Application of gradient-based Hough transform to the detection of corrosion pits in optical images

Wang, Yafei; Cheng, Guangxu

doi:10.1016/j.apsusc.2015.12.207

Cited by 30 publications

(12 citation statements)

References 33 publications

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“…The size distribution of PTNT:PC 71 BM (1:2 weight ratio) NPs prepared from different organic solvents were characterized from SEM images with a circular Hough transform algorithm. [37][38] In the varied annealing temperature study, all films were predried at 90 °C for 4 min immediately after spin-coating for consistency with device fabrication.…”

Section: Nanoparticle Characterizationmentioning

confidence: 99%

Environmentally friendly preparation of nanoparticles for organic photovoltaics

Pan

Sharma

Gedefaw

et al. 2018

Organic Electronics

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Aqueous nanoparticle dispersions were prepared from a conjugated polymer poly(2,5-thiophene-alt-4,9-bis(2-hexyldecyl)-4,9-dihydrodithieno[3,2-c:3',2'-h][1,5]naphthyridine-5,10-dione) (PTNT) and fullerene blend utilizing chloroform as well as a non-chlorinated and environmentally benign solvent, o-xylene, as the miniemulsion dispersed phase solvent. The nanoparticles (NPs) in the solid-state film were found to coalesce and offered a smooth surface topography upon thermal annealing. Organic photovoltaics (OPVs) with photoactive layer processed from the nanoparticle dispersions prepared using chloroform as the miniemulsion dispersed phase solvent were found to have a power conversion efficiency M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT2 (PCE) of 1.04%, which increased to 1.65% for devices utilizing NPs prepared from o-xylene.Physical, thermal and optical properties of NPs prepared using both chloroform and o-xylene were systematically studied using dynamic mechanical thermal analysis (DMTA) and photoluminescence (PL) spectroscopy and correlated to their photovoltaic properties. The PL results indicate different morphology of NPs in the solid state were achieved by varying miniemulsion dispersed phase solvent.

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Section: Nanoparticle Characterizationmentioning

confidence: 99%

Environmentally friendly preparation of nanoparticles for organic photovoltaics

Pan

Sharma

Gedefaw

et al. 2018

Organic Electronics

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“…The center of circle can be recognized by finding the maximum intensity in the accumulation array. These recognition processes have been discussed in depth in our previous paper [24]. The original optical image was imported into MATLAB and transformed into a grayscale image.…”

Section: Image-recognition Methodsmentioning

confidence: 99%

“…This is partially due to the difficulty of precisely determining the locations of all pits at the same time. In our previous study, an image recognition method named gradient-based Hough transformation was applied to the analysis of corrosion pits, which was proved to be effective for accurate recognition of the pit diameters and locations [24,25]. In this paper, this technique was further applied to the recognition of high-resolution and large field-of-view images and a new multiscale analysis method was proposed, which can reveal the spatial distributions of pits in different scales.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Statistical Analysis of Massive Corrosion Pits Based on Image Recognition of High Resolution and Large Field-of-View Images

Wang

Tian

2020

Materials

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In the present study, a new multiscale method is proposed for the statistical analysis of spatial distribution of massive corrosion pits, based on the image recognition of high resolution and large field-of-view (montage) optical images. Pitting corrosion for high strength pipeline steel exposed to sodium chloride solution was observed using an optical microscope. Montage images of the corrosion pits were obtained, with a single image containing a large number of corrosion pits. The diameters and locations of all the pits were determined simultaneously using an image recognition algorithm, followed by statistical analysis of the two-dimensional spatial point pattern. The multiscale spatial distributions of pits were analyzed by dividing the montage image into a number of different windows. The results indicate the clear dependence of distribution features on the spatial scales. The proposed method can provide a better understanding of the pit growth from the perspective of multiscale spatial evolution.

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“…Then, an accumulation step is introduced to find the highest response of the gradient, which would be the center of the circle. For the determination of the radii [30], if the point satisfies (3), where ∆ is the interval between adjacent r values.…”

Section: B Cell Detectionmentioning

confidence: 99%

A novel deep learning scheme for morphology-based classification of mycobacterial infection in unstained macrophages

Zhao

Bao

Wang

et al. 2019

Preprint

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Objective: Mycobacterium tuberculosis (Mtb) is an airborne, contagious bacterial pathogen that causes widespread infections in humans. Using Mycobacterium marinum (Mm), a surrogate model organism for Mtb research, the present study develops a deep learning-based scheme that can classify the Mminfected and uninfected macrophages in tissue culture solely based on morphological changes. Methods: A novel weak-and semisupervised learning method is developed to detect and extract the cells, firstly. Then, transfer learning and fine-tuning from the CNN is built to classify the infected and uninfected cells. Results: The performance is evaluated by accuracy (ACC), sensitivity (SENS) and specificity (SPEC) with 10-fold cross-validation. It demonstrates that the scheme can classify the infected cells accurately and efficiently at the early infection stage. At 2 hour post infection (hpi), we achieve the ACC of 0.923 ± 0.005, SENS of 0.938 ± 0.020, and SPEC of 0.905 ± 0.019, indicating that the scheme has detected significant morphological differences between the infected and uninfected macrophages, although these differences are hardly visible to naked eyes. Interestingly, the ACC at 12 and 24 hpi are 0.749 ± 0.010 and 0.824 ± 0.009, respectively, suggesting that the infection-induced morphological changes are dynamic throughout the infection. Finally, deconvolution with guided propagation maps the key morphological features contributing to the classification. Significance: This proof-ofconcept study provides a novel venue to investigate bacterial pathogenesis in a macroscopic level and has a great promise in diagnosis of bacterial infections.

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Application of gradient-based Hough transform to the detection of corrosion pits in optical images

Cited by 30 publications

References 33 publications

Environmentally friendly preparation of nanoparticles for organic photovoltaics

Environmentally friendly preparation of nanoparticles for organic photovoltaics

Multiscale Statistical Analysis of Massive Corrosion Pits Based on Image Recognition of High Resolution and Large Field-of-View Images

A novel deep learning scheme for morphology-based classification of mycobacterial infection in unstained macrophages

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