Ideogram identification in a realtime traffic sign recognition system

Priese, Lutz; Lakmann, Raimund; Rehrmann, Volker

doi:10.1109/ivs.1995.528299

Cited by 36 publications

(16 citation statements)

References 8 publications

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“…These singularities are used for the recognition of rectangular, circular, and triangular shapes. Autonomous robots or intelligent systems for cars may use this kind of system [23].…”

Section: System Architecture-tnn and Control Integrationmentioning

confidence: 99%

“…Robust methods for the analysis of images, and the implementation of a system based on specialized TNN have been developed for shape recognition by means of the analysis of characteristics elements of shapes, called singularities [22], [23], and which will be explained later in this paper, in Section V. Trafflc signal and/or pedestrian recognition are two of the most relevant applications. These networks work cooperatively, as explained later in this paper, to obtain the classiflcation of the image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Moreno¹,

Alarcon²,

Salvador³

et al. 2009

IEEE Trans. Ind. Electron.

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Abstract-Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs) specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units (layers) and dynamic reconflguration, depending on the application speciflc demands. One of the most important features of TNNs is their learning ability. Weight modiflcation and architecture reconflguration can be carried out at run-time. Our system performs objects identiflcation by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to valídate the research, the system has been implemented and conflgured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has signiflcant performance beneflts.

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“…These singularities are used for the recognition of rectangular, circular, and triangular shapes. Autonomous robots or intelligent systems for cars may use this kind of system [23].…”

Section: System Architecture-tnn and Control Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Moreno¹,

Alarcon²,

Salvador³

et al. 2009

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…These singularities are used for the recognition of rectangular, circular and triangular shapes. Autonomous robots or intelligent systems for cars may use this kind of system [29]. The decision to have the communication of the global control system through a bus structure was taken after consideration of the efficiency level that we wanted to achieve.…”

Section: Interconnection Of the Specialized Tnn To The Global Controlmentioning

confidence: 99%

Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

et al. 2009

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Neural networks, widely used in pattern recognition, security applications and robot control have been chosen for the task of object recognition within this system. One of the main drawbacks of the implementation of traditional neural networks in reconfigurable hardware is the huge resource consuming demand. This is due not only to their intrinsic parallelism, but also to the traditional big networks designed. However, modern FPGA architectures are perfectly suited for this kind of massive parallel computational needs. Therefore, our proposal is the implementation of Tiny Neural Networks, TNN -self-coined term-, in reconfigurable architectures. One of most important features of TNNs is their learning ability. Therefore, what we show here is the attempt to rise the autonomy features of the system, triggering a new learning phase, at run-time, when necessary. In this way, autonomous adaptation of the system is achieved. The system performs shape identification by the interpretation of object singularities. This is achieved by interconnecting several specialized TNN that work cooperatively. In order to validate the research, the system has been implemented and configured as a perceptron-like TNN with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits.

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“…Researchers have explored several techniques for classifying the ideographs, including artificial neural networks (ANNs) [4], template matching [14], chain code [15], and matching pursuit methods [9].…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Recognition in Disturbing Environments

Yang

Liu

et al. 2003

Lecture Notes in Computer Science

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Abstract. Traffic sign recognition is a difficult task if we aim at detecting and recognizing signs in images captured from unfavorable environments. Complex background, weather, shadow, and other lighting-related problems may make it difficult to detect and recognize signs in the rural as well as the urban areas. We employ discrete cosine transform and singular value decomposition for extracting features that defy external disturbances, and compare different designs of detection and classification systems for the task. Experimental results show that our pilot systems offer satisfactory performance when tested with very challenging data.

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Ideogram identification in a realtime traffic sign recognition system

Cited by 36 publications

References 8 publications

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

Traffic Sign Recognition in Disturbing Environments

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