The Experts below are selected from a list of 19353 Experts worldwide ranked by ideXlab platform

Zhengming Ding - One of the best experts on this subject based on the ideXlab platform.

  • sparse representation based graph embedding for traffic Sign Recognition
    IEEE Transactions on Intelligent Transportation Systems, 2012
    Co-Authors: Zhengming Ding
    Abstract:

    Researchers have proposed various machine learning algorithms for traffic Sign Recognition, which is a supervised multicategory classification problem with unbalanced class frequencies and various appearances. We present a novel graph embedding algorithm that strikes a balance between local manifold structures and global discriminative information. A novel graph structure is deSigned to depict explicitly the local manifold structures of traffic Signs with various appearances and to intuitively model between-class discriminative information. Through this graph structure, our algorithm effectively learns a compact and discriminative subspace. Moreover, by using L2, 1-norm, the proposed algorithm can preserve the sparse representation property in the original space after graph embedding, thereby generating a more accurate projection matrix. Experiments demonstrate that the proposed algorithm exhibits better performance than the recent state-of-the-art methods.

Y.-j. Zheng - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive system for traffic Sign Recognition
    Proceedings of the Intelligent Vehicles '94 Symposium, 1994
    Co-Authors: Y.-j. Zheng, Wolfgang Ritter, R Janssen
    Abstract:

    Traffic Sign Recognition is a primary goal of almost all road environment understanding systems. A vision system for traffic Sign Recognition was developed by Daimler-Benz Research Center Ulm. The two main modules of the system are detection and verification (Recognition). Here regions of possible traffic Signs in a color image sequence are first detected before each of them is verified and recognized. In this paper the authors pay attention to the verification and Recognition process. The authors present an adaptive approach and emphasize the importance of the adaptability to various road and traffic Sign environments. The authors utilize a distance-weighted k-nearest-neighbor classifier for traffic Sign Recognition and show its equivalence to the kind of radial basis function networks which can be easily integrated into chips. The authors also present a way to evaluate the uncertainty of recognized traffic Signs and demonstrate their approach using real images.

  • A real-time traffic Sign Recognition system
    IEEE Intelligent Vehicles Symposium, 1994
    Co-Authors: S. Estable, Ronald Ott, Wolfgang Ritter, F Stein, R Janssen, Jonathan Schick, Y.-j. Zheng
    Abstract:

    The ability of recognising traffic Signs in a road traffic scenario is an important feature of the Daimler-Benz autonomous vehicle VITA II. This real-time vision-based traffic Sign Recognition system has been developed by Daimler-Benz in the European research project PROMETHEUS. In this paper we focus on the overall system deSign, the real-time implementation, and field test evaluation. The software architecture of the system integrates three hierarchical levels of data processing. On each level the specific tasks are isolated. The lowest level comprises specialists for colour, shape and pictogram analysis; they perform the iconic to symbolic data transformation. On the highest level the administration processes organise data flow as a double bottom-up and top-down mechanism to dynamically interpret the image sequence. A hybrid parallel machine was deSigned for running the traffic Sign Recognition system in real time on a transputer network coupled to powerPC processors

R Janssen - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive system for traffic Sign Recognition
    Proceedings of the Intelligent Vehicles '94 Symposium, 1994
    Co-Authors: Y.-j. Zheng, Wolfgang Ritter, R Janssen
    Abstract:

    Traffic Sign Recognition is a primary goal of almost all road environment understanding systems. A vision system for traffic Sign Recognition was developed by Daimler-Benz Research Center Ulm. The two main modules of the system are detection and verification (Recognition). Here regions of possible traffic Signs in a color image sequence are first detected before each of them is verified and recognized. In this paper the authors pay attention to the verification and Recognition process. The authors present an adaptive approach and emphasize the importance of the adaptability to various road and traffic Sign environments. The authors utilize a distance-weighted k-nearest-neighbor classifier for traffic Sign Recognition and show its equivalence to the kind of radial basis function networks which can be easily integrated into chips. The authors also present a way to evaluate the uncertainty of recognized traffic Signs and demonstrate their approach using real images.

