Ms. Liya Ding Profile

Liya Ding

at Zhejiang Univ

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Publications (2)

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Proceedings Article | 9 June 2003 Paper

Automatic traffic real-time analysis system based on video

Liya Ding, Jilin Liu, Qubo Zhou, Rengrong Wang

Proceedings Volume 5009, (2003) https://doi.org/10.1117/12.473955

KEYWORDS: Video, Image processing, Image segmentation, Cameras, Roads, Detection and tracking algorithms, Calibration, Imaging systems, Video processing, Image acquisition

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Automatic traffic analysis is very important in the modern world with heavy traffic. It can be achieved in numerous ways, among them, detection and analysis through video system, being able to provide affluent information and having little disturbance to the traffic, is an ideal choice. The proposed traffic vision analysis system uses Image Acquisition Card to capture real time images of the traffic scene through video camera, and then exploits the sequence of traffic scene and the image processing and analysis technique to detect the presence and movement of vehicles. First getting rid of the complex traffic background, which is always changing, the system segment each vehicle in the region the user interested. The system extracts features from each vehicle and tracks them through the image sequence. Combined with calibration, the system calculates information of the traffic, such as the speed of the vehicles, their types, the volume of flow, the traffic density, the waiting length of the lanes, the turning information of the vehicles, and so on. Traffic congestion and vehicles’ shadows are disturbing problems of the vehicle detection, segmentation and tracking. So we make great effort to investigate on methods to dealing with them.

Proceedings Article | 28 June 1994 Paper

Neural network implementation of fuzzy inference for approximate case-based reasoning

Liya Ding

Proceedings Volume 10312, 1031203 (1994) https://doi.org/10.1117/12.2283786

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In this chapter, we point out the symbolical-numerical duality of fuzzy logic and the rule-case duality of fuzzy rules in approximate reasoning. By the former, we can use a node of a neural network to represent a fuzzy proposition for the symbolic and the value passing the node (input or output) for the numeric. By the latter, we can construct a neural network structure for describing the relations of fuzzy rules and modifythe weights of the neural network to realize learning from cases (examples). The concept of the so-called approximate case-based reasoning' and its neural network implementation is set up on the above understanding. We first give the basic mechanism of approximate case-based reasoning and the neural network implementation, then extend it to more general and complex cases by several examples.

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