Titlebook: Machine Vision; Automated Visual Ins Jürgen Beyerer,Fernando Puente León,Christian Fres Book 20161st edition Springer-Verlag Berlin Heidelb

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書(shū)目名稱Machine Vision影響因子(影響力)




書(shū)目名稱Machine Vision影響因子(影響力)學(xué)科排名




書(shū)目名稱Machine Vision網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Machine Vision網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Machine Vision被引頻次




書(shū)目名稱Machine Vision被引頻次學(xué)科排名




書(shū)目名稱Machine Vision年度引用




書(shū)目名稱Machine Vision年度引用學(xué)科排名




書(shū)目名稱Machine Vision讀者反饋




書(shū)目名稱Machine Vision讀者反饋學(xué)科排名

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Methods of Image AcquisitionThis chapter covers different methods for image acquisition in automated visual inspection. Selecting the appropriate acquisition method depends on the properties of interest of the investigated object (Fig. 7.1):

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Morphological Image ProcessingMorphological methods used in the algebra of sets can be used for morphological image processing. They were introduced by Matheron and Serra under the term ‘Mathematical Morphology’ [12, 16, 17]. Here, image signals are considered to be point sets and morphological filters are operations manipulating these sets.

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A Mathematical FoundationsThe intercept theorem [1,2] is an important principle for constructing the optical paths in the model of geometric optics (Chap. 3).

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Radiometrysible light is only a part of the whole electromagnetic spectrum (see Sec. 2.1.1). The respective wavelengths range from about 380 nm to 780 nm. Of course, physical measurements of intensity, power, etc., can be obtained for the whole electromagnetic spectrum without respect to the human eye, as is done in the domain of . or ..