6,952 research outputs found
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Robotic and animal mapping systems share many challenges and characteristics:
they must function in a wide variety of environmental conditions, enable the
robot or animal to navigate effectively to find food or shelter, and be
computationally tractable from both a speed and storage perspective. With
regards to map storage, the mammalian brain appears to take a diametrically
opposed approach to all current robotic mapping systems. Where robotic mapping
systems attempt to solve the data association problem to minimise
representational aliasing, neurons in the brain intentionally break data
association by encoding large (potentially unlimited) numbers of places with a
single neuron. In this paper, we propose a novel method based on supervised
learning techniques that seeks out regularly repeating visual patterns in the
environment with mutually complementary co-prime frequencies, and an encoding
scheme that enables storage requirements to grow sub-linearly with the size of
the environment being mapped. To improve robustness in challenging real-world
environments while maintaining storage growth sub-linearity, we incorporate
both multi-exemplar learning and data augmentation techniques. Using large
benchmark robotic mapping datasets, we demonstrate the combined system
achieving high-performance place recognition with sub-linear storage
requirements, and characterize the performance-storage growth trade-off curve.
The work serves as the first robotic mapping system with sub-linear storage
scaling properties, as well as the first large-scale demonstration in
real-world environments of one of the proposed memory benefits of these
neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and
Automation Letter
Confocal microscopic image sequence compression using vector quantization and 3D pyramids
The 3D pyramid compressor project at the University of Glasgow has developed a compressor for images obtained from CLSM device. The proposed method using a combination of image pyramid coder and vector quantization techniques has good performance at compressing confocal volume image data. An experiment was conducted on several kinds of CLSM data using the presented compressor compared to other well-known volume data compressors, such as MPEG-1. The results showed that the 3D pyramid compressor gave higher subjective and objective image quality of reconstructed images at the same compression ratio and presented more acceptable results when applying image processing filters on reconstructed images
Semiannual status report
The work performed in the previous six months can be divided into three main cases: (1) transmission of images over local area networks (LAN's); (2) coding of color mapped (pseudo-color) images; and (3) low rate video coding. A brief overview of the work done in the first two areas is presented. The third item is reported in somewhat more detail
Data compression for satellite images
An efficient data compression system is presented for satellite pictures and two grey level pictures derived from satellite pictures. The compression techniques take advantages of the correlation between adjacent picture elements. Several source coding methods are investigated. Double delta coding is presented and shown to be the most efficient. Both predictive differential quantizing technique and double delta coding can be significantly improved by applying a background skipping technique. An extension code is constructed. This code requires very little storage space and operates efficiently. Simulation results are presented for various coding schemes and source codes
Data comparison schemes for Pattern Recognition in Digital Images using Fractals
Pattern recognition in digital images is a common problem with application in
remote sensing, electron microscopy, medical imaging, seismic imaging and
astrophysics for example. Although this subject has been researched for over
twenty years there is still no general solution which can be compared with the
human cognitive system in which a pattern can be recognised subject to
arbitrary orientation and scale.
The application of Artificial Neural Networks can in principle provide a very
general solution providing suitable training schemes are implemented.
However, this approach raises some major issues in practice. First, the CPU
time required to train an ANN for a grey level or colour image can be very
large especially if the object has a complex structure with no clear geometrical
features such as those that arise in remote sensing applications. Secondly,
both the core and file space memory required to represent large images and
their associated data tasks leads to a number of problems in which the use of
virtual memory is paramount.
The primary goal of this research has been to assess methods of image data
compression for pattern recognition using a range of different compression
methods. In particular, this research has resulted in the design and
implementation of a new algorithm for general pattern recognition based on
the use of fractal image compression.
This approach has for the first time allowed the pattern recognition problem to
be solved in a way that is invariant of rotation and scale. It allows both ANNs
and correlation to be used subject to appropriate pre-and post-processing
techniques for digital image processing on aspect for which a dedicated
programmer's work bench has been developed using X-Designer
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