An Application of Sampling Point in Real Time Vehicle Classific- ation
碩士 === 國立臺灣大學 === 土木工程研究所 === 82 === Many of the approaches to and classifications of vehicles with image of traffic flow preseted in the past theses remain applicable to real time vehicle classification today and seems to possess considerable pote...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
1994
|
Online Access: | http://ndltd.ncl.edu.tw/handle/07535780128524727888 |
id |
ndltd-TW-082NTU00015076 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-082NTU000150762016-07-18T04:09:53Z http://ndltd.ncl.edu.tw/handle/07535780128524727888 An Application of Sampling Point in Real Time Vehicle Classific- ation 樣本點辨識方法應用於即時車種之辨識 Liao,Ming-Tsan 廖明燦 碩士 國立臺灣大學 土木工程研究所 82 Many of the approaches to and classifications of vehicles with image of traffic flow preseted in the past theses remain applicable to real time vehicle classification today and seems to possess considerable potential in behavioral traffic flow and traffic management research. This study attempts to discuss the premises supporting application of the approaches to categorizing vehicles set forth by real time color image processing. The author maintains that an important premise in such applications involves the construction of "The system of vehicle length detection technique." The present work uses such a system to categorize motorcycle, car, truck, buses and trailers in order to demonstrate that such a system can be used to obtain a more systematic understanding of concepts presented vehicle classification, and that this approach can be extended for real time application of traffic control. Stanley T. Lung 龍天立 1994 學位論文 ; thesis 99 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立臺灣大學 === 土木工程研究所 === 82 === Many of the approaches to and classifications of vehicles
with image of traffic flow preseted in the past theses remain
applicable to real time vehicle classification today and seems
to possess considerable potential in behavioral traffic flow
and traffic management research. This study attempts to discuss
the premises supporting application of the approaches to
categorizing vehicles set forth by real time color image
processing. The author maintains that an important premise in
such applications involves the construction of "The system of
vehicle length detection technique." The present work uses such
a system to categorize motorcycle, car, truck, buses and
trailers in order to demonstrate that such a system can be used
to obtain a more systematic understanding of concepts presented
vehicle classification, and that this approach can be extended
for real time application of traffic control.
|
author2 |
Stanley T. Lung |
author_facet |
Stanley T. Lung Liao,Ming-Tsan 廖明燦 |
author |
Liao,Ming-Tsan 廖明燦 |
spellingShingle |
Liao,Ming-Tsan 廖明燦 An Application of Sampling Point in Real Time Vehicle Classific- ation |
author_sort |
Liao,Ming-Tsan |
title |
An Application of Sampling Point in Real Time Vehicle Classific- ation |
title_short |
An Application of Sampling Point in Real Time Vehicle Classific- ation |
title_full |
An Application of Sampling Point in Real Time Vehicle Classific- ation |
title_fullStr |
An Application of Sampling Point in Real Time Vehicle Classific- ation |
title_full_unstemmed |
An Application of Sampling Point in Real Time Vehicle Classific- ation |
title_sort |
application of sampling point in real time vehicle classific- ation |
publishDate |
1994 |
url |
http://ndltd.ncl.edu.tw/handle/07535780128524727888 |
work_keys_str_mv |
AT liaomingtsan anapplicationofsamplingpointinrealtimevehicleclassification AT liàomíngcàn anapplicationofsamplingpointinrealtimevehicleclassification AT liaomingtsan yàngběndiǎnbiànshífāngfǎyīngyòngyújíshíchēzhǒngzhībiànshí AT liàomíngcàn yàngběndiǎnbiànshífāngfǎyīngyòngyújíshíchēzhǒngzhībiànshí AT liaomingtsan applicationofsamplingpointinrealtimevehicleclassification AT liàomíngcàn applicationofsamplingpointinrealtimevehicleclassification |
_version_ |
1718353616892329984 |