By Tony Allen, Richard Ellis, Miltos Petridis
The papers during this quantity are the refereed software papers provided at AI-2008, the Twenty-eighth SGAI overseas convention on cutting edge recommendations and functions of man-made Intelligence, held in Cambridge in December 2008. They current new and leading edge purposes of synthetic intelligence throughout quite a lot of domain names; divided into sections on desktop studying, internet applied sciences, clever platforms and AI in future health Care. the quantity additionally contains the textual content of brief papers offered as posters on the convention. this is often the 16th quantity within the program and options sequence, which bargains an annual photograph of the most recent purposes of man-made intelligence throughout undefined, trade, schooling and executive. The Technical flow papers are released as a significant other quantity lower than the name examine and improvement in clever platforms XXV.
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Additional resources for Applications and Innovations in Intelligent Systems XVI: Proceedings of AI-2008, The Twenty-eighth SGAI International Conference on Innovative Techniques ... of Artificial Intelligence (v. 16)
It was tested on the MNIST image database of Computer Vision System for Manufacturing of Micro Workpieces 23 handwritten digits and ORL image database of faces, and showed good results - . The structure of PCNC is presented in Figure 5. The image is input to the feature extractor. The extracted features are applied to the encoder input. The encoder produces the output binary vector of large dimension, which is to be presented to the input of one-layer neural classifier. The classifier output represents the recognized class.
The permutations in horizontal (X) and vertical (Y) directions are different permutations. In Figure 8 an example of permutation pattern for horizontal (X) direction is presented. Computer Vision System for Manufacturing of Micro Workpieces 25 Figure 8 Permutation pattern for horizontal (X) direction Same feature can have different locations on the image. Such feature will have different binary code for each location. For two locations of the same feature the binary codes must be strongly correlated if the distance between the feature locations is small and must be weakly correlated if the distance is large.
The associative neural layer A and the reaction neural layer R are the same as in the PCNC neural classifier. The training rules for connections between the layers A and R and the recognition procedure are also the same. The coding procedure used in the LIRA neural classifier is the following. We connect an A-layer neuron to S-layer neurons through the neurons of the intermediate neural layer I (Figure 9). The input of each I-layer neuron is connected to one neuron of the S-layer and the output is connected to the input of one neuron of the A-layer.
Applications and Innovations in Intelligent Systems XVI: Proceedings of AI-2008, The Twenty-eighth SGAI International Conference on Innovative Techniques ... of Artificial Intelligence (v. 16) by Tony Allen, Richard Ellis, Miltos Petridis