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iee colloquium on image processing for Security applications (Digest No: 1997/074)

作者： A.N. Marana S.A. Velastin L.F. Costa R.A. Lotufo DEMAC IGCE UNESP Sao Paulo Brazil EEE King''s College London UK IFSC USP Sao Paulo Brazil DCA-FEEC-UNICAMP-Brazil Sao Paulo Brazil

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The paper describes a technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

关键词： Safety

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Advanced visual surveillance using Bayesian networks

Advanced visual surveillance using Bayesian networks

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iee colloquium on image processing for Security applications (Digest No: 1997/074)

作者： H. Buxton School of Cognitive and Computing Sciences University of Sussex UK

Visual surveillance primarily involves the interpretation of image sequences. Advanced visual surveillance goes further and automates the detection of predefined alarm events in a given context. However, it is the intelligent, dynamic scene and event discrimination which lies at the heart of advanced vision systems. Developing a systematic methodology for the design, implementation and integration of such systems is currently a very important research problem. One way of overcoming some of these problems is to build in more knowledge of the scene and tasks. For example, in object detection and tracking in the image, we have demonstrated that it is beneficial to bring scene-based knowledge of expected object trajectories, size and speed into the interpretation process. We have also shown that both scene and task-based knowledge allows for selective processing under attentional control for behavioural evaluation. In addition to this general requirement for integration of information in advanced visual surveillance, we have adopted more specific requirements. A fixed, precalibrated camera model and precomputed ground-plane geometry is used to simplify the interpretation of the scene data in the on-line system. We also adopt a knowledge-based approach in which domain specific models of the dynamic objects, events and behaviour are used to meet the requirement for sensitive and accurate performance.

关键词： Surveillance

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Using Xilinx FPGAs to implement neural networks and fuzzy systems

Using Xilinx FPGAs to implement neural networks and fuzzy sy...

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iee colloquium on neural and Fuzzy Systems: Design, Hardware and applications (Digest No: 1997/133)

作者： J.J. Blake L.P. Maguire T.M. McGinnity L.J. McDaid Faculty of Engineering University of Ulster Londonderr UK

Over the last thirty years, since Zadeh first introduced fuzzy set theory, there has been widespread interest in the real-time application of fuzzy logic, particularly in the area of control. Recently, there has been considerable interest in the development of dedicated hardware implementations which facilitate high speed processing. However, the main drawback of such an approach is that it is only cost effective for high-volume applications. A more feasible methodology for lower volume problems demands the application of general-purpose or programmable hardware such as the Xilinx FPGAs. There has been a similar trend in the area of neural networks, as initial research employed software simulations but more recent interest has investigated hardware implementations. FPGAs are becoming increasingly popular for prototyping and designing complex hardware systems. The structure of an FPGA can be described as an "array of blocks" connected together via programmable interconnections. The main advantage of FPGAs is the flexibility that they afford. An engineer can change and refine a device's design by exploiting the device's reprogrammability. Xilinx introduced the world's first FPGA, the XC2064, in 1985. This contained approximately 1000 logic gates. Since then, the gate density of Xilinx FPGAs has increased 25 times. There has been a lot of recent interest in the FPGA realisation of fuzzy systems. Similarly there are a number of FPGA implementations of neural networks reported in the literature. However. this paper provides a report on the implementation of both architectures and also offers a comparison with the hybrid structure.

关键词： neural networks

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Autonomous segmentation and neural network texture classification of IR image sequences

Autonomous segmentation and neural network texture classific...

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Proceedings of the iee colloquium on image processing for Remote Sensing

作者： Haddon, John F. Boyce, James F. Strens, Malcolm Farnborough Hants

This paper presents a technique for the texture classification of segmented regions in sequences of infrared images taken from low flying aircraft. Sequences of IR images have been segmented using a technique which integrates edge detection and region segmentation into a single, inherently parallel process. A temporal component is used to ensure that the segmentation is consistent in time. The texture of the segmented regions is characterized using discrete Hermite functions and property co-occurrence matrices. This results in a low order feature vector in which the zeroth coefficient describes the Gaussian noise of the region while the higher orders describe the texture. neural networks are used to perform an initial classification of the segmented regions, this is then refined using spatio-temporal relaxation labelling to ensure consistency of interpretation, both spatially within an image and temporal within the sequence.

