artificial retina chips which can simultaneously sense and process the real world images are described. Device concept, structure, fundamental performance, operation principle, processing functions are described. Appl...
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artificial retina chips which can simultaneously sense and process the real world images are described. Device concept, structure, fundamental performance, operation principle, processing functions are described. applications including the interactive games by gesture-input are also introduced.
Two observations are worthy of note. First, the experienced optical engineer can usually determine, sometimes partly subjectively, the positions of phase wraps in the image. This suggests that the information necessar...
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Two observations are worthy of note. First, the experienced optical engineer can usually determine, sometimes partly subjectively, the positions of phase wraps in the image. This suggests that the information necessary to identify phase wraps does exist. Second, to date, no universally applicable technique for phase wrap detection is available. Indeed, it may be that there is no straight forward analytical method that can be used. The concept of a neuron was first postulated by McCulloch and Pitts (1943) and a neural network provides a mechanism by which a machine can learn from experience. The foregoing discussion suggests that neural network technology may be suitable for addressing the phase unwrapping problem. This paper describes preliminary work on the use of neuralnetworks to identify phase wraps earlier in the phase measuring process, prior to the calculation of wrapped phase.
artificialneuralnetworks have evolved from their biologically inspired roots to a well established means to solve a broad spectrum of engineering problems. Their embedding into modern statistics has provided the nec...
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artificialneuralnetworks have evolved from their biologically inspired roots to a well established means to solve a broad spectrum of engineering problems. Their embedding into modern statistics has provided the necessary theoretical foundation for challenging engineering tasks, such as advanced real time image and signal processing. These are exemplary demonstrations for the applicability of this approach to complex information processing. However, the large number of applications must not obscure the fact that there are some major unsolved problems concerning neuralnetworks. There are still no satisfactorily constructive ways to determine the optimal structure (elements as well as organization) or the learning and evaluation dynamics. Ongoing research addresses these problems. In addition to pursuing this direction, one can ask what other lessons we can learn from biology concerning complex information processing. Our goal is to sketch a possible pathway from neuralnetworks to more comprehensive neural strategies.
The implementation of artificialneuralnetworks (ANN) in various aspects is increasing day by day. One of the major applications is image compression. Here an algorithm has been developed for use in the encoding of c...
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The implementation of artificialneuralnetworks (ANN) in various aspects is increasing day by day. One of the major applications is image compression. Here an algorithm has been developed for use in the encoding of computed tomography (CT) image sequences. The method is based on application of an ANN distributed system which classifies all possible m/spl times/m (here 4/spl times/4) blocks into a smaller number well distinct classes of vectors. In an extension of the Kohonen self organising net called the frequency sensitive competitive learning (FSCL) algorithm, the required time for obtaining an ignorable error will depend on both the distortion and the number of iterations which, are more or less equal for all units. The application of an ANN to vector quantisation (VQ) stems from the concept that in the usual methods the error between each input pattern and the pattern of the codebook (word), is calculated without regarding the weight of each pixel value in the entire pattern. A proper ANN exploits this concept in an efficient classification of various patterns in an image and/or sequence of images. This significantly decreases the artefact, such as the blocking effect which normally appears in ordinary VQ reconstructed images at a low bit rate. In the case of sequences, interframes correlation is exploited in the provision of a common codebook for highly correlated frames. Further, the redundancy is decreased by optimal decomposition of the sequence into the most correlated subsequences.
Multi-spectral imagery plays a significant role in Earth resource survey and evaluation and has been an essential part of terrestrial and planetary exploration. Multi-spectral imaging sensors-such as the Airborne Visi...
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Multi-spectral imagery plays a significant role in Earth resource survey and evaluation and has been an essential part of terrestrial and planetary exploration. Multi-spectral imaging sensors-such as the Airborne Visible/Infrared Imaging spectrometer (AVIRIS), which images in 224 wavebands in the range 0.4-2.45 /spl mu/m giving 224 images, each of 614/spl times/512 pixels, and GER which images in 63 wavebands-are now routinely in use. With this increasing utilization of imaging spectrometer data and the constant improvement in instrument resolution and spectral discrimination, automatic identification of spectral signatures emanating from this imagery would be an invaluable facility as a precursor to classifying each pixel. Existing methods for identifying constituent spectra typically rely on spectra that are selected either manually or involve manual intervention. The aim of the work described in this paper is to devise techniques suitable for fully-automatic analysis. Two techniques are described. The first is artificialneuralnetworks and the second is singular value decomposition (SVD). The ability of these methods to distinguish between up to 160 different spectra is assessed, as is their stability in the presence of noise and their capacity to identify correctly combinations of spectra, i.e., where a test spectrum is made up of more than one spectrum from a reference database.
