1,929 research outputs found
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
In this paper we introduce a fully end-to-end approach for multi-spectral
image registration and fusion. Our method for fusion combines images from
different spectral channels into a single fused image by different approaches
for low and high frequency signals. A prerequisite of fusion is a stage of
geometric alignment between the spectral bands, commonly referred to as
registration. Unfortunately, common methods for image registration of a single
spectral channel do not yield reasonable results on images from different
modalities. For that end, we introduce a new algorithm for multi-spectral image
registration, based on a novel edge descriptor of feature points. Our method
achieves an accurate alignment of a level that allows us to further fuse the
images. As our experiments show, we produce a high quality of multi-spectral
image registration and fusion under many challenging scenarios
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
In this paper, we introduce a novel deep-learning method to align
cross-spectral images. Our approach relies on a learned descriptor which is
invariant to different spectra. Multi-modal images of the same scene capture
different signals and therefore their registration is challenging and it is not
solved by classic approaches. To that end, we developed a feature-based
approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration
problem. Our algorithm detects corners by Harris and matches them by a
patch-metric learned on top of CIFAR-10 network descriptor. As our experiments
demonstrate we achieve a high-quality alignment of cross-spectral images with a
sub-pixel accuracy. Comparing to other existing methods, our approach is more
accurate in the task of VIS to NIR registration
Methods for multi-spectral image fusion: identifying stable and repeatable information across the visible and infrared spectra
Fusion of images captured from different viewpoints is a well-known challenge in computer vision with many established approaches and applications; however, if the observations are captured by sensors also separated by wavelength, this challenge is compounded significantly. This dissertation presents an investigation into the fusion of visible and thermal image information from two front-facing sensors mounted side-by-side. The primary focus of this work is the development of methods that enable us to map and overlay multi-spectral information; the goal is to establish a combined image in which each pixel contains both colour and thermal information. Pixel-level fusion of these distinct modalities is approached using computational stereo methods; the focus is on the viewpoint alignment and correspondence search/matching stages of processing. Frequency domain analysis is performed using a method called phase congruency. An extensive investigation of this method is carried out with two major objectives: to identify predictable relationships between the elements extracted from each modality, and to establish a stable representation of the common information captured by both sensors. Phase congruency is shown to be a stable edge detector and repeatable spatial similarity measure for multi-spectral information; this result forms the basis for the methods developed in the subsequent chapters of this work. The feasibility of automatic alignment with sparse feature-correspondence methods is investigated. It is found that conventional methods fail to match inter-spectrum correspondences, motivating the development of an edge orientation histogram (EOH) descriptor which incorporates elements of the phase congruency process. A cost function, which incorporates the outputs of the phase congruency process and the mutual information similarity measure, is developed for computational stereo correspondence matching. An evaluation of the proposed cost function shows it to be an effective similarity measure for multi-spectral information
Visible and infrared self-supervised fusion trained on a single example
This paper addresses the problem of visible (RGB) to Near-Infrared (NIR)
image fusion. Multispectral imaging is an important task relevant to image
processing and computer vision, even more, since the development of the RGBT
sensor. While the visible image sees color and suffers from noise, haze, and
clouds, the NIR channel captures a clearer picture and it is significantly
required by applications such as dehazing or object detection. The proposed
approach fuses these two aligned channels by training a
Convolutional-Neural-Network (CNN) by a Self-Supervised-Learning (SSL) on a
single example. For each such pair, RGB and IR, the network is trained for
seconds to deduce the final fusion. The SSL is based on Sturcture-of-Similarity
(SSIM) loss combined with Edge-Preservation (EP) loss. The labels for the SSL
are the input channels themselves. This fusion preserves the relevant detail of
each spectral channel while not based on a heavy training process. In the
experiments section, the proposed approach achieves better qualitative and
quantitative multispectral fusion results with respect to other recent methods,
that are not based on large dataset training
Target detection, tracking, and localization using multi-spectral image fusion and RF Doppler differentials
It is critical for defense and security applications to have a high probability of detection and low false alarm rate while operating over a wide variety of conditions. Sensor fusion, which is the the process of combining data from two or more sensors, has been utilized to improve the performance of a system by exploiting the strengths of each sensor. This dissertation presents algorithms to fuse multi-sensor data that improves system performance by increasing detection rates, lowering false alarms, and improving track performance. Furthermore, this dissertation presents a framework for comparing algorithm error for image registration which is a critical pre-processing step for multi-spectral image fusion.
First, I present an algorithm to improve detection and tracking performance for moving targets in a cluttered urban environment by fusing foreground maps from multi-spectral imagery. Most research in image fusion consider visible and long-wave infrared bands; I examine these bands along with near infrared and mid-wave infrared. To localize and track a particular target of interest, I present an algorithm to fuse output from the multi-spectral image tracker with a constellation of RF sensors measuring a specific cellular emanation. The fusion algorithm matches the Doppler differential from the RF sensors with the theoretical Doppler Differential of the video tracker output by selecting the sensor pair that minimizes the absolute difference or root-mean-square difference. Finally, a framework to quantify shift-estimation error for both area- and feature-based algorithms is presented. By exploiting synthetically generated visible and long-wave infrared imagery, error metrics are computed and compared for a number of area- and feature-based shift estimation algorithms.
A number of key results are presented in this dissertation. The multi-spectral image tracker improves the location accuracy of the algorithm while improving the detection rate and lowering false alarms for most spectral bands. All 12 moving targets were tracked through the video sequence with only one lost track that was later recovered. Targets from the multi-spectral tracking algorithm were correctly associated with their corresponding cellular emanation for all targets at lower measurement uncertainty using the root-mean-square difference while also having a high confidence ratio for selecting the true target from background targets. For the area-based algorithms and the synthetic air-field image pair, the DFT and ECC algorithms produces sub-pixel shift-estimation error in regions such as shadows and high contrast painted line regions. The edge orientation feature descriptors increase the number of sub-field estimates while improving the shift-estimation error compared to the Lowe descriptor
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
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