Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations

Mehl, Patrick M.; Chen, Yud-Ren; Kim, Moon S; Chan, Diane E.

doi:10.1016/s0260-8774(03)00188-2

Cited by 291 publications

(125 citation statements)

References 9 publications

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“…Recently, it has been applied to other areas such as biometrics, skin diagnosis, etc. [4][5][6][7][8][9][10][11][12] . Especially, some studies on hyperspectral face recognition have been reported very recently [8][9][10] [12] .…”

Section: Introductionmentioning

confidence: 99%

Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection

Wei

Zhang

et al. 2010

IEEE Trans. Syst., Man, Cybern. A

124

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Abstract:This paper studies face recognition by using hyperspectral imagery in the visible light bands. The spectral measurements over the visible spectrum have different discriminatory information for the task of face identification, and it is found that the absorption bands related to hemoglobin are more discriminative than the other bands. Therefore, feature band selection based on the physical absorption characteristics of face skin is performed and two feature band subsets are selected. Then, three methods are proposed for hyperspectral face recognition, including whole band (2D) 2 PCA, single band (2D) 2 PCA with decision level fusion, and band subset fusion based (2D) 2 PCA. A simple yet efficient decision level fusion strategy is also proposed for the later two methods. To testify the proposed techniques, a hyperspectral face database was established, which contains 25 subjects and has 33 bands over the visible light spectrum (0.4μm -0.72μm).The experimental results demonstrated that multispectral face recognition with the selected feature bands outperforms that by using a single band, or using the whole bands, or, interestingly, using the conventional RGB color bands.

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Section: Introductionmentioning

confidence: 99%

Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection

Wei

Zhang

et al. 2010

IEEE Trans. Syst., Man, Cybern. A

124

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“…The authors did not precise if this result was obtained during the training process or in validation. Mehl, Chen, Kim, and Chan (2004) used an hyper-spectral imaging system for selecting a reduced number of wavelength to detect defects on the surface of ÔRed DeliciousÕ, ÔGolden DeliciousÕ, ÔGalaÕ and ÔFujiÕ apples. They worked on five sound and five defective fruits of each cultivar.…”

Section: Introductionmentioning

confidence: 99%

Development of a multi-spectral vision system for the detection of defects on apples

Kleynen

Leemans

Destain

2005

Journal of Food Engineering

174

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A method to sort ÔJonagoldÕ apples based on the presence of defects was proposed. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed. Multi-spectral images of sound and defective fruits were acquired tending to cover the whole colour variability of this bicolour apple variety. Defects were grouped into four categories: slight defects, more serious defects, defects leading to the rejection of the fruit and recent bruises. Stem-ends/calyxes were detected using a correlation pattern matching algorithm. The efficiency of this method depended on the orientation of the stem-end/calyx according to the optical axis of the camera. Defect segmentation consisted in a pixel classification procedure based on the BayesÕ theorem and non-parametric models of the sound and defective tissue. Fruit classification tests were performed in order to evaluate the efficiency of the proposed method. No error was made on rejected fruits and high classification rates were reached for apples presenting serious defects and recent bruises. Fruits with slight defects presented a more important misclassification rate but those errors fitted however the quality tolerances of the European standard. Considering an actual ratio of sound fruits of 90%, less than 2% of defective fruits were classified into the sound ones.

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“…In the reports of Mehl et al (2004), Xing et al (2005Xing et al ( , 2007, Ariana et al (2006), and Qin et al (2008, 2009), owing to the obvious differences between the abnormality (i.e. bruise, rot, scab, canker, greasy spot, insect damage) regions and those of normal tissue of fruit (i.e.…”

Section: Segmentation Of Bruise Regionmentioning

confidence: 99%

“…X-ray imaging has Supported by the National Natural Science Foundation of China, Grant No. 30771243. been used to inspect internal quality, such as water core, bruise of apple (Shahin et al 1999(Shahin et al , 2002bKim et al 2000), weevil-infested mango (Thomas & Kannan 1995), and rotten onion (Shahin et al 2002a), etc. In the recent years, hyperspectral imaging has been investigated for the estimation of quality and safety of poultry (Liu et al 2003;Park et al 2006Park et al , 2007, fruits (Lu et al 1999;Lu 2003;Mehl et al 2004;Xing & Baerdemaeker 2005;Xing et al 2005Xing et al , 2007Noh & Lu 2007;Qin et al 2009), vegetables (Liu et al 2005;Ariana et al 2006), and milk (Qin & Lu 2007). Lu et al (1999) have studied hyperspectral imaging for detecting bruises on three cultivars of apples in the spectral region between 450 nm and 900 nm.…”

mentioning

confidence: 99%

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

LüQiang¹,

Tang²,

Cai³

et al. 2011

Czech J. Food Sci.

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Lü Q., Tang M.-j., Cai j.-r., Zhao j.-w., Vittayapadung S. (2011): Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits. Czech j. Food Sci., 29: 595-602.It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.we proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIr (408-1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using rBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

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Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations

Cited by 291 publications

References 9 publications

Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection

Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection

Development of a multi-spectral vision system for the detection of defects on apples

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

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