Bor-Shenn Jeng
Yuan Ze University
24 Papers
352 Citations
Bor-Shenn Jeng is an academic researcher from Yuan Ze University. The author has contributed to research in topics: Object detection & Toll. The author has an hindex of 13, co-authored 24 publications. Previous affiliations of Bor-Shenn Jeng include National Central University & Chunghwa Telecom.
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Papers
Lane Detection With Moving Vehicles in the Traffic Scenes
TL;DR: A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed, which is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic.
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The Application of a Convolution Neural Network on Face and License Plate Detection
Ying-Nong Chen,Chin-Chuan Han,Cheng-Tzu Wang,Bor-Shenn Jeng,Kuo-Chin Fan +4 more
- 20 Aug 2006
TL;DR: Two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network (CNN) verifier, and Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates.
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Environment classification and hierarchical lane detection for structured and unstructured roads
TL;DR: The experimental results have shown that the classification mechanism can effectively choose the correct lane detection algorithm according to the current environment setting, and the system is able to robustly find the lane boundaries on different types of roads in various weather conditions.
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Analysis of the Optimum Configuration of Roadside Units and Onboard Units in Dedicated Short-Range Communication Systems
TL;DR: The uplink signal strength received by the receiving module of a roadside unit (RSU) and emitted from the radiation module of an onboard unit (OBU) can be described and calculated successfully, and an optimum RSU and OBU mounting configuration can be easily obtained.
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Real-time traffic parameter extraction using entropy
W.-L. Hsu,Hong-Yuan Mark Liao,Bor-Shenn Jeng,K.-C. Fan +3 more
- 01 Jun 2004
TL;DR: The entropy measurement, which is commonly adopted as an important feature to describe the degree of disorder in thermodynamics, is used as an underlying feature in this work and the experimental results clearly show that the proposed system is highly effective.
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