基于模糊邏輯技術(shù)圖像上邊緣檢測(cè)-----外文翻譯.doc
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基于模糊邏輯技術(shù)圖像上邊緣檢測(cè)-----外文翻譯,摘要:模糊技術(shù)是經(jīng)營者為了模擬在數(shù)學(xué)水平的代償行為過程的決策或主觀評(píng)價(jià)而引入的。下面介紹經(jīng)營商已經(jīng)完成了的計(jì)算機(jī)視覺應(yīng)用。本文提出了一種基于模糊邏輯推理戰(zhàn)略為基礎(chǔ)的新方法,它被建議使用在沒有確定閾值的數(shù)字圖像邊緣檢測(cè)上。這種方法首先將用3 3的浮點(diǎn)二進(jìn)制矩陣將圖像分割成幾個(gè)區(qū)域。邊緣像素被映射到一個(gè)屬性值與彼此不同的范...
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摘要:模糊技術(shù)是經(jīng)營者為了模擬在數(shù)學(xué)水平的代償行為過程的決策或主觀評(píng)價(jià)而引入的。下面介紹經(jīng)營商已經(jīng)完成了的計(jì)算機(jī)視覺應(yīng)用。本文提出了一種基于模糊邏輯推理戰(zhàn)略為基礎(chǔ)的新方法,它被建議使用在沒有確定閾值的數(shù)字圖像邊緣檢測(cè)上。這種方法首先將用3 3的浮點(diǎn)二進(jìn)制矩陣將圖像分割成幾個(gè)區(qū)域。邊緣像素被映射到一個(gè)屬性值與彼此不同的范圍。該方法的魯棒性所得到的不同拍攝圖像將與線性Sobel運(yùn)算所得到的圖像相比較。并且該方法給出了直線的線條平滑度、平直度和弧形線條的良好弧度這些永久的效果。同時(shí)角位可以更清晰并且可以更容易的定義。
關(guān)鍵詞:模糊邏輯,邊緣檢測(cè),圖像處理,電腦視覺,機(jī)械的部位,測(cè)量
1. 引言
在過去的幾十年里,對(duì)計(jì)算機(jī)視覺系統(tǒng)的興趣,研究和發(fā)展已經(jīng)增長(zhǎng)了不少。如今,它們出現(xiàn)在各個(gè)生活領(lǐng)域,從停車場(chǎng),街道和商場(chǎng)各角落的監(jiān)測(cè)系統(tǒng)到主要食品生產(chǎn)的分類和質(zhì)量控制系統(tǒng)。因此,引進(jìn)自動(dòng)化的視覺檢測(cè)和測(cè)量系統(tǒng)是有必要的,特別是二維機(jī)械對(duì)象[1,8]。部分原因是由于那些每天產(chǎn)生的數(shù)字圖像大幅度的增加(比如,從X光片到衛(wèi)星影像),并且對(duì)于這樣圖片的自動(dòng)處理有增加的需求[9,10,11]。因此,現(xiàn)在的許多應(yīng)用例如對(duì)醫(yī)學(xué)圖像進(jìn)行計(jì)算機(jī)輔助診斷,將遙感圖像分割和分類成土地類別(比如,對(duì)麥田,非法大麻種植園的鑒定,以及對(duì)作物生長(zhǎng)的估計(jì)判斷),光學(xué)字符識(shí)別,閉環(huán)控制,基于目錄檢索的多媒體應(yīng)用,電影產(chǎn)業(yè)上的圖像處理,汽車車牌的詳細(xì)記錄的鑒定,以及許多工業(yè)檢測(cè)任務(wù)(比如,紡織品,鋼材,平板玻璃等的缺陷檢測(cè))。歷史上的許多數(shù)據(jù)已經(jīng)被生成圖像,以幫助人們分析(相比較于數(shù)字表之類的,圖像顯然容易理解多了)[12]。所以這鼓勵(lì)了數(shù)字分析技術(shù)在數(shù)據(jù)處理方面的使用。此外,由于人類善于理解圖像,基于圖像的分析法在算法發(fā)展上提供了一些幫助(比如,它鼓勵(lì)幾何分析),并且也有助于非正式確認(rèn)的結(jié)果。雖然計(jì)算機(jī)視覺
Abstract—The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective eva luation. The following paper introduces such operators on hand of computer vision application.
In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.
Keywords—Fuzzy logic, Edge detection, Image processing, computer vision, Mechanical
關(guān)鍵詞:模糊邏輯,邊緣檢測(cè),圖像處理,電腦視覺,機(jī)械的部位,測(cè)量
1. 引言
在過去的幾十年里,對(duì)計(jì)算機(jī)視覺系統(tǒng)的興趣,研究和發(fā)展已經(jīng)增長(zhǎng)了不少。如今,它們出現(xiàn)在各個(gè)生活領(lǐng)域,從停車場(chǎng),街道和商場(chǎng)各角落的監(jiān)測(cè)系統(tǒng)到主要食品生產(chǎn)的分類和質(zhì)量控制系統(tǒng)。因此,引進(jìn)自動(dòng)化的視覺檢測(cè)和測(cè)量系統(tǒng)是有必要的,特別是二維機(jī)械對(duì)象[1,8]。部分原因是由于那些每天產(chǎn)生的數(shù)字圖像大幅度的增加(比如,從X光片到衛(wèi)星影像),并且對(duì)于這樣圖片的自動(dòng)處理有增加的需求[9,10,11]。因此,現(xiàn)在的許多應(yīng)用例如對(duì)醫(yī)學(xué)圖像進(jìn)行計(jì)算機(jī)輔助診斷,將遙感圖像分割和分類成土地類別(比如,對(duì)麥田,非法大麻種植園的鑒定,以及對(duì)作物生長(zhǎng)的估計(jì)判斷),光學(xué)字符識(shí)別,閉環(huán)控制,基于目錄檢索的多媒體應(yīng)用,電影產(chǎn)業(yè)上的圖像處理,汽車車牌的詳細(xì)記錄的鑒定,以及許多工業(yè)檢測(cè)任務(wù)(比如,紡織品,鋼材,平板玻璃等的缺陷檢測(cè))。歷史上的許多數(shù)據(jù)已經(jīng)被生成圖像,以幫助人們分析(相比較于數(shù)字表之類的,圖像顯然容易理解多了)[12]。所以這鼓勵(lì)了數(shù)字分析技術(shù)在數(shù)據(jù)處理方面的使用。此外,由于人類善于理解圖像,基于圖像的分析法在算法發(fā)展上提供了一些幫助(比如,它鼓勵(lì)幾何分析),并且也有助于非正式確認(rèn)的結(jié)果。雖然計(jì)算機(jī)視覺
Abstract—The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective eva luation. The following paper introduces such operators on hand of computer vision application.
In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.
Keywords—Fuzzy logic, Edge detection, Image processing, computer vision, Mechanical
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