Real-life applications of neural networks require a high degree of success, usability and reliability. imageprocessing has an importance for both data preparation and human vision to increase the success and reliabil...
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ISBN:
(纸本)354029032X
Real-life applications of neural networks require a high degree of success, usability and reliability. imageprocessing has an importance for both data preparation and human vision to increase the success and reliability of pattern recognition applications. The combination of both imageprocessing and neural networks can provide sufficient and robust solutions to problems where intelligent recognition is required. This paper presents an implementation of neural networks for the recognition of various banknotes. One combined neural network will be trained to recognize all the banknotes of the Turkish Lira and the Cyprus Pound;as they are the main currencies used in Cyprus. The flexibility, usability and reliability of this Intelligent Banknote Identification System (IBIS) will be shown through the results and a comparison will be drawn between using separate neural networks or a combined neural network for each currency.
A wavelet based method of noise reduction has been tested for mammography using computer-simulated images for which the truth is known exactly. This method is based on comparing two images at different scales, using a...
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ISBN:
(纸本)0819457191
A wavelet based method of noise reduction has been tested for mammography using computer-simulated images for which the truth is known exactly. This method is based on comparing two images at different scales, using a cross-correlation-function as a measure of similarity to define the image modifications in the wavelet domain. The computer-simulated images were calculated for noise-free primary radiation using a quasi-realistic voxel phantom. Two images corresponding to slightly different geometry were produced. Gaussian noise was added with a mean value of zero and a standard deviation equal to 0.25% to 10% of the actual pixel value to simulate quantum noise with a certain level. The added noise could be reduced by more than 70% using the proposed method without any noticeable corruption of the structures for 4% added noise. The results indicate that it is possible to save 50% dose in mammography by producing C, two images (each 25% of the dose for a standard mammogram). Additionally, a reduction or even a removal of the anatomical noise might be possible and therefore better detection rates of breast cancer in mammography might be achievable.
Neural network based imageprocessing algorithms present numerous advantages due to their supervised adjustable weight and bias coefficients. Among various neural network architectures, dynamic neural networks, Hopfie...
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ISBN:
(纸本)0819458031
Neural network based imageprocessing algorithms present numerous advantages due to their supervised adjustable weight and bias coefficients. Among various neural network architectures, dynamic neural networks, Hopfield and Cellular neural networks have been found inherently suitable for filtering applications. These kind of neural networks present two important features;supervised learnable and optimization properties. Using these properties, dynamic neural filtering technique has been developed based on Hopfield neural networks'. The filtering structure involves adjustable a filter mask and 2D convolution operation instead of weight matrix operations. To improve the supervised training properties, Widrow-recurrent learning algorithm has been proposed in this paper. Since the proposed learning algorithm requires less computation, consumption time in the training stage has been decreased considerably compared to previous reported supervised techniques for dynamic neural filtering.
The computational requirements in Neurophysiology are increasing with the development of new analysis methods. The resources the GRID has to offer are ideally suited for this complexprocessing. A practical implementa...
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Coherent optical signal processors, due to their ability to process and relay information in two dimensions, have been receiving increasing attention in recent years. These systems involve a coherent field being propa...
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ISBN:
(纸本)0819458120
Coherent optical signal processors, due to their ability to process and relay information in two dimensions, have been receiving increasing attention in recent years. These systems involve a coherent field being propagated through some bulk optical system consisting of thin lenses and sections of free space (such paraxial systems being described mathematically using the Linear Canonical Transformation). A Spatial Light Modulator (SLM) may be used to modulate the input digital data onto a coherent wave-field as well as to modulate the amplitude and/or phase of the complex wave-field at any desired plane. The complex field (amplitude and phase) at any desired plane may be recorded quantitatively using a CCD camera, using digital holographic techniques allowing the further processing of data digitally. Such hybrid optoelectronic systems have applications for 2D and 3D data processing covering fields as diverse as data storage, data security and pattern recognition. But devices such as SLMs and CCD cameras can represent only discrete levels of data necessitating a quantisation of continuous valued analog information. In this paper, we take the example of an optical system used to encrypt 2D and 3D data and evaluate the effect of the finite discrete levels of an SLM on the encryption/decryption process.
