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Applications of Neural Networks to Signal Processing (Digest No. 1994/248), IEE Colloquium on

Date 15 Dec 1994

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Displaying Results 1 - 12 of 12
  • Neural network edge detection-successes and failures

    Publication Year: 1994, Page(s):2/1 - 2/3
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (216 KB)

    A common problem in image processing is the detection of edges in noisy and incomplete images. Conventional edge detection techniques rely on local gradients which are not robust in noise. Variable thresholding can be used to detect changing edge strengths in the image, but these thresholds have to be found. The present work examines the use of various neural network topologies to improve the robu... View full abstract»

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  • How do neural networks compare with standard filters for image noise suppression?

    Publication Year: 1994, Page(s):3/1 - 3/4
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (444 KB)

    The present paper has been partly motivated by curiosity-can ANNs successfully cope with image noise removal? If so, can they improve on recognised noise suppression techniques? One must remember that the latter use conventional algorithms, or the corresponding hardware, and are not trained to perform the task. Yet the very fact of training reflects that ANNs learn by example, embodying implicit l... View full abstract»

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  • IEE Colloquium on `Applications of Neural Networks to Signal Processing' (Digest No.1994/248)

    Publication Year: 1994
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (48 KB)

    The following topics were dealt with: image feature identification; edge detection; image noise suppression; remote serving data classification; texture classification; ESM radar function identification; gesture recognition; medical signal processing; adaptive equalisers in digital communications View full abstract»

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  • The application of artificial neural networks to naval ESM radar function identification

    Publication Year: 1994, Page(s):7/1 - 7/3
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (236 KB)

    Identification is the last stage in ESM (electronic surveillance measures) processing, which is currently the radar type classification of each track. A track is a parametric description of a radar signal thought to be in the environment that results from the preceding stages of pulse sorting/grouping and characterisation. Identification consists essentially of an ESM radar library and an identifi... View full abstract»

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  • Comparing small object detectors

    Publication Year: 1994, Page(s):4/1 - 4/3
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (188 KB)

    The signal produced by a small object when imaged on a CCD array is spread over several pixels in the array due to device physics and atmospheric effects. It has been typically represented as a raised cosine function. The present paper details a comparison of two potential small object detection networks: (a) the artificial neural network (ANN) and, (b) the least-mean-square (LMS) filter View full abstract»

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  • Classification of multi-spectral remote sensing data with neural networks: a comparative study

    Publication Year: 1994, Page(s):5/1 - 5/2
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (148 KB)

    Satellites or planes generate remote sensing images by simultaneously recording `grey-level' images for a number of wave-bands. The resulting images are usually processed using statistical classifiers to extract features such as roads, built-up areas, vegetation or water. In the present study two types of neural networks, a multi-layer perceptron (MLP) and a Kohonen learning vector quantization (L... View full abstract»

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  • Comparing classical and neural network classification techniques for image feature identification

    Publication Year: 1994, Page(s):1/1 - 1/8
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (476 KB)

    One of the main requirements of an image processing system is the ability to automatically recognise a given object within a scene. Many military systems rely on the use of imagery based upon infra-red (IR) technology. Another requirement is for robustness over a wide range of operating conditions. Another overall consideration in any system is one of processing requirements in terms of speed, cos... View full abstract»

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  • A critical assessment of recurrent artificial neural networks as adaptive equalizers in digital communications

    Publication Year: 1994, Page(s):11/1 - 11/4
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (208 KB)

    A number of neural network structures have previously been applied to the problem of equalization of digital communications channels and view the problem as one of pattern classification rather than one of inverse filtering. The recurrent neural network (RNN) has previously been shown to outperform the conventional linear transversal equalizer structure and has the advantage of requiring a small n... View full abstract»

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  • Techniques for detection and classification of the fetal QRS complex

    Publication Year: 1994, Page(s):10/1 - 10/3
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (216 KB)

    A critical process in both the fetal cardiotocogram (CTG) and fetal electrocardiogram (FECG) analysis is to determine the location of the QRS complex from the raw FECG data. Difficulties arise because the FECG signal is degraded and sometimes totally obscured by noise. Thus, to determine the location of the QRS complexes is a two stage process: pre-processing of the noisy FECG to detect candidate ... View full abstract»

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  • Application of artificial neural networks to medical signal processing

    Publication Year: 1994, Page(s):9/1 - 9/3
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (228 KB)

    The dynamics of human sleep have previously been examined using unsupervised clustering techniques [Roberts and Tarassenko, 1992]. This culminated in the hypothesis that the structure of sleep can be described as a linear combination of three underlying processes. These correspond to the conventional, rule-based stages of wakefulness, REM sleep, and the deepest form of non-REM sleep, stage 4. The ... View full abstract»

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  • Neural networks and texture classification

    Publication Year: 1994, Page(s):6/1 - 6/4
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (232 KB)

    Texture plays an increasingly important role in computer vision. It has found wide application in remote sensing, medical diagnosis, quality control, food inspection and so forth. Research on texture started in the 1970s. The resurgence of research interest and resulting techniques in artificial neural networks gives rise to a new paradigm for texture analysis. The paper presents an application of... View full abstract»

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  • Gesture recognition: an assessment of the performance of recurrent neural networks versus competing techniques

    Publication Year: 1994, Page(s):8/1 - 8/3
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (212 KB)

    A gesture is a motion of the body that contains information (e.g. waving goodbye, beckoning with an index finger, signs in a sign language). There are four classes of gestures; signs (substitutes for spoken language); indications (pointing and showing direction); illustration (conveying ideas such as size and shape); and manipulation (for example making something from virtual clay). The first thre... View full abstract»

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