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Neural network edge detection-successes and failures

Neural network edge detection-successes and failures

引用

iee colloquium on applications of Neural Networks to Signal processing (Digest No. 1994/248)

作者： I.A. Hunter J.J. Soraghan Signal Processing Division University of Strathclyde Glasgow UK

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 robustness of the edge detection in noisy and incomplete images. Throughout the paper neural network edge detection is illustrated by the left ventricle boundary extraction problem in echocardiographic images.

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Mapping image processing algorithms onto abstract multiprocessors

Mapping image processing algorithms onto abstract multiproce...

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iee colloquium on Mathematical Modelling and Simulation of Industrial and Economic Processes

作者： I. Erenyi Z. Fazekas KFKI Research Institute of Measurement and Computing Techniques Budapest Hungary

The need for parallel computing technology is rapidly growing in several image processing applications, such as industrial quality control, bio-medical imaging, traffic control automation. Most of the image processing algorithms are inherently computationally intensive and may require vast computing power if strict time-constraints are posed. The spreading of parallel image processing techniques and systems has been driven not only by the afore-mentioned need, but the inherent parallel nature of many image processing algorithms has also eased this evolution. The execution characteristics of a certain parallel algorithm on a given architecture heavily depends on the 'mutual conformance' of the mentioned algorithm and the architecture pair. Two algorithms with similar sequential performance may behave very differently in a parallel environment. In sequential algorithms the complexity is expressed in terms of operations and storage. In parallel environments these terms are not adequate for characterizing the computing efficiency - fewer operations does not directly mean shorter execution time since there is a definite overhead involved due to availability of resources and communication between processors.< >

关键词： image processing Parallel algorithms Parallel architectures

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Developments in 'smart' custom image sensors at the University of Brighton

Developments in 'smart' custom image sensors at the Universi...

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iee colloquium on Integrated Imaging Sensors and processing

作者： G.J. Awcock M.T. Rigby Applied Image Proccessing Research Unit University of Brighton UK

Any image processing that can be performed within the sensor itself has the potential to dramatically reduce the communication and processing workload of the host controller. Thus onchip processing has an important role to play in the viability of visual servoing applications. When coupled with the increasing accessibility of custom VLSI design this makes the development of 'smart' image sensing architectures attractive. Investigations into a proprietary optic DRAM image sensor suggested a sensing mechanism based upon selective sensitisation of photosites that would allow edge-detection of binary images to be performed in the focal plane of the sensor. This mechanism was chosen as the vehicle for development of a prototype 'smart' custom VLSI image sensor. A 32x32 array featuring this unique unit cell architecture was implemented in a 2 micron ES2 CMOS process. Each unit cell was 80.5 microns square and featured a diode photosite which occupied 24% of the total area, the remainder being concerned with buffering, binarisation and edge-extraction hardware. The requirement for support circuitry was met by a rack containing three Eurocards with a microprocessor.< >

关键词： DRAM chips image edge analysis image sensors Intelligent sensors Very-large-scale integration

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Comparing classical and neural network classification techniques for image feature identification

Comparing classical and neural network classification techni...

引用

iee colloquium on applications of Neural Networks to Signal processing (Digest No. 1994/248)

作者： R. Milliken Systems Analysis and Assessment Department Dynamic Division British AeroSpace Public Limited Company Bristol UK

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, cost and physical size; military systems often impose severe constraints on the physical size. A simple approach to recognising objects is to segment the image to form a set of regions, where one or more of the regions is representative of the object. This step is then followed by the computation of a set of rudimentary numerical metrics for each object region indicated by the segmentation process. For each region, the set of metrics form a feature vector whose values are, in some sense, representative of the nature of the region. Ideally the elements of the feature vector associated with a object region of one class will significantly differ from the feature vectors describing regions from other classes. So the objective is to classify the object region given the contents of the feature vector computed for that region. Traditionally, a typical feature classification process made use of well established algorithms based upon linear discriminant or nearest neighbour techniques. More recently neural network classification techniques have emerged and the objective of the present paper is to perform some initial simple experiments to compare the traditional and more contemporary classification techniques.

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

How do neural networks compare with standard filters for ima...