关键词： Infrared imaging

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Feature detection using electromagnetic tomography and neural networks

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iee colloquium (Digest) 1996年第143期 varpaging页

作者： Lau, Jose Nuno Borges, Antonio Rui Universidade de Aveiro Aveiro Portugal

Electromagnetic tomography (EMT) was used to perform feature detection of anomalies of `a priori' known objects, employing neural networks (NNs) for data processing. Investigations showed that the NNs have good discrimination properties over features for which they have been trained, performing very well even in a not very precise environment where the object was moved by hand. An estimation approach lead to simpler and faster training. For a similar resolution, the network complexity was also much lower than for the classification approach. The EMT NN combination is a very promising approach for applications requiring not an image but information on the variability of a particular feature of a given object.

关键词： Computerized tomography

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Autonomous segmentation and neural network texture classification of IR image sequences

Autonomous segmentation and neural network texture classific...

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iee colloquium on image processing for Remote Sensing

作者： J.F. Haddon J.F. Boyce M. Strens Famborough Hants Defence Research Agency Wheatstone Laboratory King''s College Hospital Medical School London UK

Presents a technique for the texture classification of segmented regions in sequences of infrared images taken from low flying aircraft. Sequences of IR images have been segmented using a technique which integrates edge detection and region segmentation into a single, inherently parallel process. A temporal component is used to ensure that the segmentation is consistent in time. The texture of the segmented regions is characterized using discrete Hermite functions and property co-occurrence matrices. This results in a low order feature vector in which the zeroth coefficient describes the Gaussian noise of the region while the higher orders describe the texture. neural networks are used to perform an initial classification of the segmented regions, this is then refined using spatio-temporal relaxation labelling to ensure consistency of interpretation, both spatially within an image and temporal within the sequence.

关键词： Remote sensing Geophysical measurements Geophysical signal processing image texture analysis image classification Infrared imaging Optical data processing image segmentation neural networks image sequence analysis

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Identification of boundaries in MRI medical images using artificial neural networks

Identification of boundaries in MRI medical images using art...

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iee colloquium on Artificial Intelligence Methods for Biomedical Data processing

作者： I. Middleton R.I. Damper Dept. of Electron. & Comput. Sci. Southampton Univ. UK Image Speech and Intelligent Systems Research Group Department of Electronics and Computer Science University of Southampton Southampton UK

In the area of medical imaging, fully-automatic and robust segmentation techniques would have an enormous beneficial impact on clinical practice and research, by decreasing dramatically the manual effort which must otherwise be devoted to this task. Deployment of conventional image processing techniques has not so far led to a fully-automatic solution, although semi-automatic systems do exist. Since no known, robust segmentation algorithm exists, the ability of neural networks to discover regularities and features in complex data is appealing. Indeed, many preliminary attempts at neural segmentation have been described, although none yet achieves the necessary level of performance for routine application. Southampton General Hospital have a requirement to obtain lung-boundary data within an asthma research project. In connection with this requirement, we have previously reported on work in which multilayer perceptrons (MLPs) are trained using backpropagation to segment the region of the lungs in magnetic resonance images of the thorax. This is achieved by training the network to classify voxels as either boundary (voxels on the boundary between lung interior and surrounding tissue) or non-boundary. In this paper, we present the latest results using this technique. We also show how the generalisation performance of the MLP can be improved using a variety of techniques, including weight pruning algorithms.

关键词： Biomedical magnetic resonance imaging Respiratory system Biomedical image processing image classification Multilayer perceptrons Backpropagation Artificial intelligence image edge analysis

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EEG-based assessment of anaesthetic depth using neural networks

EEG-based assessment of anaesthetic depth using neural netwo...