This paper presents a development platform for real-time imageprocessing based on the ADSP-BF533 Blackfin processor and the MicroC/OS-II real-time operating system (RTOS). MicroC/OS-II is a completely portable, ROMab...
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ISBN:
(纸本)0819458082
This paper presents a development platform for real-time imageprocessing based on the ADSP-BF533 Blackfin processor and the MicroC/OS-II real-time operating system (RTOS). MicroC/OS-II is a completely portable, ROMable, pre-emptive, real-time kernel. The Blackfin Digital signal Processors (DSPs), incorporating the Analog Devices/Intel Micro signal Architecture (MSA), are a broad family of 16-bit fixed-point products with a dual Multiply Accumulate (MAC) core. In addition, they have a rich instruction set with variable instruction length and both DSP and MCU functionality thus making them ideal for media based applications. Using the MicroC/OS-II for task scheduling and management, the proposed system can capture and process raw RGB data from any standard 8-bit greyscale image sensor in soft real-time and then display the processed result using a simple PC graphical user interface (GUI). Additionally, the GUI allows configuration of the image capture rate and the system and core DSP clock rates thereby allowing connectivity to a selection of image sensors and memory devices. The GUI also allows selection from a set of imageprocessing algorithms based in the embedded operating system.
We study an image denoising approach the core of which is a locally adaptive estimation of the probability that a given coefficient contains a significant noise-free component, which we call "signal of interest&q...
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We study an image denoising approach the core of which is a locally adaptive estimation of the probability that a given coefficient contains a significant noise-free component, which we call "signal of interest". We motivate this approach within the minimum mean squared error criterion and develop and analyze different locally adaptive versions of this method for color and for multispectral images in remote sensing. For color images, we study two different approaches: (i) using a joint spatial/spectral activity indicator in the RGB color space and (ii) componentwise spatially adaptive denoising in a luminance-chrominance space. We demonstrate and discuss the advantages of both of these approaches in different scenarios. We also compare the analyzed method to other recent wavelet domain denoisers for multiband data both on color and on multispectral images.
An algorithm for fine-grained scalability (FGS) in H.264/AVC is presented. This algorithm is included in Working Draft 2 of the H.264/AVC scalable extension. Encoding block coefficients in a cyclical manner places &qu...
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ISBN:
(纸本)0780392434
An algorithm for fine-grained scalability (FGS) in H.264/AVC is presented. This algorithm is included in Working Draft 2 of the H.264/AVC scalable extension. Encoding block coefficients in a cyclical manner places "higher-value'" bits earlier in the bit stream. If the bit stream is truncated, higher-value bits are therefore more likely to be retained. By arranging the bit stream in such a manner, a PSNR gain of up to 0.5 dB can be achieved versus non-cyclical coding, depending upon the sequence and bit stream truncation point.
Digital holography is an inherently three-dimensional (3D) technique for the capture of real-world objects. Many existing 3D imaging and processing techniques are based on the explicit combination of several 2D perspe...
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ISBN:
(纸本)0819458120
Digital holography is an inherently three-dimensional (3D) technique for the capture of real-world objects. Many existing 3D imaging and processing techniques are based on the explicit combination of several 2D perspectives (or light stripes, etc.) through digital imageprocessing. The advantage of recording a hologram is that multiple 2D perspectives can be optically combined in parallel, and in a constant number of steps independent of the hologram size. Although holography and its capabilities have been known for many decades, it is only very recently that digital holography has been practically investigated due to the recent development of megapixel digital sensors with sufficient spatial resolution and dynamic range. The applications of digital holography could include 3D television, virtual reality, and medical imaging. If these applications are realised, compression standards will have to be defined. We outline the techniques that have been proposed to date for the compression of digital hologram data and show that, they are comparable to the performance of what in communication theory is known as optimal signal quantisation. We adapt the optimal signal quantisation technique to complex-valued 2D signals. The technique relies on knowledge of the histograms of real and imaginary values in the digital holograms. Our digital holograms of 3D objects are captured using phase-shift interferometry.
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