An automated imaging BRDF polarimeter for fruit quality inspection

Boyer, Jacob T.; Keresztes, Janos; Saeys, Wouter; Koshel, John

doi:10.1117/12.2239008

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…For optimizing illumination without specular reflectance spots, different halogen tungsten light sources and light diffusers were evaluated. This noise from light sources on the surface of the sample has been reported in literature [ 36 , 37 , 38 , 39 ]. Although the light diffusers were successful in eliminating saturated (specular reflectance) points, the light intensity for reflectance capture was reduced considerably.…”

Section: Methodssupporting

confidence: 55%

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Jarolmasjed

Khot

Sankaran

2018

Sensors

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Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible–near-infrared spectrometry-based reflectance spectral data (350–2500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 °C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550–1700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible–near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90–100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines.

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Section: Methodssupporting

confidence: 55%

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Jarolmasjed

Khot

Sankaran

2018

Sensors

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“…At the moment, with the exception of humans (who use polarisation cameras to judge fruit quality on a factory conveyor-sorting system; Boyer et al, 2016), little evidence exists for polarisation vision being used to locate food or judge its quality. A few authors have noted that flowers, and indeed other areas of plants, reflect polarisation patterns.…”

Section: Polarised Foodmentioning

confidence: 99%

Polarisation signals: a new currency for communication

Marshall

Powell

Cronin

et al. 2019

Journal of Experimental Biology

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Most polarisation vision studies reveal elegant examples of how animals, mainly the invertebrates, use polarised light cues for navigation, course-control or habitat selection. Within the past two decades it has been recognised that polarised light, reflected, blocked or transmitted by some animal and plant tissues, may also provide signals that are received or sent between or within species. Much as animals use colour and colour signalling in behaviour and survival, other species additionally make use of polarisation signalling, or indeed may rely on polarisation-based signals instead. It is possible that the degree (or percentage) of polarisation provides a more reliable currency of information than the angle or orientation of the polarised light electric vector (e-vector). Alternatively, signals with specific e-vector angles may be important for some behaviours. Mixed messages, making use of polarisation and colour signals, also exist. While our knowledge of the physics of polarised reflections and sensory systems has increased, the observational and behavioural biology side of the story needs more (and more careful) attention. This Review aims to critically examine recent ideas and findings, and suggests ways forward to reveal the use of light that we cannot see.

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“…Nguyen-Do-Trong et al [10] excluded the specular highlight from the acquired hyperspectral reflectance images by implementing the cross-polarization to block the specular reflection, and they evaluated the cross-polarization approach in a line-scanning hyperspectral reflectance imaging system for the first time. Boyer [27] also used a cross-polarization imaging system to reduce the specular artifacts. Wen et al [28] developed a polarization-guided model to generate a polarization chromaticity image and then reformulated the problem into a global energy function based on the proposed model.…”

Section: Related Workmentioning

confidence: 99%

A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits

2022

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Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.

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An automated imaging BRDF polarimeter for fruit quality inspection

Cited by 5 publications

References 7 publications

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Polarisation signals: a new currency for communication

A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits

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