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Neural object classification by pattern recognition of one dimensional dataarrays which represent object information transformed by nonlinear functions.
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Neural object classification by pattern recognition of one dimensional data arrays which represent object information transformed by nonlinear functions.

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Pattern...

Pattern
Groups of measurements or observations, defining points in an appropriate multidimensional space.

Pattern recognition
Aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns.
My aim is to classify 2-dimensional objects with the help of manipulator and by the use of pattern recognition.

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    Neural object classification by pattern recognition of one dimensional dataarrays which represent object information transformed by nonlinear functions. Neural object classification by pattern recognition of one dimensional data arrays which represent object information transformed by nonlinear functions. Presentation Transcript

    • Neural object classification by pattern recognition of one dimensional data arrays which represent object information transformed by nonlinear functions. presented by Kayhan Ince Thesis Supervisor: Univ.Prof. Dipl.-Ing. Dr.techn. FAVRE-BULLE, Bernard Thesis Co-Advisor: Dipl.-Ing. Fauaz Labadi ACIN – Automation and Control Institute
    • Outline
      • Definition and aim
      • State of the art in neural pattern recognition
      • Shape analysis (classification) and grasping process
      • Simulation Results
      • Conclusion
    • Definition and aim
      • Pattern
        • Groups of measurements or observations, defining points in an appropriate multidimensional space.
      • Pattern recognition
        • Aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns.
        • My aim is to classify 2-dimensional objects with the help of manipulator and by the use of pattern recognition.
    • Grasping Process 7-DOF manipulator performs with whole arm grasping of a planar object
      • Hyper-redundant manipulator
        • - Serial-Chain-Mechanism
        • - Planar, rotational Joints
        • n Degrees of Freedom (DOF)
        • Array of angles
      joints Base Link
    • Classification process Classification shape = [pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4 pi/4] FEATURE VECTOR DRAWTESTSET TURNINGFUNCTION PERIODIC CLASSIFICATION RESULTS CLASSIFY
      • Fourier Descriptors
      Turning Functions A special property of Fourier descriptors is that a shape’s symmetry shows up in the feature vector.
        • Arkin published an efficient method for comparing polygonal shapes. The notion of the turning function which represents the shape of an object.
      Classification PNN
    • Neural Object Classification Artificial neuron Radial basis function Input Neuron 1 x   u F() 2 x u y    b w x u i i 1 w 2 w b Output(classification)
      • A probabilistic neural network structure is able to classify the objects for tentacle case problem
      • Turning functions carry the distance information of the objects
      Conclusion
    • Thank you for your attention.