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  • 201.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Channel Representation of Information2000Report (Other academic)
  • 202.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Computer Processing and Display of Chromosome Image Information1973Report (Other academic)
  • 203.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Context Controllable Linkage Models2000Report (Other academic)
  • 204.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Discriminant Functions, Linear Operations and Learning1989Report (Other academic)
  • 205.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Fourier Preprocessing for Hand Print Character Recognition1972In: I.E.E.E. transactions on computers (Print), ISSN 0018-9340, E-ISSN 1557-9956, Vol. C--21, no 2, p. 195-201Article in journal (Refereed)
  • 206.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    From Multidimensional Signals to the Generation of Responses1997In: Algebraic Frames for the Perception-Action Cycle, eds G. Sommer and J. J. Koenderink, Springer-Verlag , 1997, Vol. 1315, p. 29-53Conference paper (Refereed)
    Abstract [en]

    It has become increasingly apparent that perception cannot be treated in isolation from the response generation, firstly because a very high degree of integration is required between different levels of percepts and corresponding response primitives. Secondly, it turns out that the response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. The state of the system is in consequence the combination of the responses produced and the percepts associated with these responses. Thirdly, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain. There are not yet solutions available to all of these problems. In consequence, this overview will focus on what are considered crucial problems for the future, rather than on the solutions available today. It will discuss hierarchical architectures for combination of percept and response primitives, and the concept of combined percept-response invariances as important structural elements for Vision. It will be maintained that learning is essential to obtain the necessary exibility and adaptivity. In consequence, it will be argued that invariances for the purpose of vision are not geometrical but derived from the percept-response interaction with the environment. The issue of information representation becomes extremely important in distributed structures of the types foreseen, where uncertainty of information has to be stated for update of models and associated data.

  • 207.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    From signal to response: Issues in representation and computation1997In: Proceedings of TFTS'97, The 2nd IEEE UK Symposium on Applications of Time-frequency and Time-scale Methods, 1997Conference paper (Refereed)
  • 208.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Hierarchical Distributed Data Structures and Operations1982Report (Other academic)
  • 209.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Hierarchical Image Processing1983In: Proceedings of SPIE Technical Conference: Geneva, Switzerland, 1983Conference paper (Refereed)
  • 210.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Image Processing Systems and Components1989Report (Other academic)
  • 211.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Image Sequence Analysis1993In: Mustererkennung 1993, Mustererkennung im Dienste der Gesundheit eds S.J. Pöppl and H. Handels: Berlin, 1993, p. 1-18Conference paper (Refereed)
  • 212.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Imprecision of Measurements in Computer Vision Handled by Fuzzy Set Theory1987In: 5th IEEE-ASSP and EURASIP Workshop on Multidimensional Signal Processing, 1987Conference paper (Refereed)
  • 213.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    In Search of a General Picture Processing Operator1978In: Computer Graphics and Image Processing, ISSN 0146-664X, Vol. 8, no 2, p. 155-173Article in journal (Refereed)
    Abstract [en]

    The problem of finding a general, parallel, and hierarchical operator for picture processing is considered. An operator is defined which at different levels can detect and describe structure as opposed to uniformity within local regions, whatever structure and uniformity may imply at a particular level. The operator performs a mapping from one complex field to another. The important characteristic of this approach is the use of complex fields which allows a global-to-local feedback. In the transformation process the image is simplified. A Fourier implementation of the operator is described and a new transform is defined. The operators become increasingly global on higher levels in order to include adjacent high-level features. A hierarchical structure of such transformations gives a sequential description of structure over increasingly larger regions of the image. The processed information at different levels can be used as input to a classifier. Examples are given of processing results.

  • 214.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Information Representation in Image Analysis Algorithms1989Report (Other academic)
  • 215.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Integrated Analysis-Response Structures for Robotics Systems1988Report (Other academic)
  • 216.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Integrated Analysis-Response Structures for Robotics Systems1988Report (Other academic)
  • 217.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Introduction and Overview1995In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 1-39Chapter in book (Refereed)
    Abstract [en]

    This chapter establishes the motivation anduse of hierarchical operation structures to provide a systematicorganization for the implementation of complicated models. The chaptergives an intuitive treatment of most aspects that are considered inthe later chapters.

