29,705 research outputs found

    Face Recognition Methods Based on Feedforward Neural Networks, Principal Component Analysis and Self-Organizing Map

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    In this contribution, human face as biometric is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and RBF (radial basis function) networks, and to a system using MLP as a feature extractor and MLP and RBF networks in the role of classifier. Also a two-stage method for face recognition is presented, in which Kohonen self-organizing map is used as a feature extractor. MLP and RBF network are used as classifiers. In order to obtain deeper insight into presented methods, also visualizations of internal representation of input data obtained by neural networks are presented

    Artificial Neural Networks for Automated Quality Control of Textile Seams

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    Bahlmann C, Heidemann G, Ritter H. Artificial Neural Networks for Automated Quality Control of Textile Seams. Pattern Recognition. 1999;32(6):1049-1060.We present a method for an automated quality control of textile seams, which is aimed to establish a standardized quality measure and to lower coals in manufacturing. The system consists of a suitable image acquisition setup, an algorithm for locating the seam, a feature extraction stage and a neural network of the self-organizing map type for feature classification. A procedure to select an optimized feature set carrying the information relevant for classification is described. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd, All rights reserved

    Clustering Of Complex Shaped Data Sets Via Kohonen Maps And Mathematical Morphology

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    Clustering is the process of discovering groups within the data, based on similarities, with a minimal, if any, knowledge of their structure. The self-organizing (or Kohonen) map (SOM) is one of the best known neural network algorithms. It has been widely studied as a software tool for visualization of high-dimensional data. Important features include information compression while preserving topological and metric relationship of the primary data items. Although Kohonen maps had been applied for clustering data, usually the researcher sets the number of neurons equal to the expected number of clusters, or manually segments a two-dimensional map using some a priori knowledge of the data. This paper proposes techniques for automatic partitioning and labeling SOM networks in clusters of neurons that may be used to represent the data clusters. Mathematical morphology operations, such as watershed, are performed on the U-matrix, which is a neuron-distance image. The direct application of watershed leads to an oversegmented image. It is used markers to identify significant clusters and homotopy modification to suppress the others. Markers are automatically found by performing a multi-level scan of connected regions of the U-matrix. Each cluster of neurons is a sub-graph that defines, in the input space, complex and nonparametric geometries which approximately describes the shape of the clusters. The process of map partitioning is extended recursively. Each cluster of neurons gives rise to a new map, which are trained with the subset of data that were classified to it. The algorithm produces dynamically a hierarchical tree of maps, which explains the cluster's structure in levels of granularity. The distributed and multiple prototypes cluster representation enables the discoveries of clusters even in the case when we have two or more non-separable pattern classes.43841627Vinod, V.V., Chaudhury, S., Mukherjee, J., Ghose, S., A connectionist approach for clustering with applications in image analysis (1994) IEEE Trans. Systems, Man & Cybernetics, 24 (3), pp. 356-384Costa, J.A.F., (1999) Automatic classification and data analysis by self-organizing neural networks, , Ph.D. Thesis. State University of Campinas, SP, BrazilEveritt, B.S., (1993) Cluster Analysis, , Wiley: New YorkKaufman, L., Rousseeuw, P., (1990) Finding Groups in Data: An Introduction to Cluster Analysis, , Wiley: New YorkSu, M.-C., Declaris, N., Liu, T.-K., Application of neural networks in cluster analysis (1997) Proc. of the 1997 IEEE Intl. Conf. on Systems, Man, and Cybernetics, pp. 1-6Kothari, R., Pitts, D., On finding the number of clusters (1999) Pattern Recognition Letters, 20, pp. 405-416Hardy, A., (1996) On the number of clusters. Computational Statistics and Data Analysis, 23, pp. 83-96Jain, A.K., Murty, M.N., Flynn, P.J., Data clustering: A review (1999) ACM Computing Surveys, 31 (3), pp. 264-323Ball, G., Hall, D., A clustering technique for summarizing multivariate data (1967) Behavioral Science, 12, pp. 153-155Bezdek, J.C., Pal, N.R., Some new indexes of cluster validity (1998) IEEE Transactions on Systems, Man, and Cybernetics (Part B), 28, pp. 301-315Haykin, S., (1999) Neural Networks: A Comprehensive Foundation, , 2nd edition, Prentice-Hall: New YorkKamgar-Parsi, B., Gualtieri, J.A., Devaney, J.E., Kamgar-Parsi, B., Clustering with neural networks (1990) Biological Cybernetics, 63, pp. 201-208Frank, T., Kraiss, K.-F., Kuhlen, T., Comparative analysis of fuzzy ART and ART-2A network clustering performance (1998) IEEE Trans. on Neural Networks, 9, pp. 544-559Kohonen, T., (1997) Self-Organizing Maps, , 2nd Ed., Springer-Verlag: BerlinUltsch, A., Self-Organizing Neural Networks for Visualization and Classification (1993) Information and Classification, pp. 301-306. , O. Opitz et al. (Eds)., Springer: BerlinGirardin, L., (1995) Cyberspace geography visualization, , heiwww.unige.ch/girardin/cgvGonzales, R.C., Woods, R.E., (1992) Digital Image Processing. Reading, , MA: Addison-WesleyBarrera, J., Banon, J., Lotufo, R., Mathematical Morphology Toolbox for the Khoros System (1994) Image Algebra and Morphological Image Processing V, 2300, pp. 241-252. , E. Dougherty et al. Eds. Proc. SPIESerra, J., (1982) Image Analysis and Mathematical Morphology, , Academic Press: LondonNajman, L., Schmitt, M., Geodesic Saliency of Watershed Contours and Hierarchical Segmentation (1996) IEEE Trans. on Pattern Analysis and Machine Intelligence, 18, pp. 1163-1173Bleau, A., Leon, L.J., Watershed-based segmentation and region merging Comp. Vis. Image Underst., 77, pp. 317-370Costa, J.A.F., Mascarenhas, N., Netto, M.L.A., Cell nuclei segmentation in noisy images using morphological watersheds (1997) Applications of Digital Image Processing XX., 3164, pp. 314-324. , A. Tescher Ed. Proc. of the SPIECosta, J.A.F., Netto, M.L.A., Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps (1999) International Journal of Neural Systems, 9 (3), pp. 195-202Costa, J.A.F., Netto, M.L.A., Cluster analysis using self-organizing maps and image processing techniques Proc. of the 1999 IEEE Intl. Conf. on Systems, Man, and Cybernetics, , Tokyo, JapanNakamura, E., Kehtarnavaz, N., Determining the number of clusters and prototype locations via multi-scale clustering (1998) Pattern Recognition Letters, 19, pp. 1265-1283Li, T., Tang, Y., Suen, S., Fang, L., Hierarchical classification and vector quantisation with neural trees (1993) Neurocomputing, 5, pp. 119-139Racz, J., Klotz, T., Knowledge representation by dynamic competitive learning techniques Proc. 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    Automated morphological classification of galaxies