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iee colloquium on Artificial Intelligence Methods for Biomedical Data processing

作者： M. Krkic S.J. Roberts I. Rezek C. Jordan Department of Electrical Electronic Engineering Imperial College University of London UK Department of Anaesthetics Northwick Park Hospital Harrow UK

To determine the appropriate dose of anaesthetic drug to be used during surgery is a far from trivial problem. Signs of autonomic activity are not very accurate; hence, an alternative technique is required to monitor the depth of anaesthesia. In common with many biological systems, brain activity is a dynamical system having irregular and unpredictable characteristics. The mathematical analysis of the behaviour of dynamical systems has expanded rapidly with the advent of fast computers. This has given rise to a wide range of medical applications, including mathematical analysis of the human EEG. It is in this last genre that the automatic assessment of the depth of anaesthesia, using ongoing EEG as a measure of brain activity, is discussed. One of the most important problems in EEG analysis is the extraction of appropriate features to describe the ongoing signal, and this can be tackled in various ways. The feature extraction stage of the work described in this paper was performed using methods of dynamic systems analysis and involved the extraction of stochastic and dynamic complexity features of the signal. Feature-spaces formed using these two methods were used as input to a radial basis function pattern classifier. We show that, despite the agent specificity of EEG changes in anaesthesia, a useful anaesthetic depth monitor may be created. We present results for two different anaesthetic agents: desflurane and propofol.

关键词： Surgery Biomedical monitoring Electroencephalography Biomedical signal processing Feature extraction Feedforward neural networks Nonlinear systems Pattern classification

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Fully automatic left ventricular myocardial boundary detection in echocardiographic images: a comparison of two modern methods

Fully automatic left ventricular myocardial boundary detecti...

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iee colloquium on Artificial Intelligence Methods for Biomedical Data processing

作者： S.K. Setarehdan J.J. Soraghan I.A. Hunter Signal Process. Div. Strathclyde Univ. Glasgow UK Texas Instruments Inc. Northampton UK

The echo images in 2D echocardiography have poor noise characteristics and low spatial and grey-value resolutions. Numerous attempts have been made to develop automated algorithms for quantitative analysis and boundary extraction in these images, but as yet none have been developed adequately to be used clinically. This report presents two modern approaches for automatic extraction of the left ventricular (LV) epicardial and endocardial boundaries from short-axis (SA) echocardiographic data, and compares their performance. Both methods use the radial search algorithm in the extraction process. In the AMRBDS (automatic multiresolution boundary detection system), the first stage uses fuzzy logic and the spatial and intensity information of the input image to estimate the LV centre point (LVCP). Then, a novel multiresolution edge detection technique based on the wavelet transform is applied to each one of the radial intensity profiles to extract the most probable and unique LV edge points along them. Median post-filtering and cubic B-spline techniques are employed to produce the final LV boundaries. In the AANNBDS (automatic artificial neural networks boundary detection system), an MLP (multilayer perceptron) is used to detect the most appropriate centre point of the LV. A second MLP is trained to classify each pixel on the radial lines as an inner, outer or non-edge point. Finally, knowledge guided snakes are employed to extract the LV borders by minimization of the snakes' energy function.

关键词： Echocardiography Muscles Biomedical image processing image edge analysis Multilayer perceptrons Spline functions

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iee Electronics Division colloquium on Signal Procesing in Cardiography

IEE Electronics Division Colloquium on Signal Procesing in C...

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iee Electronics Division colloquium on Signal Procesing in Cardiography

The proceedings contains 11 papers. Topics discussed include signal processing in cardiology, image display of electrocardiograms, frequency analysis of ventricular fibrillation, fragmentation mapping systems, optimal enhancement of fetal electrocardiograms, spectral analysis of prosthetic heart valve sounds, ECG signal enhancement, signal processing in analysis of heart sounds, time-frequency analysis and diagnosis of heart valve sounds, neural networks applications, wiener filtering of fetal ECG using wavelet transforms.

关键词： Signal processing

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