<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>University of Kashan</PublisherName>
				<JournalTitle>Soft Computing Journal</JournalTitle>
				<Issn>2322-3707</Issn>
				<Volume>4</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2021</Year>
					<Month>05</Month>
					<Day>23</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Vehicle Type Recognition Using 3-D CAD</ArticleTitle>
<VernacularTitle>Vehicle Type Recognition Using 3-D CAD</VernacularTitle>
			<FirstPage>2</FirstPage>
			<LastPage>13</LastPage>
			<ELocationID EIdType="pii">111388</ELocationID>
			
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>Mohsen</FirstName>
					<LastName>Moradi</LastName>
<Affiliation></Affiliation>

</Author>
<Author>
					<FirstName>Mojtaba</FirstName>
					<LastName>Hosseini</LastName>
<Affiliation></Affiliation>

</Author>
<Author>
					<FirstName>Amirhossein</FirstName>
					<LastName>Momeni Azandaryani</LastName>
<Affiliation></Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2021</Year>
					<Month>05</Month>
					<Day>23</Day>
				</PubDate>
			</History>
		<Abstract>In vehicle surveillance systems, one of the appropriate methods for recognition are 3-D models. Several methods have been proposed for this purpose. Feature based methods are most significant and widely used. In this paper, is proposed an algorithm within recognition framework. Proposed algorithm is considered information of image and model edges as feature. A block descriptor has been used extract edges information to feature vector. Every feature vectors provide arrangement and layout in neighbourhood of edge point. Image and model feature vectors are compared using nearest neighbour method and measuring compliance are stored in a score matrix. Finally, the model has the most points in the image is detected as vehicle type. The experimental result is shown the proposed algorithm in terms of speed and accuracy offers better performance than the algorithms SURF and FREAK.</Abstract>
			<OtherAbstract Language="FA">In vehicle surveillance systems, one of the appropriate methods for recognition are 3-D models. Several methods have been proposed for this purpose. Feature based methods are most significant and widely used. In this paper, is proposed an algorithm within recognition framework. Proposed algorithm is considered information of image and model edges as feature. A block descriptor has been used extract edges information to feature vector. Every feature vectors provide arrangement and layout in neighbourhood of edge point. Image and model feature vectors are compared using nearest neighbour method and measuring compliance are stored in a score matrix. Finally, the model has the most points in the image is detected as vehicle type. The experimental result is shown the proposed algorithm in terms of speed and accuracy offers better performance than the algorithms SURF and FREAK.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Vehicle Type</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Recognition</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Surveillance System</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">3-D CAD Models</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Feature-Based Methods</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scj.kashanu.ac.ir/article_111388_516fde73a711f947943afdf777e5448f.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
