463 research outputs found
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging
In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly
used beamformer. However, it leads to a low resolution and high level of
sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower
sidelobes compared to DAS. In this paper, to improve the resolution and
sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based
Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is
shown that expanding the DMAS algebra leads to several terms which can be
interpreted as DAS. Using the EIBMV adaptive beamforming instead of the
existing DAS (inside the DMAS algebra expansion) is proposed to improve the
image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is
shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution
and sidelobes. In particular, at the depth of 11 mm of the experimental images,
EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to
DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental
images, the quantitative results indicate that EIBMV-DMAS leads to improvement
in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and
EIBMV, respectively.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0796
Recovering facial shape using a statistical model of surface normal direction
In this paper, we show how a statistical model of facial shape can be embedded within a shape-from-shading algorithm. We describe how facial shape can be captured using a statistical model of variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to Cartesian points on a local tangent plane. The distribution of surface normal directions is captured using the covariance matrix for the projected point positions. The eigenvectors of the covariance matrix define the modes of shape-variation in the fields of transformed surface normals. We show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by Lambert's law. We demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details. We assess the accuracy of the technique on a variety of images with ground truth and real-world images
Object recognition in infrared imagery using appearance-based methods
Abstract unavailable please refer to PD
Hyperspectral phase imaging based on denoising in complex-valued eigensubspace
A new denoising algorithm for hyperspectral complex domain data has been
developed and studied. This algorithm is based on the complex domain
block-matching 3D filter including the 3D Wiener filtering stage. The developed
algorithm is applied and tuned to work in the singular value decomposition
(SVD) eigenspace of reduced dimension. The accuracy and quantitative advantage
of the new algorithm are demonstrated in simulation tests and in the processing
of the experimental data. It is shown that the algorithm is effective and
provides reliable results even for highly noisy data
Principal Component Analysis based Image Fusion Routine with Application to Stamping Split Detection
This dissertation presents a novel thermal and visible image fusion system with application in online automotive stamping split detection. The thermal vision system scans temperature maps of high reflective steel panels to locate abnormal temperature readings indicative of high local wrinkling pressure that causes metal splitting. The visible vision system offsets the blurring effect of thermal vision system caused by heat diffusion across the surface through conduction and heat losses to the surroundings through convection. The fusion of thermal and visible images combines two separate physical channels and provides more informative result image than the original ones. Principal Component Analysis (PCA) is employed for image fusion to transform original image to its eigenspace. By retaining the principal components with influencing eigenvalues, PCA keeps the key features in the original image and reduces noise level. Then a pixel level image fusion algorithm is developed to fuse images from the thermal and visible channels, enhance the result image from low level and increase the signal to noise ratio. Finally, an automatic split detection algorithm is designed and implemented to perform online objective automotive stamping split detection. The integrated PCA based image fusion system for stamping split detection is developed and tested on an automotive press line. It is also assessed by online thermal and visible acquisitions and illustrates performance and success. Different splits with variant shape, size and amount are detected under actual operating conditions
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