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1 |
Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022 Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit
and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper
exploits the combination of image processing techniques and machine learning models (Random Forests) to
assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor-
mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma-
chine learning components, was able to build effective models for either the classification problem (visual quality
level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and
ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on
packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with
respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con-
tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and
can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be
analyzed using traditional tools. keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction |
مقاله انگلیسی |
2 |
PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022 In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity.
However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop-
ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a
solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous,
real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed
with all the steps necessary for the system to operate, from hardware specifications and data collection to su-
pervisory information obtained by deep learning and statistical analysis. The overall system was tested and
validated in an operational environment in a material recovery facility.
PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2%
precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%.
Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively,
giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed
per batch, the detection results were used to estimate purity and its confidence level. The estimation error was
calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon-
strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can
stream. keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision |
مقاله انگلیسی |
3 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
4 |
Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022 Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In
recent years, social media data have become a promising data source for the assessment of cultural ecosystem
services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the
CES and landscape features from social media photos by manual content analysis. In addition, relatively few
studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this
study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition,
image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud
Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and
landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that
the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’
photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes
(corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated
image analysis on social media photos. We also noticed that tourists paid more attention to the place identity
featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants,
flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and
CES assessment, which could provide insights for urban park planning and tourism management. keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision |
مقاله انگلیسی |
5 |
New method for a SEM-based quantitative microstructural clay analysis - MiCA
روش جدید برای تجزیه و تحلیل کمی خاک رس ریزساختاری مبتنی بر SEM - MiCA-2021 The soil microstructure is recognised to strongly influence the mechanical behaviour of both coarse and fine geomaterials. Proper identification and tracking of the shape and position of the particles has become more and more critical to form a link between the micro and macro behaviour. Scanning Electron Microscopy (SEM) has been widely used in the last decades to study the clay fabric variation with its mechanical behaviour and physical properties. However, the particles orientation has so far been discussed only from a qualitative point of view due to the lack of updated, automatised quantification tools. Consequently, developing constitutive models that correlate the soil micro structure to its mechanical behaviour is not feasible. In order to make a step forward in this direction, an Image Analysis based code called MiCA (microstructural clay analyser) capable of quantifying the particle orientation and the porosity of clay samples through the analysis of SEM micrographs was developed in this study. The code reliability was first validated through the application to geometrical reference patterns, then to textbook micrographs illustrating typical clay fabrics (dispersed, honeycomb, flocculated and aggre- gated), and finally to high quality images. MiCA showed good accuracy in the results obtained, regardless of the number of lines in the image, the complexity of the geometrical shapes and the pixel size of the analysed graphs. Therefore, MiCA can be considered suitable for quantitative analysis of the particle orientation and/or pores shape in clay materials. Keywords: Image analysis | Computer vision | Quantitative analysis | Clay particles orientation | SEM | Clay porosity |
مقاله انگلیسی |
6 |
Calibrated color measurement of cashmere using a novel computer vision system
