检索结果-内蒙古大学图书馆

License Plate Detection and Segmentation Using Cluster run length smoothing algorithm

JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2012年第3期5卷 46-70页

作者： Abdullah, Siti Norul Huda Sheikh Sudin, Muhammad Nuruddin Prabuwono, Anton Satria Mantoro, Teddy Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Bangi Malaysia Univ Teknol Malaysia Adv Informat Sch Johor Baharu Malaysia

For the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster run length smoothing algorithm (CRLSA) was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using 3 x 3 kernel masks and 128 grayscale offset plus a three dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed;Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.

关键词： Clustering Image Detection License Plate Recognition run length smoothing algorithm

来源：评论

学校读者我要写书评

暂无评论

License plate detection using cluster run length smoothing algorithm (CRLSA)

License plate detection using cluster run length smoothing a...

引用

IASTED International Conference on Artificial Intelligence and Applications

作者： Abdullah, Siti Norul Huda Sheikh Omar, Khairuddin Khalid, Marzuki Yusof, Rubiyah Univ Teknol Malaysia Fac Elect Engn Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia Univ Kebangsaan Malaysia Fak Teknol Sains Maklumat Jabatan Sains & Pengurusan Sistem Bangi 43600 Selangor Malaysia

ISBN: (纸本)9780889866294

Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, clustering, feature extraction and neural networks. The image processing library is developed in-house which referred to as Vision System Development Platform (VSDP). Fixed filter, Minimum filter, Median Filter and Homomorphic Filtering are used in image enhancement process. After applying image enhancement, the image is segmented using blob analysis, horizontal scan line profiles, clustering and run length smoothing algorithm approach to identify the location of the license plate. Thoroughly each image is transformed into blob objects and its important information such as total of blobs, location, height and width, are being analyzed for the purpose of cluster exercising and choosing the best cluster with winner blobs. Here, new algorithm called Cluster run length smoothing algorithm (CRLSA) approach was applied to locate the license plate at the fight position. CRLSA consisted of two separate new proposed algorithm which applied new edge detector algorithm using 30 kernel masks and 128 grayscale offset plus a new way (3D method) to calculate run length smoothing algorithm (RLSA), which can improve clustering techniques in segmentation phase. Three separate experiments were performed;Cluster and Threshold value 130 (CT130) and CRLSA with Threshold value I (CCTI). From those experiments, analysis of error tables based on segmentation errors were constructed. The prototyped system has an accuracy more than 96% and suggestions to further improve the system are discussed in this paper pertaining to analysis of the error.

关键词： license plate recognition clustering run length smoothing algorithm thresholding

来源：评论

学校读者我要写书评

暂无评论

Segmentation of historical machine-printed documents using Adaptive run length smoothing and skeleton segmentation paths

引用

IMAGE AND VISION COMPUTING 2010年第4期28卷 590-604页

作者： Nikolaou, Nikos Makridis, Michael Gatos, Basilis Stamatopoulos, Nikolaos Papamarkos, Nikos Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece Natl Ctr Sci Res Demokritos Inst Informat & Telecommun Computat Intelligence Lab Athens 15310 Greece

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introduce the following innovative aspects: (i) use of a novel Adaptive run length smoothing algorithm (ARLSA) in order to face the problem of complex and dense document layout, (ii) detection of noisy areas and punctuation marks that are usual in historical machine-printed documents, (iii) detection of possible obstacles formed from background areas in order to separate neighboring text columns or text lines, and (iv) use of skeleton segmentation paths in order to isolate possible connected characters. Comparative experiments using several historical machine-printed documents prove the efficiency of the proposed technique. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Text line segmentation Word segmentation Character segmentation Historical machine-printed documents run length smoothing algorithm

来源：评论

学校读者我要写书评

暂无评论

Detecting license plate using cluster run length smoothing algorithm

Detecting license plate using cluster run length smoothing a...

引用

3rd International Conference on Informatics in Control, Automation and Robotics

作者： Abduilah, Siti Norul Huda Sheikh Khalid, Marzuki Yusof, Rubiyah Omar, Khairuddin Univ Teknol Malaysia Fac Elect Engn Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia Univ Kebangsaan Malaysia Fak Teknol Sains Maklumat Jabatan Sains Pengurusan Sistem Bangi 43600 Malaysia

ISBN: (纸本)9728865597

Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, clustering, feature extraction and neural networks. The image processing library is developed in-house which referred to as Vision System Development Platform (VSDP). After applying image enhancement, the image is segmented using blob analysis, horizontal scan line profiles, clustering and run length smoothing algorithm approach to identify the location of the license plate. Thoroughly each image is transformed into blob objects and its important information such as total of blobs, location, height and width, are being analyzed for the purpose of cluster exercising and choosing the best cluster with winner blobs. Here, new algorithm called Cluster run length smoothing algorithm (CLSA) approach was applied to locate the license plate at the right position. CLSA consisted of two separate new proposed algorithm which applied new edge detector algorithm using 30 kernel masks and 128 grayscale offset plus a new way (313 method) to calculate run length smoothing algorithm (RLSA), which can improve clustering techniques in segmentation phase. Two separate experiments were performed;Cluster and Threshold value 130 (CT130) and CRLSA with Threshold value 1 (CCT1). The prototyped system I has an accuracy more than 96% and suggestions to further improve te system are discussed in this paper pertaining to analysis of the error.

关键词： license plate recognition clustering run length smoothing algorithm thresholding

来源：评论

学校读者我要写书评

暂无评论

License plate recognition based on Support Vector Machine

License plate recognition based on Support Vector Machine

引用

International Conference on Electrical Engineering and Informatics

作者： Abdullah, Siti Norul Huda Sheikh Omar, Khairuddin Sahran, Shahnorbanun Khalid, Marzuki Univ Kebangsaan Malaysia Fac Informat Syst & Technol CAIT Bangi 43600 Selangor Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot Fac Elect Engn Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9781424449125

This Different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. After applying image enhancement, the image is segmented using clustering and run length smoothing algorithm approach to identify the location of the license plate. A proposed algorithm called Cluster run length smoothing algorithm approach was applied to locate the license plate at the right position. Enhanced geometrical feature topological analysis has been used as the feature extraction technique while support vector machine has been applied as the classification technique. Three separate experiments were performed and compared. From those experiments, analysis based on segmentation and classification errors were constructed. The results showed that the proposed prototype system gives up to 80% of accuracy rate.

关键词： run length smoothing algorithm license plate recognition geometrical feature topological analysis support vector machine

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：