  • A real-time traffic Sign Recognition system
    IEEE Intelligent Vehicles Symposium, 1994
    Co-Authors: S. Estable, Ronald Ott, Wolfgang Ritter, F Stein, R Janssen, Jonathan Schick, Y.-j. Zheng
    Abstract:

    The ability of recognising traffic Signs in a road traffic scenario is an important feature of the Daimler-Benz autonomous vehicle VITA II. This real-time vision-based traffic Sign Recognition system has been developed by Daimler-Benz in the European research project PROMETHEUS. In this paper we focus on the overall system deSign, the real-time implementation, and field test evaluation. The software architecture of the system integrates three hierarchical levels of data processing. On each level the specific tasks are isolated. The lowest level comprises specialists for colour, shape and pictogram analysis; they perform the iconic to symbolic data transformation. On the highest level the administration processes organise data flow as a double bottom-up and top-down mechanism to dynamically interpret the image sequence. A hybrid parallel machine was deSigned for running the traffic Sign Recognition system in real time on a transputer network coupled to powerPC processors

S. Estable - One of the best experts on this subject based on the ideXlab platform.

  • A real-time traffic Sign Recognition system
    IEEE Intelligent Vehicles Symposium, 1994
    Co-Authors: S. Estable, Ronald Ott, Wolfgang Ritter, F Stein, R Janssen, Jonathan Schick, Y.-j. Zheng
    Abstract:

    The ability of recognising traffic Signs in a road traffic scenario is an important feature of the Daimler-Benz autonomous vehicle VITA II. This real-time vision-based traffic Sign Recognition system has been developed by Daimler-Benz in the European research project PROMETHEUS. In this paper we focus on the overall system deSign, the real-time implementation, and field test evaluation. The software architecture of the system integrates three hierarchical levels of data processing. On each level the specific tasks are isolated. The lowest level comprises specialists for colour, shape and pictogram analysis; they perform the iconic to symbolic data transformation. On the highest level the administration processes organise data flow as a double bottom-up and top-down mechanism to dynamically interpret the image sequence. A hybrid parallel machine was deSigned for running the traffic Sign Recognition system in real time on a transputer network coupled to powerPC processors

  • Shape Classification for Traffic Sign Recognition
    IFAC Proceedings Volumes, 1993
    Co-Authors: B. Besserer, S. Estable, B. Ulmer, Dirk Reichardt
    Abstract:

    Abstract A traffic Sign detection and Recognition approach is presented in this paper. This project is a part of the European research project PROMETHEUS(PROgraM for a European Traffic with Highest Efficiency and Unprecedented Safety) and is being developed by DAIMLER BENZ in collaboration with various university labs. Intensity segmentation, shape and traffic Sign Recognition have been joined together in a processing chain. Uncertainty handling, combining and propagation using Dernpster-Shafer rules fonn the heart of the shape Recognition method. Multiple Knowledge Sources extract infonnation from the segmented image and increase knowledge about undefined shapes. Recognized shapes are transmitted to a high-level processing stage which perfonns model-based traffic Sign Recognition.

Wolfgang Ritter - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive system for traffic Sign Recognition
    Proceedings of the Intelligent Vehicles '94 Symposium, 1994
    Co-Authors: Y.-j. Zheng, Wolfgang Ritter, R Janssen
    Abstract:

    Traffic Sign Recognition is a primary goal of almost all road environment understanding systems. A vision system for traffic Sign Recognition was developed by Daimler-Benz Research Center Ulm. The two main modules of the system are detection and verification (Recognition). Here regions of possible traffic Signs in a color image sequence are first detected before each of them is verified and recognized. In this paper the authors pay attention to the verification and Recognition process. The authors present an adaptive approach and emphasize the importance of the adaptability to various road and traffic Sign environments. The authors utilize a distance-weighted k-nearest-neighbor classifier for traffic Sign Recognition and show its equivalence to the kind of radial basis function networks which can be easily integrated into chips. The authors also present a way to evaluate the uncertainty of recognized traffic Signs and demonstrate their approach using real images.

  • A real-time traffic Sign Recognition system
    IEEE Intelligent Vehicles Symposium, 1994
    Co-Authors: S. Estable, Ronald Ott, Wolfgang Ritter, F Stein, R Janssen, Jonathan Schick, Y.-j. Zheng
    Abstract:

    The ability of recognising traffic Signs in a road traffic scenario is an important feature of the Daimler-Benz autonomous vehicle VITA II. This real-time vision-based traffic Sign Recognition system has been developed by Daimler-Benz in the European research project PROMETHEUS. In this paper we focus on the overall system deSign, the real-time implementation, and field test evaluation. The software architecture of the system integrates three hierarchical levels of data processing. On each level the specific tasks are isolated. The lowest level comprises specialists for colour, shape and pictogram analysis; they perform the iconic to symbolic data transformation. On the highest level the administration processes organise data flow as a double bottom-up and top-down mechanism to dynamically interpret the image sequence. A hybrid parallel machine was deSigned for running the traffic Sign Recognition system in real time on a transputer network coupled to powerPC processors