8,468 research outputs found
Coding local and global binary visual features extracted from video sequences
Binary local features represent an effective alternative to real-valued
descriptors, leading to comparable results for many visual analysis tasks,
while being characterized by significantly lower computational complexity and
memory requirements. When dealing with large collections, a more compact
representation based on global features is often preferred, which can be
obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW)
model. Several applications, including for example visual sensor networks and
mobile augmented reality, require visual features to be transmitted over a
bandwidth-limited network, thus calling for coding techniques that aim at
reducing the required bit budget, while attaining a target level of efficiency.
In this paper we investigate a coding scheme tailored to both local and global
binary features, which aims at exploiting both spatial and temporal redundancy
by means of intra- and inter-frame coding. In this respect, the proposed coding
scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC)
paradigm. That is, visual features are extracted from the acquired content,
encoded at remote nodes, and finally transmitted to a central controller that
performs visual analysis. This is in contrast with the traditional approach, in
which visual content is acquired at a node, compressed and then sent to a
central unit for further processing, according to the Compress-Then-Analyze
(CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of
rate-efficiency curves in the context of two different visual analysis tasks:
homography estimation and content-based retrieval. Our results show that the
novel ATC paradigm based on the proposed coding primitives can be competitive
with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin
Boosting the Maxwell double layer potential using a right spin factor
We construct new spin singular integral equations for solving scattering
problems for Maxwell's equations, both against perfect conductors and in media
with piecewise constant permittivity, permeability and conductivity, improving
and extending earlier formulations by the author. These differ in a fundamental
way from classical integral equations, which use double layer potential
operators, and have the advantage of having a better condition number, in
particular in Fredholm sense and on Lipschitz regular interfaces, and do not
suffer from spurious resonances. The construction of the integral equations
builds on the observation that the double layer potential factorises into a
boundary value problem and an ansatz. We modify the ansatz, inspired by a
non-selfadjoint local elliptic boundary condition for Dirac equations
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %
Acoustic Scene Classification
This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship
(EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council
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