166 research outputs found
Detection of a signal in linear subspace with bounded mismatch
We consider the problem of detecting a signal of interest in a background of noise with unknown covariance matrix, taking into account a possible mismatch between the actual steering vector and the presumed one. We assume that the former belongs to a known linear subspace, up to a fraction of its energy. When the subspace of interest consists of the presumed steering vector, this amounts to assuming that the angle between the actual steering vector and the presumed steering vector is upper bounded. Within this framework, we derive the generalized likelihood ratio test (GLRT). We show that it involves solving a minimization problem with the constraint that the signal of interest lies inside a cone. We present a computationally efficient algorithm to find the maximum likelihood estimator (MLE) based on the Lagrange multiplier technique. Numerical simulations illustrate the performance and the robustness of this new detector, and compare it with the adaptive coherence estimator which assumes that the steering vector lies entirely in a subspace
Coercive Region-level Registration for Multi-modal Images
We propose a coercive approach to simultaneously register and segment
multi-modal images which share similar spatial structure. Registration is done
at the region level to facilitate data fusion while avoiding the need for
interpolation. The algorithm performs alternating minimization of an objective
function informed by statistical models for pixel values in different
modalities. Hypothesis tests are developed to determine whether to refine
segmentations by splitting regions. We demonstrate that our approach has
significantly better performance than the state-of-the-art registration and
segmentation methods on microscopy images.Comment: This work has been accepted to International Conference on Image
Processing (ICIP) 201
Mobility and Handoff Management in Wireless Networks
With the increasing demands for new data and real-time services, wireless
networks should support calls with different traffic characteristics and
different Quality of Service (QoS)guarantees. In addition, various wireless
technologies and networks exist currently that can satisfy different needs and
requirements of mobile users. Since these different wireless networks act as
complementary to each other in terms of their capabilities and suitability for
different applications, integration of these networks will enable the mobile
users to be always connected to the best available access network depending on
their requirements. This integration of heterogeneous networks will, however,
lead to heterogeneities in access technologies and network protocols. To meet
the requirements of mobile users under this heterogeneous environment, a common
infrastructure to interconnect multiple access networks will be needed. In this
chapter, the design issues of a number of mobility management schemes have been
presented. Each of these schemes utilizes IP-based technologies to enable
efficient roaming in heterogeneous network. Efficient handoff mechanisms are
essential for ensuring seamless connectivity and uninterrupted service
delivery. A number of handoff schemes in a heterogeneous networking environment
are also presented in this chapter.Comment: 28 pages, 11 figure
How to compare noisy patches? Patch similarity beyond Gaussian noise
International audienceMany tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches (so-called image-based) compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. Recent progress in natural image modeling also makes intensive use of patch comparison. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and machine learning. By expressing patch (dis)similarity as a detection test under a given noise model, we introduce these criteria with a new one and discuss their properties. We then assess their performance for different tasks: patch discrimination, image denoising, stereo-matching and motion-tracking under gamma and Poisson noises. The proposed criterion based on the generalized likelihood ratio is shown to be both easy to derive and powerful in these diverse applications
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