149 research outputs found
Adaptive Image Restoration: Perception Based Neural Nework Models and Algorithms.
Abstract This thesis describes research into the field of image restoration. Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. Two primary concepts within this field have been investigated. The first concept is the use of a Hopfield neural network to implement the constrained least square error method of image restoration. In this thesis, the author reviews previous neural network restoration algorithms in the literature and builds on these algorithms to develop a new faster version of the Hopfield neural network algorithm for image restoration. The versatility of the neural network approach is then extended by the author to deal with the cases of spatially variant distortion and adaptive regularisation. It is found that using the Hopfield-based neural network approach, an image suffering spatially variant degradation can be accurately restored without a substantial penalty in restoration time. In addition, the adaptive regularisation restoration technique presented in this thesis is shown to produce superior results when compared to non-adaptive techniques and is particularly effective when applied to the difficult, yet important, problem of semi-blind deconvolution. The second concept investigated in this thesis, is the difficult problem of incorporating concepts involved in human visual perception into image restoration techniques. In this thesis, the author develops a novel image error measure which compares two images based on the differences between local regional statistics rather than pixel level differences. This measure more closely corresponds to the way humans perceive the differences between two images. Two restoration algorithms are developed by the author based on versions of the novel image error measure. It is shown that the algorithms which utilise this error measure have improved performance and produce visually more pleasing images in the cases of colour and grayscale images under high noise conditions. Most importantly, the perception based algorithms are shown to be extremely tolerant of faults in the restoration algorithm and hence are very robust. A number of experiments have been performed to demonstrate the performance of the various algorithms presented
Data comparison schemes for Pattern Recognition in Digital Images using Fractals
Pattern recognition in digital images is a common problem with application in
remote sensing, electron microscopy, medical imaging, seismic imaging and
astrophysics for example. Although this subject has been researched for over
twenty years there is still no general solution which can be compared with the
human cognitive system in which a pattern can be recognised subject to
arbitrary orientation and scale.
The application of Artificial Neural Networks can in principle provide a very
general solution providing suitable training schemes are implemented.
However, this approach raises some major issues in practice. First, the CPU
time required to train an ANN for a grey level or colour image can be very
large especially if the object has a complex structure with no clear geometrical
features such as those that arise in remote sensing applications. Secondly,
both the core and file space memory required to represent large images and
their associated data tasks leads to a number of problems in which the use of
virtual memory is paramount.
The primary goal of this research has been to assess methods of image data
compression for pattern recognition using a range of different compression
methods. In particular, this research has resulted in the design and
implementation of a new algorithm for general pattern recognition based on
the use of fractal image compression.
This approach has for the first time allowed the pattern recognition problem to
be solved in a way that is invariant of rotation and scale. It allows both ANNs
and correlation to be used subject to appropriate pre-and post-processing
techniques for digital image processing on aspect for which a dedicated
programmer's work bench has been developed using X-Designer
Intelligent X-ray imaging inspection system for the food industry.
The inspection process of a product is an important stage of a modern
production factory. This research presents a generic X-ray imaging inspection system
with application for the detection of foreign bodies in a meat product for the food
industry. The most important modules in the system are the image processing module
and the high-level detection system.
This research discusses the use of neural networks for image processing and
fuzzy-logic for the detection of potential foreign bodies found in x-ray images of
chicken breast meat after the de-boning process. The meat product is passed under a
solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a
low- and a high energy image). A series of image processing operations are applied to
the acquired image (pre-processing, noise removal, contrast enhancement). The most
important step of the image processing is the segmentation of the image into meaningful
objects. The segmentation task is a difficult one due to the lack of clarity of the acquired
X-ray images and the resulting segmented image represents not only correctly identified
foreign bodies but also areas caused by overlapping muscle regions in the meat which
appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural
network architecture was proposed for the segmentation of a X-ray dual-band image. A
number of image processing measurements were made on each object (geometrical and
grey-level based statistical features) and these features were used as the input into a
fuzzy logic based high-level detection system whose function was to differentiate
between bones and non-bone segmented regions. The results show that system's
performance is considerably improved over non-fuzzy or crisp methods. Possible noise
affecting the system is also investigated.
The proposed system proved to be robust and flexible while achieving a high
level of performance. Furthermore, it is possible to use the same approach when
analysing images from other applications areas from the automotive industry to
medicine
Intelligent X-ray imaging inspection system for the food industry.
The inspection process of a product is an important stage of a modern
production factory. This research presents a generic X-ray imaging inspection system
with application for the detection of foreign bodies in a meat product for the food
industry. The most important modules in the system are the image processing module
and the high-level detection system.
This research discusses the use of neural networks for image processing and
fuzzy-logic for the detection of potential foreign bodies found in x-ray images of
chicken breast meat after the de-boning process. The meat product is passed under a
solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a
low- and a high energy image). A series of image processing operations are applied to
the acquired image (pre-processing, noise removal, contrast enhancement). The most
important step of the image processing is the segmentation of the image into meaningful
objects. The segmentation task is a difficult one due to the lack of clarity of the acquired
X-ray images and the resulting segmented image represents not only correctly identified
foreign bodies but also areas caused by overlapping muscle regions in the meat which
appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural
network architecture was proposed for the segmentation of a X-ray dual-band image. A
number of image processing measurements were made on each object (geometrical and
grey-level based statistical features) and these features were used as the input into a
fuzzy logic based high-level detection system whose function was to differentiate
between bones and non-bone segmented regions. The results show that system's
performance is considerably improved over non-fuzzy or crisp methods. Possible noise
affecting the system is also investigated.
The proposed system proved to be robust and flexible while achieving a high
level of performance. Furthermore, it is possible to use the same approach when
analysing images from other applications areas from the automotive industry to
medicine
34th Midwest Symposium on Circuits and Systems-Final Program
Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society.
Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi
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