  • 218.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Introduction to GOP Computer Vision.1986Report (Other academic)
  • 219.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Issues in Robot Vision1993In: British Machine Vision Conference 1993, 1993, p. 1-14Conference paper (Refereed)
  • 220.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Learning Through Response-Driven Association2000Report (Other academic)
  • 221.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Low Level Image Interpretation Using Associative Mapping2000Report (Other academic)
  • 222.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Magnitude Representation of Feature Variables1988Report (Other academic)
  • 223.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Magnitude Representation of Features in Image Analysis1989In: Proceedings of the 6th Scandinavian Conference on Image Analysis : Oulu, June 19-22, 1989 / [ed] Matti Pietikainen and Juha Röning, Pattern Recognition Society of Finland , 1989, p. 212-219Conference paper (Refereed)
    Abstract [en]

    We have in the preceding sections studlied the use of magnitude representation for feature variables. There are several indications that such a representation may be used in biological visual systerms.

    The natural introduction of a nonlinearity may be most useful for many purposes. This has been studied for the implementation of penalty function operations. Such operations show great promise as they can be made very specific based on their zero-crossing property.

    There is a great deal of indication that inhibition or penalty mechanisms are very important in neural systems. It has e.g. been found that in the cerebellar structure almost all synapses are inhibitory. This could indicate that inhibitory or penalty matching is a primary mechanism in biological vision systems.

  • 224.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Operations and Representations for Multidimensional Information1996In: Proceedings of RecPad'96, The 8th Portuguese Conference on Pattern Recognition: Guimaraes, Portugal, 1996Conference paper (Refereed)
  • 225.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Pattern Processing Using Multilevel Systems1970In: Proceedings of the Eigth Annual Allerton Conference on Circuit and System Theory, 1970, p. 445-453Conference paper (Refereed)
  • 226.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Processing and Analysis of Multidimensional Information Using Adaptive Models1989In: Proceedings of the SSAB Conference on Image Analysis: Gothenburg, Sweden, 1989, p. 37-44Conference paper (Refereed)
  • 227.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Processing and Analysis of Multidimensional Information Using Adaptive Models1990In: Proceedings of the SSAB Symposium on Image Analysis: Linköping, Sweden, 1990, p. 19-34Conference paper (Refereed)
  • 228.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Processing and Analysis of Multi-Dimensional Information Using Adaptive Models1988In: Proceedings from SSAB Symposium on Picture Processing: Lund University, Sweden, 1988Conference paper (Refereed)
  • 229.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Response Generation and Learning Crucial Issues in Machine Vision1996In: Machine Perception Applications. Proc. of the IAPR TC-8 Workshop in Machine Perception Applications, Technical University, Graz, Austria, 2--3 September, 1996, eds A. Pinz and W. Pölzleitner, 1996, Vol. 93, p. 155-184Conference paper (Refereed)
  • 230.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Statistical Analysis of Chromosome Characteristics1974In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 6, no 2, p. 115-126Article in journal (Refereed)
    Abstract [en]

    The advent of new stains for chromosomes has increased the possibility of implementing useful automated chromosome analysis. The case with which chromosomes can now be recognized makes it possible to perform detailed statistical analysis of the chromosomes of an individual. This paper describes methods for assembling chromosome information from several cells in such a way that accidental variations due to preparation, etc. can be eliminated and an undistorted set of characteristics of the chromosome complement can be established. This set of characteristics can then be compared with various references, and statements can be made concerning the relationships between variations in the chromosome complement and genetic traits. These same methods can be employed in multiple-cell karyotyping to circumvent the classical problem of touching and overlapping chromosomes. The methods also allow one to achieve very reliable descriptions of the chromosome complement. The importance of appropriate descriptors of the chromosomes is illustrated.

  • 231.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    The Use of Distribution Functions to Describe Integrated Density Profiles of Human Chromosomes1973In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 40, no 3, p. 573-589Article in journal (Refereed)
    Abstract [en]

    The advent of new stains for chromosomes has increased the possibilities that useful automated chromosome analysis can be implemented. The search for appropriate descriptors to use in this process is an important task. Data compression using integrated intensity and density profiles has already shown itself to be valuable. A method is proposed in this paper to describe these profiles as a sum of distribution functions. Every distribution function can be described by a triplet stating peak height, position, and width and it appears that these parameters are directly related to physical processes. The importance of such parameters in statistical chromosome analysis is emphasized. A classification experiment is described in which 240 chromosomes 1 to 22, X and Y have been classified with an accuracy of 96%.

