We describe a method for visual based robot navigation with a single omni-directional (catadioptic) camera. We show how omni-directional images can be used to generate the representations needed for two main navigatio...
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We describe a method for visual based robot navigation with a single omni-directional (catadioptic) camera. We show how omni-directional images can be used to generate the representations needed for two main navigation modalities: Topological Navigation and Visual Path Following. Topological Navigation relies on the robot's qualitative global position, estimated from a set of omni-directional images obtained during a training stage (compressed using PCA). To deal with illumination changes, an eigenspace approximation to the Hausdorff measure is exploited. We present a method to transform omni-directional images to Bird's Eye Views that correspond to scaled orthographic views of the ground plane. These images are used to locally control the orientation of the robot, through visual servoing. Visual Path Following is used to accurately control the robot along a prescribed trajectory, by using bird's eye views to track landmarks on the ground plane. Due to the simplified geometry of these images, the robot's pose can be estimated easily and used for accurate trajectory following. Omni-directional images facilitate landmark based navigation, since landmarks remain visible in all images, as opposed to a small field-of-view standard camera. Also, omni-directional images provide the means of having adequate representations to support both accurate or qualitative navigation. Results are described in the paper.
Repetitive, or multipass, processes are a class of 2D systems of both practical and algorithmic/theoretical interest whose dynamics cannot be analysed or controlled using standard (1D) systems theory. Recently it has ...
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Repetitive, or multipass, processes are a class of 2D systems of both practical and algorithmic/theoretical interest whose dynamics cannot be analysed or controlled using standard (1D) systems theory. Recently it has ...
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Repetitive, or multipass, processes are a class of 2D systems of both practical and algorithmic/theoretical interest whose dynamics cannot be analysed or controlled using standard (1D) systems theory. Recently it has been shown that the modelling of the boundary conditions, also known as the process initial conditions, is a crucial feature in the analysis and control of these processes. This paper presents some further results on the effects of so-called 'dynamic' process initial conditions on the controllability and stability properties of discrete linear repetitive processes. Previous work has shown that these dynamic process initial conditions alone can destroy the stability properties of these processes. Hence their effects must be 'adequately' accounted for the process modelling stage in order to ensure that subsequent analysis does not lead to incorrect results/conclusions. The main results developed in this paper can be summarised as follows. (i) Computationally efficient stability tests which can, in effect, be applied using standard, or 1D, linear systems tests. (ii) Characterisation of so-called pass controllability in the form of matrix rank based conditions. (iii) Conditions under which the dynamic process initial conditions can be selected to ensure stability and pass controllability.
Research in human/computer interaction has mainly focused on natural language, text, speech and vision primarily in isolation. Recently there have been a number of research projects that have concentrated on the integ...
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Research in human/computer interaction has mainly focused on natural language, text, speech and vision primarily in isolation. Recently there have been a number of research projects that have concentrated on the integration of such modalities using intelligent reasoners. The rationale is that many inherent ambiguities in single modes of communication can be resolved if extra information is available. This paper describes an intelligent multi-modal system called the Smart Work Manager. The main characteristics of the Smart Work Manager are that it can process speech, text, face images, gaze information and simulated gestures rising the mouse as input modalities, and its output is in the form of speech, text or graphics. The main components of the system are the reasoner, a speech system, a vision system, an integration platform and the application interface. The overall architecture of the system will be described together with the integration platform and the components of the system which include a non-intrusive neural network based gaze-tracking system. The paper concludes with a discussion on the applicability of such systems to intelligent human/computer interaction and lessons learnt in terms of reliability and efficiency.
An important example of bilateral symmetry in nature is the approximate bilateral symmetry exhibited by humans. Detecting and measuring bilateral symmetry in medical images should provide benefits in a clinical enviro...
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An important example of bilateral symmetry in nature is the approximate bilateral symmetry exhibited by humans. Detecting and measuring bilateral symmetry in medical images should provide benefits in a clinical environment. This paper presents a method for detecting the dominant plane of bilateral symmetry in an image of arbitrary dimension and subsequently measuring the degree of bilateral symmetry in the image. By adapting the work of others, we provide a unique solution to the problem by using a simple representation of the data and a more sensitive measure of symmetry.
The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient-based, and so are sensitive to noise, and fail to accurately ...
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The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient-based, and so are sensitive to noise, and fail to accurately ...
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The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient based, and so are sensitive to noise, and fail to accurately ...
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The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient based, and so are sensitive to noise, and fail to accurately locate some feature types that are important in confocal microscopy. The local energy feature detector developed by M.C. Morrone and R.A. Omens (1987) marks locations where there is maximal congruence of phase in the Fourier components of an image. Points of maximal phase congruency occur at all common feature profiles: step and roof edges, line features and Mach bands. A 3D implementation of the local energy feature detector, suitable for confocal microscope data, is presented. The detector computes local energy by convolving an image with oriented pairs of 3D filters. The filters are 3D versions of Morlet wavelets. To increase the speed of the convolution, the filters are designed in frequency space and multiplied by the image's Fourier transform. Results are presented for real confocal images and synthetic 3D image volumes.
Recognizing faces is a difficult problem due to the generally similar shape of faces combined with the considerable variability in images of the same face under different viewing conditions. In this paper we consider ...
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