstructured environmental images. It aims
at the problems of artifact, information loss and noise in traditional visible
and near infrared image fusion methods. Firstly, the structure information of
the visible image (RGB) and the near infrared image (NIR) after texture removal
is obtained by relative total variation (RTV) calculation as the base layer of
the fused image; secondly, a Bayesian classification model is established to
calculate the noise weight and the noise information and the noise information
in the visible image is adaptively filtered by joint bilateral filter; finally,
the fused image is acquired by color space conversion. The experimental results
demonstrate that the proposed algorithm can preserve the spectral
characteristics and the unique information of visible and near-infrared images
without artifacts and color distortion, and has good robustness as well as
preserving the unique texture.Comment: 10 pages,11 figure
Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction
As an efficient image contrast enhancement (CE) tool, adaptive gamma
correction (AGC) was previously proposed by relating gamma parameter with
cumulative distribution function (CDF) of the pixel gray levels within an
image. ACG deals well with most dimmed images, but fails for globally bright
images and the dimmed images with local bright regions. Such two categories of
brightness-distorted images are universal in real scenarios, such as improper
exposure and white object regions. In order to attenuate such deficiencies,
here we propose an improved AGC algorithm. The novel strategy of negative
images is used to realize CE of the bright images, and the gamma correction
modulated by truncated CDF is employed to enhance the dimmed ones. As such,
local over-enhancement and structure distortion can be alleviated. Both
qualitative and quantitative experimental results show that our proposed method
yields consistently good CE results
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
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