  • 232.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    The Use of Distribution Functions to Describe Interated Profiles of Human Chromosomes1973In: Chromosome Identification, Proceedings of the 23rd Nobel Symposium: eds T. Caspersson and L. Zech / [ed] Torbjörn Caspersson and Lore Zech, New York: Academic Press , 1973Chapter in book (Refereed)
  • 233.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    The Use of Dynamics to Establish Knowledge of Invariant Structure2000Report (Other academic)
  • 234.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Arvidsson, Jan
    n/a.
    Computer Architectures for Image Processing.1985In: Proceedings of The 4th Scandinavian Conference on Image Analysis: Trondheim, Norway, 1985Conference paper (Refereed)
  • 235.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Arvidsson, Jan
    n/a.
    The GOP Image Computer1983In: Fundamentals in Computer Vision: ed O. D. Faugeras / [ed] O. D. Faugeras, Cambridge: Cambridge University Press , 1983Chapter in book (Refereed)
  • 236.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Arvidsson, Jan
    n/a.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    GOP, A Paradigm in Hierarchical Image Processing1982In: Proceedings of The First IEEE Computer Society International Symposium on Medical Imaging and Image Interpretation, ISMI II'82: Berlin, Federal Republic of Germany, 1982Conference paper (Refereed)
  • 237.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Karlholm, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Classification and Response Generation1995In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 367-397Chapter in book (Refereed)
    Abstract [en]

    This chapter is not original, but presents methods for linear classification in the tradition of N. J. Nilsson as well as R. O. Duda and P. E. Hart. Part of the motivation for including this well-known material is to allow the vision structure to be brought to a logical conclusion in which feature properties are combined to form responses or class statements. Another motivation developed here is to display the similarity in structure between convolution operations and linear discriminant functions. This brings all operations for feature extraction and classification to the use of a common component, linear discriminants. This is also illustrated in the form of perceptrons, which allows a transition to the modern theory of neural networks.

  • 238.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Karlholm, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westelius, Carl-Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Biological Vision1995In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 41-95Chapter in book (Refereed)
    Abstract [en]

    This chapter givesan overview of important biological vision mechanisms. Although agreat deal is known about neural processing of visual information,most essential questions about biological vision remain as yetunanswered. Nonetheless, the knowledge available has already provideduseful guidance to the organization of effective machine visionsystems.

  • 239.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Compact Associative Representation of Structural Information1988Report (Other academic)
  • 240.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Compact Associative Representation of Visual Information1990In: Proceedings of The 10th International Conference on Pattern Recognition: Atlantic City, New Jersey, 1990Conference paper (Refereed)
  • 241.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Contrast of Structured and Homogenous Representations1983In: Physical and Biological Processing of Images: eds O. J. Braddick and A. C. Sleigh / [ed] Oliver J. Braddick, A. C. Sleigh, Berlin: Springer Verlag , 1983, p. 282-303Chapter in book (Refereed)
  • 242.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Hierarchical Processing of Structural Information in Artificial Intelligence1982In: Proceedings of 1982 IEEE Conference on Acoustics, Speech and Signal Processing: Paris, 1982Conference paper (Refereed)
  • 243.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Hedlund, Martin
    n/a.
    Hierarchical Processing of Structural Information1981Report (Other academic)
  • 244.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westelius, Carl-Johan
    n/a.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Issues in Robot Vision1994In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 12, no 3, p. 131-148Article in journal (Refereed)
    Abstract [en]

    In this paper, we discuss certain issues regarding robot vision. The main theme will be the importance of the choice of information representation. We will see the implications at different parts of a robot vision structure. We deal with aspects of pre-attentive versus attentive vision, control mechanisms for low level focus of attention, and representation of motion as the orientation of hyperplanes in multdimensional time-space. Issues of scale will be touched upon, and finally, a depth-from stereo algorithm based on guadrature filter phase is presented.

  • 245.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wilson, Roland
    n/a.
    Image Enhancement1983In: Fundamentals in Computer Vision: ed O. D. Faugeras / [ed] O. D. Faugeras, Cambridge: Cambridge University Press , 1983, p. 57-68Chapter in book (Refereed)
  • 246.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Low Level Operations1995In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 97-116Chapter in book (Refereed)
    Abstract [en]

    This chapter gives an introductory treatment of operations andrepresentations for low-level features in multi-dimensional spaces. Animportant issue is how to combine contributions from several filtersto provide robust statements in accordance with certain low-levelmodels. This chapter gives an introduction to the problems ofunambiguous mappings in multi-dimensional spaces.

