50,150 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
An empirical investigation of sparse distributed memory using discrete speech recognition
Presented here is a step by step analysis of how the basic Sparse Distributed Memory (SDM) model can be modified to enhance its generalization capabilities for classification tasks. Data is taken from speech generated by a single talker. Experiments are used to investigate the theory of associative memories and the question of generalization from specific instances
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
This paper describes a novel system for automatic classification of images
obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial
type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
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