Optical tape offers the greatest volumetric storage density of any commercially available storage medium, and thus is a cost effective alternative to traditional forms of archival storage. An optical tape recorder uti...
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Optical tape offers the greatest volumetric storage density of any commercially available storage medium, and thus is a cost effective alternative to traditional forms of archival storage. An optical tape recorder utilizing linear scanning with a single reel storage capacity of one terabyte has been commercially available for some time. We utilize a two-dimensional read-out based on linear scanning which provides a page oriented format similar to that of a holographic memory. Two-dimensional optical memories promise to provide high storage capacities, making them ideal candidates for archival storage in future computing applications. This extension of channel capability has the potential to increase the read bandwidth of the storage system allowing faster retrieval of a larger file space than traditional archival storage. Data recovery with imageprocessing techniques and with an artificialneural network are discussed.
In this paper, we present a neural network approach for scene analysis: detection of human beings in images. To solve this problem, a precise classification system is required, with adaptation systems based on data pr...
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ISBN:
(纸本)0819420387
In this paper, we present a neural network approach for scene analysis: detection of human beings in images. To solve this problem, a precise classification system is required, with adaptation systems based on data processing. These systems must be largely parallel, which is why neuralnetworks have been chosen. The first part of this text is a brief introduction to neuralnetworks and their applications. The second part is a description of the image base composed for experiments and the low-level processing used, then we detail the method used to extract the texture feature of images. The third part describes the Bayesian method and its application to our problem. Part four shows the association of these texture processes with the neural network for identification of human beings. Finally, we conclude with the validity of the method and its future applications.
In this paper we propose a new method of image quality assessment for the evaluation of the block distortion through artificialneuralnetworks (ANNs). The approach is new and intends to address the problem of the ass...
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ISBN:
(纸本)0819420387
In this paper we propose a new method of image quality assessment for the evaluation of the block distortion through artificialneuralnetworks (ANNs). The approach is new and intends to address the problem of the assessment of the visual quality of compressed images from an original point of view. ANNs in particular are applied in order to detect the presence of blocking errors inside pre-processed pictures. To this purpose, a new local blocking distortion parameter is introduced. Experiments and simulations, even if very preliminary, have confirmed the interest of the proposed approach. A complete formalization of the problem also is presented.
A method that makes the Hopfield neural network perform the point pattern relaxation matching process is proposed. An advantage of this is that the relaxation matching process can be performed in real time with the ma...
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ISBN:
(纸本)0819420387
A method that makes the Hopfield neural network perform the point pattern relaxation matching process is proposed. An advantage of this is that the relaxation matching process can be performed in real time with the massively parallel capability to process information of the neural network. Experimental results with large simulated images prove the effectiveness and feasibility to perform point relaxation matching by the Hopfield neural network.
This work describes an alternative technique for hardware and software implementation of RAM-based Boolean neuralnetworks, which describes neurons using the VHDL language. An example of application consisting of the ...
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This work describes an alternative technique for hardware and software implementation of RAM-based Boolean neuralnetworks, which describes neurons using the VHDL language. An example of application consisting of the classification problem of the British mail scanned address is attended with a RAM architecture presenting (340 x 12)-input neurons. The weights of each neuron are represented by its truth table and described using simple logic gates (AND, OR, and NOT), aiming to make possible the network logic minimisation and its hardware implementation by the ALTERA MAX + PLUS ii fast prototyping package (Altera, 1992). The developed software tool allows the specification and training of the network. Then, its VHDL description is generated to be interpreted and minimised by the ALTERA EPLD design system. If it is not necessary to have high-speed processing or if pre-processing phases are needed, the ANN can be implemented in software. The software strategy makes use of the direct translation of the VHDL description into a simplified C language code. Once the user has specified and taught the network, this approach makes possible automatic prototyping of RAM neuralnetworks in software and hardware.
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