  • 247.
    Granlund, Gösta
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, HansLinköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Signal Processing for Computer Vision1995Collection (editor) (Other academic)
    Abstract [en]

    Signal Processing for Computer Vision is a unique and thorough treatment of the signal processing aspects of filters and operators for low-level computer vision.

    Computer vision has progressed considerably over recent years. From methods only applicable to simple images, it has developed to deal with increasingly complex scenes, volumes and time sequences. A substantial part of this book deals with the problem of designing models that can be used for several purposes within computer vision. These partial models have some general properties of invariance generation and generality in model generation.

    Signal Processing for Computer Vision is the first book to give a unified treatment of representation and filtering of higher order data, such as vectors and tensors in multidimensional space. Included is a systematic organisation for the implementation of complex models in a hierarchical modular structure and novel material on adaptive filtering using tensor data representation.

    Signal Processing for Computer Vision is intended for final year undergraduate and graduate students as well as engineers and researchers in the field of computer vision and image processing.

  • 248.
    Granlund, Gösta
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Unrestricted Recognition of 3-D Objects for Robotics Using Multi-Level Triplet Invariants2004In: Artificial Intelligence Magazine, Vol. 25, no 2, p. 51-67Article in journal (Refereed)
    Abstract [en]

     A method for unrestricted recognition of 3-D objects has been developed. By unrestricted, we imply that the recognition shall be done independently of object position, scale, orientation and pose, against a structured background. It shall not assume any preceding segmentation and allow a reasonable degree of occlusion. The method uses a hierarchy of triplet feature invariants, which are at each level defined by a learning procedure. In the feed-back learning procedure, percepts are mapped upon system states corresponding to manipulation parameters of the object. The method uses a learning architecture employing channel information representation. The paper contains a discussion of how objects can be represented. A structure is proposed to deal with object and contextual properties in a transparent manner.

  • 249.
    Granlund, Gösta
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anne
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science.
    Unrestricted recognition of 3D objects for robotics using multilevel triplet invariants2004In: The AI Magazine, ISSN 0738-4602, Vol. 25, no 2, p. 51-67Article in journal (Refereed)
    Abstract [en]

    A method for unrestricted recognition of three-dimensional objects was developed. By unrestricted, we imply that the recognition will be done independently of object position, scale, orientation, and pose against a structured background. It does not assume any preceding segmentation or allow a reasonable degree of occlusion. The method uses a hierarchy of triplet feature invariants, which are at each level defined by a learning procedure. In the feedback learning procedure, percepts are mapped on system states corresponding to manipulation parameters of the object. The method uses a learning architecture with channel information representation. This article discusses how objects can be represented. We propose a structure to deal with object and contextual properties in a transparent manner.

  • 250.
    Granlund, Gösta
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Nordberg, Klas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Skarman, Erik
    Linköping University, Department of Computer and Information Science, EMTEK - Entity for Methodology and Technology of Knowledge Management. Linköping University, The Institute of Technology.
    Sandewall, Erik
    Linköping University, Department of Computer and Information Science, CASL - Cognitive Autonomous Systems Laboratory. Linköping University, The Institute of Technology.
    WITAS: An Intelligent Autonomous Aircraft Using Active Vision2000In: Proceedings of the UAV 2000 International Technical Conference and Exhibition (UAV), Paris, France: Euro UVS , 2000Conference paper (Refereed)
    Abstract [en]

    The WITAS Unmanned Aerial Vehicle Project is a long term basic research project located at Linköping University (LIU), Sweden. The project is multi-disciplinary in nature and involves cooperation with different departments at LIU, and a number of other universities in Europe, the USA, and South America. In addition to academic cooperation, the project involves collaboration with a number of private companies supplying products and expertise related to simulation tools and models, and the hardware and sensory platforms used for actual flight experimentation with the UAV. Currently, the project is in its second phase with an intended duration from 2000-2003.

    This paper will begin with a brief overview of the project, but will focus primarily on the computer vision related issues associated with interpreting the operational environment which consists of traffic and road networks and vehicular patterns associated with these networks.

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