160,224 research outputs found
Comparative study of Image Fusion Methods: A Review
As the size and cost of sensors decrease, sensor networks are increasingly becoming an attractive method to collect information in a given area. However, one single sensor is not capable of providing all the required information,either because of their design or because of observational constraints. One possible solution to get all the required information about a particular scene or subject is data fusion.. A small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. This paper proposes a method for comprehensive, objective, image fusion performance characterization using a fusion evaluation framework based on gradient information representation. We give the framework of the overallnbsp system and explain its USAge method. The system has many functions: image denoising, image enhancement, image registration, image segmentation, image fusion, and fusion evaluation. This paper presents a literature review on some of the image fusion techniques for image fusion like, Laplace transform, Discrete Wavelet transform based fusion, Principal component analysis (PCA) based fusion etc. Comparison of all the techniques can be the better approach fornbsp future research
Kernel and Classifier Level Fusion for Image Classification.
Automatic understanding of visual information is one of the main requirements for a complete artificial intelligence system and an essential component of autonomous robots. State-of-the-art image recognition approaches are based on different local descriptors, each capturing some properties of the image such as intensity, color and texture. Each set of local descriptors is represented by a codebook and gives rise to a separate feature channel. For classification the feature channels are combined by using multiple kernel learning (MKL), early fusion or classifier level fusion approaches. Due to the importance of complementary information in fusion techniques, there is an increasing demand for diverse feature channels. The first part of the thesis focuses on the ways to encode information from images that is complementary to the state-of-the-art local features. To address this issue we present a novel image representation which can encode the structure of an object and propose three descriptors based on this representation. In the state-of-the-art recognition system the kernels are often computed independently of each other and thus may be highly informative yet redundant. Proper selection and fusion of the kernels is, therefore, crucial to maximize the performance and to address the efficiency issues in visual recognition applications. We address this issue in second part of the thesis where, we propose novel techniques to fuse feature channels for object and pattern recognition. We present an extensive evaluation of the fusion methods on four object recognition datasets and achieve state-of-the-art results on all of them. We also present results on four bioinformatics datasets to demonstrate that the proposed fusion methods work for a variety of pattern recognition problems, provided that we have multiple feature channels
Sensor Fusion of Structure-from-Motion, Bathymetric 3D, and Beacon-Based Navigation Modalities
This paper describes an approach for the fusion of 30
data underwater obtained from multiple sensing modalities.
In particular, we examine the combination of imagebased
Structure-From-Motion (SFM) data with bathymetric
data obtained using pencil-beam underwater sonar, in
order to recover the shape of the seabed terrain. We also
combine image-based egomotion estimation with acousticbased
and inertial navigation data on board the underwater
vehicle.
We examine multiple types of fusion. When fusion is
pe?$ormed at the data level, each modality is used to extract
30 information independently. The 30 representations
are then aligned and compared. In this case, we use
the bathymetric data as ground truth to measure the accuracy
and drijl of the SFM approach. Similarly we use
the navigation data as ground truth against which we measure
the accuracy of the image-based ego-motion estimation.
To our knowledge, this is the frst quantitative evaluation
of image-based SFM and egomotion accuracy in a
large-scale outdoor environment.
Fusion at the signal level uses the raw signals from multiple
sensors to produce a single coherent 30 representation
which takes optimal advantage of the sensors' complementary
strengths. In this papel; we examine how lowresolution
bathymetric data can be used to seed the higherresolution
SFM algorithm, improving convergence rates,
and reducing drift error. Similarly, acoustic-based and inertial
navigation data improves the convergence and driji
properties of egomotion estimation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86044/1/hsingh-35.pd
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
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
A Novel Metric Approach Evaluation For The Spatial Enhancement Of Pan-Sharpened Images
Various and different methods can be used to produce high-resolution
multispectral images from high-resolution panchromatic image (PAN) and
low-resolution multispectral images (MS), mostly on the pixel level. The
Quality of image fusion is an essential determinant of the value of processing
images fusion for many applications. Spatial and spectral qualities are the two
important indexes that used to evaluate the quality of any fused image.
However, the jury is still out of fused image's benefits if it compared with
its original images. In addition, there is a lack of measures for assessing the
objective quality of the spatial resolution for the fusion methods. So, an
objective quality of the spatial resolution assessment for fusion images is
required. Therefore, this paper describes a new approach proposed to estimate
the spatial resolution improve by High Past Division Index (HPDI) upon
calculating the spatial-frequency of the edge regions of the image and it deals
with a comparison of various analytical techniques for evaluating the Spatial
quality, and estimating the colour distortion added by image fusion including:
MG, SG, FCC, SD, En, SNR, CC and NRMSE. In addition, this paper devotes to
concentrate on the comparison of various image fusion techniques based on pixel
and feature fusion technique.Comment: arXiv admin note: substantial text overlap with arXiv:1110.497
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