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    Abstract. Galaxies are systems of dark matter, stars, gas and dust orbiting around a central concentration of mass. They span a wide variety of appearances, based on which they can be classified. Several classification schemes have been developed during the last century, with an attempt to relate the different types of galaxies to each other based on their key physical features and use the received information to further the understanding of both the composition as well as the evolution of galaxies. In the past, the small amount of galaxies in survey image data allowed for the classification process to be completed visually, but with the ever-growing size and depth of survey image data, making classifications in this way nowadays is near to impossible. Various machine learning algorithms specialized in image recognition and classification can outperform human classifiers in terms of speed and likely soon also in accuracy. The advent of the development of neural network tools for astronomy was in early 1990’s, and ever since especially convolutional neural networks have been applied to classification problems in galactic astronomy. Applications of unsupervised learning have also shown promise in being able to produce self-organizing classification results. The rapid development of machine learning algorithms and hardware suited to perform automated large-scale classification tasks with astronomical survey data holds promise for machine learning methods being able to eventually fully replace humans in most classification tasks

    Handshape recognition for Argentinian Sign Language using ProbSom

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    Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic

    Handshape recognition for Argentinian Sign Language using ProbSom

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    Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA handshapes, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%

    Handshape recognition for Argentinian Sign Language using ProbSom

    Get PDF
    Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic

    Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

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    Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenario
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