引用

iee colloquium on applications of Neural Networks to Signal processing (Digest No. 1994/248)

作者： D. Greenhill E.R. Davies Sch. of Comput. Sci. & Electron. Syst. Kingston Univ. Kingston-upon-Thames UK Department of Physics Royal Holloway University of London Surrey UK

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 learning rules-thereby emulating biological systems and providing the potential to improve on conventional algorithmic approaches. In fact, there are possibilities that ANNs might perform noise suppression more effectively than conventional approaches, not least in adapting to specific types of noise, and in eliminating the image distortion which is a characteristic of the widely used median filter. In this context it is worth noting that the median filter has no adjustable parameters other than neighbourhood size, so ANNs definitely have the potential for improving on its performance-and also on that of alternative types of filter. The paper describes the authors' own studies of the problem.

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iee colloquium on 'binary image processing - techniques and applications' (Digest No.059)

IEE Colloquium on 'Binary Image Processing - Techniques and ...

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iee colloquium on binary image processing - techniques and applications

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Implications of teaching image processing without reference to computer vision

Implications of teaching image processing without reference ...

引用

iee colloquium on Teaching of Digital image processing in Universities

作者： G. Roberts S. Lloyd Department of Mathematics and Computing University of Glamorgan UK

Two modules each lasting one semester, and running consecutively, are described in outline. The image processing module proceeds from local and global spatial domain techniques, convolution and Fourier transforms, to filtering and other frequency domain techniques. The Computer Vision module deals with binary image geometry and topology and pattern recognition (extended to neural nets). It is argued that combining the two into a single course could improve the learning experience.< >

关键词： Computer science education image processing

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Specification, design and implementation of a digital binary image processing ASIC

Specification, design and implementation of a digital binary...

引用

iee colloquium on applications Specific Integrated Circuits for Digital Signal processing

作者： E. O'Rourke J.B. Foley Dept. of Microelectron. & Electr. Eng. Trinity Coll. Dublin Ireland Department of Microelectronic & Elecrical Engineering Trinity College Dublin Dublin Ireland

A novel binary image processing ASIC is described. The architecture of this ASIC is particularly well suited to high-speed nonlinear functions. The ASICs function is to filter an incoming data stream which represents an image of width 512 pixels. The data is initially thresholded to a binary data stream. This resulting data stream is then fed through a series of line delays, to effect a 3 by 3 pixel neighbourhood, or window, scanning across the threshold image. The nine binary pixel values in this neighbourhood are used as an address into an on-chip SRAM, which is used as a look up table (LUT) and memory contents at this address serve as output for the device. For cascading purposes, the window contents and the thresholded image are available as output from the device. This device has applications in image pre-processing and filtering in, for example, the area of machine vision. The user may tune the response of the filter for most image pre-processing tasks. Several ICs can be cascaded together to effect any size or shape of window and various binary filters are implemented by appropriate programming of the SRAM. ASIC design has been completed and the device is currently being fabricated.< >

关键词： Application specific integrated circuits Machine vision Table lookup

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Approaches to static background identification and removal

Approaches to static background identification and removal

引用

iee colloquium on image processing for Transport applications

作者： A.J. Shelley N.L. Seed Sheffield Univ. UK

A common feature of many image processing applications is the classification of the data into regions of interest, often called the foreground, and unwanted regions referred to as being the background. An example of this process is the extraction of the steady-state component from a video sequence. Subtraction of this static component yields images containing only the desired foreground data. This is a common operation in real-time image processing performed within widely varying applications. As image processing develops, both in complexity and application, the role of background extraction algorithms in the pre-processing of raw data is increasing. The reasons for using these techniques can be categorised loosely into three key areas: bandwidth reduction, cost, and complexity reduction.< >

关键词： image processing

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Medical applications of mathematical morphology

Medical applications of mathematical morphology

引用

iee colloquium on Morphological and Nonlinear image processing techniques

作者： G.K. Matsopoulos S. Marshall University of Strathclyde UK

Nonlinear filters have become very popular tools in signal and image processing because of their attractive properties. Their characteristics differentiate them strongly from the existing linear filters in may areas of image processing such as noise reduction, edge detection, segmentation, etc. A very important class of nonlinear digital signal/image processing results from the concepts of mathematical morphology and is known as morphological filtering (Serra, 1982) mathematical morphology is a new approach to signal/image analysis, using nonlinear pictorial transformations and functionals derived from set theory and integral geometry. As a result, morphological filters are powerful tools for geometrical shape analysis and description and their applications in image processing and analysis are numerous. Areas of applications include shape smoothing, texture analysis, automated industrial inspection, enhancement and noise suppression. The present paper investigates the use of various existing and new morphological operations for filtering and segmenting real medical data with the purpose of assisting diagnosis.< >

关键词： Digital filters Filtering image segmentation Morphological operations Biomedical image processing Medical diagnosis Prediction methods

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