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  • 1.
    Kalaykov, Ivan
    et al.
    Örebro University, Department of Technology.
    Tolt, Gustav
    Örebro University, Department of Technology.
    Fast fuzzy signal and image processing hardware2002In: Proceedings, NAFIPS 2002: Annual meeting of the North American fuzzy information processing society, 2002, 2002, p. 7-12Conference paper (Refereed)
    Abstract [en]

    The paper presents the development of fast fuzzy logic based hardware for various applications such as controllers for very fast processes, real-time image processing and pattern recognition. It is based on the fired-rules-hyper-cube (FRHC) concept, characterized by extremely simple way of the fuzzy inference in a layered parallel architecture. The processing time slightly depends on the number of inputs of the fuzzy system and does not depend on the number of rules and fuzzy partitioning of all variables. Most important is the inherent high speed of processing because of the parallelism and pipelining, implemented in all layers.

  • 2.
    Tolt, Gustav
    Örebro University, Department of Technology.
    Fuzzy similarity-based image processing2005Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Computer vision problems require low-level operations, e.g. noise reduction and edge detection, as well as high-level operations, e.g. object recognition and image understanding. Letting a PC carry out all computations is convenient but quite inefficient. One approach for improving the performance of the vision system is to bring as much as possible of the computationally intensive low-level operations closer to the camera using dedicated hardware devices, thus letting the PC focus on high-level tasks. In this thesis we present novel fuzzy techniques for reducing noise, determining edgeness and detecting junctions as well as stereo matching measures for color images, as building blocks of complex vision systems, e.g. for robot motion control or other industrial applications.

    The noise reduction is achieved by evaluating a number of fuzzy rules, each suggesting a particular filtering output. The firing strengths of the rules correspond to the degrees of similarity found among the pixels in the local processing window. The approach for determining edgeness is based on fuzzy rules that combine the estimated gradient magnitude with information about the homogeneity in different parts of the processing window. In this way the response from false edges is suppressed. In the junction detection approach we let the intersection between fuzzy sets represent the similarity between information obtained with different window sizes. The fuzzy sets represent the possible orientations of line segments in the window and non-zero intersections of the fuzzy sets indicate the presence of line segments in the window. The number of line segments characterize the nature of the junction. For the stereo matching measures, the global similarity betwen two pixels is defined in terms of fuzzy conjunctions of local similarities (color and edgeness). The proposed techniques have been designed for hardware implementation, making use of extensive parallelism and primarily simple numerical operations. The performance is shown in a number of experiments, and the strengths and limitations of the techniques are discussed.

  • 3.
    Tolt, Gustav
    Örebro University, Department of Technology.
    Fuzzy-similarity-based low-level image processing: licentiate thesis2003Licentiate thesis, monograph (Other academic)
  • 4.
    Tolt, Gustav
    et al.
    Örebro University, Department of Technology.
    Kalaykov, Ivan
    Örebro University, Department of Technology.
    A fuzzy-similarity-based approach for high-speed real-time image processing2005In: Intelligent Control, 2005 : Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation , 2005, p. 1240-1245Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a number of algorithms for performing some basic image processing tasks. The common denominator is the fuzzy similarity framework, that is used for representing vagueness and uncertainty associated with the similarity concept. The algorithms are designed so as to be implementable on FPGAs, making extensive use of the FPGA's parallel processing capabilities. Due to the limited space, we give pointers to previously published work for more details about the algorithms

  • 5.
    Tolt, Gustav
    et al.
    Örebro University, Department of Technology.
    Kalaykov, Ivan
    Örebro University, Department of Technology.
    Fuzzy-similarity-based noise cancellation for real-time image processing2001Conference paper (Refereed)
    Abstract [en]

    We introduce a new algorithm for image noise cancellation based on fuzzy similarity and homogeneity. The proposed method allows simple tuning of fuzzy filter properties and it is very convenient for high-speed real-time image processing. A detailed analysis of the filter properties is presented to support tuning its parameters for a particular application. Test examples and comparisons with other image noise cancellation techniques show the advantages of the method.

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