In this paper two methods are presented. A CNNUM-based method is shown to quantify the displacement of the normal interhemisperic bilateral symmetry line. The method uses a deformable open contouring technique. Anothe...
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In this paper two methods are presented. A CNNUM-based method is shown to quantify the displacement of the normal interhemisperic bilateral symmetry line. The method uses a deformable open contouring technique. Another method has been developed to detect bilateral asymmetries. These methods are implemented on the CNN-UM
Novel types of analogic algorithms, using spatio-temporal CNN (cellular nonlinear/neural networks) operations are introduced. These algorithms make complex decisions in images without reading out the CNN chip. This ma...
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Novel types of analogic algorithms, using spatio-temporal CNN (cellular nonlinear/neural networks) operations are introduced. These algorithms make complex decisions in images without reading out the CNN chip. This makes them extremely time, area, and power effective. Two crucial effects are emphasized: diffusion type templates are applied during a finite time interval and local logic operates within well defined parts (patches) in the image plane. Hence, a new type of pattern recognition algorithm is introduced. The technique is demonstrated on an example. In our example we are dealing with an actual problem: how to avoid the counterfeiting on color copiers.< >
In this paper, experimental results on Cellular neural Network Universal Machine (CNN-UM) chips will be presented. These analogic spatio-temporal visual microprocessors make it possible that one can use nonlinear wave...
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ISBN:
(纸本)0780366859
In this paper, experimental results on Cellular neural Network Universal Machine (CNN-UM) chips will be presented. These analogic spatio-temporal visual microprocessors make it possible that one can use nonlinear waves as the basic kernels of algorithms solving filtering-reconstruction and/or detection-classification problems. Showing output results from series of experiments it will be demonstrated how trigger waves, the simplest nonlinear waves, can constructively be used in a number of important application areas.
This paper presents an extended emulated digital retina model to compute two different retina channels in video real-time. The proposed emulated digital implementation of the two-channel retina model was compared to t...
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This paper presents an extended emulated digital retina model to compute two different retina channels in video real-time. The proposed emulated digital implementation of the two-channel retina model was compared to the previously developed single channel model from three different points of view: processing speed, number of physical cells and accuracy. A real-time test environment with camera input and display output is going to be set up to analyze the retina model implementation on emulated digital CNN (Cellular neural/Nonlinear Network) model by using a 6M gate equivalent FPGA (Field Programmable Gate Array).
A method for the path-control of automated guided vehicles (AGVs) in computer integrated manufacturing (CIM) systems that combines the flexibility and easy installation of optical methods with the simplicity and robus...
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A method for the path-control of automated guided vehicles (AGVs) in computer integrated manufacturing (CIM) systems that combines the flexibility and easy installation of optical methods with the simplicity and robustness of the inductive method is proposed. Using a new computing paradigm, the cellular neural network (CNN), and a related device, the VLSI CNN chip, a very high speed solution that is less expensive than the conventional methods can be achieved. This AGV control complies with the requirements of CIM systems. Further advantages of the proposed system are as follows: fault tolerance and the ability to give instructions along the path, and the use of a simple local control.< >
In this paper a spatio-temporal analogic cellular neural network (CNN) algorithm is designed for front-end filtering, segmentation and object recognition. First, a generalized segmentation strategy is presented based ...
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In this paper a spatio-temporal analogic cellular neural network (CNN) algorithm is designed for front-end filtering, segmentation and object recognition. First, a generalized segmentation strategy is presented based on various diffusion models. Both PDE and non-PDE related schemes are discussed and their VLSI complexity is analyzed. In classification (object recognition) a CNN implementation of the autowave metric, a "nonlinear" variant of the Hausdorff metric, is used. This approach turned out to be superior compared to some other classification methods. A number of tests have been completed within the so-called "bubble/debris" segmentation experiments using original and artificial gray-scale images.
This paper is concerned with the invertibility of parallel operations definable on two-dimensional binary arrays by means of a local Boolean function. In this way, it contributes to the theory of cellular automata and...
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