اندازه گیری کالیبره رنگ ترمه با استفاده از یک سیستم بینایی ماشین جدید-2021 Color of cashmere fiber is a key component of textile quality inspection. However, the inspection of cashmere color was determined by human vision system, which was less accuracy and time-consuming. Computer vision system (CVS) is considered as a promising technique to objectively and precisely test color. In the present work, a novel color measurement system for cashmere was proposed. Totally 29 cashmere samples with different color were adopted as standard samples to calibrate color conversion model. The correlation coefficient of L, a, b values between the two systems was separately calculated high to 0.99, 0.96 and 0.93 for the whole samples. The proposed method was further validated by other 15 samples, indicating the accuracy of the novel CVS. Besides, due to the high accuracy and strong representativeness of the new method, the categories of cashmere, which were normally tested by subjective visual assessment, could be determined by the present results. Keywords: Cashmere color | Computer vision | Image analysis | Calibration model | Classification |
مقاله انگلیسی |
7 |
Classification of fermented cocoa beans (cut test) using computer vision
طبقه بندی دانه های کاکائو تخمیر شده (تست برش) با استفاده از بینایی ماشین-2021 Fermentation of cocoa beans is a critical step for chocolate manufacturing, since fermentation influences the development of flavour, affecting components such as free amino acids, peptides and sugars. The degree of fermentation is determined by visual inspection of changes in the internal colour and texture of beans, through the cut-test. Although considered standard for evaluation of fermentation in cocoa beans, this method is time consuming and relies on specialized personnel. Therefore, this study aims to classify fermented cocoa beans using computer vision as a fast and accurate method. Imaging and image analysis provides hand-crafted features computed from the beans, that were used as predictors in random decision forests to classify the samples. A total of 1800 beans were classified into four grades of fermentation. Concerning all image features, 0.93 of accuracy was obtained for validation of unbalanced dataset, with precision of 0.85, recall of 0.81. Although the unbalanced dataset represents actual variation of fermentation, the method was tested for a balanced dataset, to investigate the influence of a smaller number of samples per class, obtaining 0.92, 0.92 and 0.90 for accuracy, precision and recall, respectively. The technique can evolve into an industrial application with a proper integration framework, substituting the traditional method to classify fermented cocoa beans. Keywords: Chocolate | Cut-test | Food quality | Analytical method | Image analysis | Random decision forest |
مقاله انگلیسی |
8 |
A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN
یک روش یادگیری عمیق برای غربالگری عفونت مالاریا: شمارش خودکار و سریع سلول ها ، تشخیص اشیاء و تقسیم بندی نمونه با استفاده از Mask R-CNN-2021 Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several com- puter vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we used a deep learning model called Mask R-CNN that is trained on uninfected and Plasmodium falciparum-infected red blood cells. Our predictive model produced reports at a rate 15 times faster than manual counting without compromising on accuracy. Another unique feature of our model is its ability to generate segmentation masks on top of bounding box classifications for immediate visualization, making it superior to existing models. Furthermore, with greater standardization, it holds much potential to reduce errors arising from manual counting and save a significant amount of human resources, time, and cost. Keywords: Malaria diagnosis | Mask R-CNN | Computer vision | Image analysis |
مقاله انگلیسی |
9 |
Unrest index for estimating thermal comfort of poultry birds (Gallus gallus domesticus) using computer vision techniques
شاخص ناآرامی برای برآورد آسایش حرارتی پرندگان طیور (Gallus gallus domesticus) با استفاده از تکنیک های بینایی ماشین-2021 Behaviour can be used to infer animal welfare states. Poultry birds tend to move less under
conditions of thermal stress; hence the hypothesis of this research is that this unrest
behaviour can be used as an indicator of thermal comfort. The objective was to develop an
Unrest Index for poultry bird’s sensitive to changes in this behaviour under different air
temperature conditions. The proposed Unrest Index was based on the Hausdorff distance
measure and was tested on recorded videos of laying hens and broilers breeders, obtained
in different experiments. The index was efficient in detecting the unrest of poultry birds in
different thermal conditions and, in conditions above thermoneutrality, the birds moved
significantly less. The distribution Unrest Index of data for each thermal condition tested
was shown to be asymmetric. However, there seems to be a tendency to reverse this
asymmetry when the conditions are thermal comfort and heat stress. It is suggested that
the Unrest Index can be used to estimate the thermal comfort of poultry birds and that
further studies on the asymmetry of the index data should be carried out in order to
identify of the thermoneutrality zone of birds in a non-invasive way. The Unrest Index and
the computer vision techniques adopted to assess poultry thermal comfort automatically
were efficient in demonstrating differences in bird agitation in distinct thermal stress
conditions. The low computational effort and the mathematical simplicity of the model
allows the Unrest Index to incorporate bird surveillance systems and estimate thermal
comfort automatically.
Keywords: Animal behaviour | Hausdorff distance | Comfort index | Precision livestock farming | Image analysis |
مقاله انگلیسی |
10 |
Monitoring the hot-air drying process of organically grown apples (cv: Gala) using computer vision
نظارت بر فرایند خشک شدن سیب های ارگانیک در هوا (cv: Gala) با استفاده از بینایی ماشین-2021 The feasibility of using a computer-vision (CV) system embedded in a hot-air dryer for nondestructive and real-time monitoring of the drying behaviour of organic apples was
investigated in the present study. Apple cylinders were subjected to anti-browning treatments with different dipping solutions (water as control, trehalose (4% w/v) or
trehalose þ ascorbic acid (4% w/v and 1% w/v)) and dipping pressures (atmospheric,
101.3 kPa and sub-atmospheric pressure, 50 kPa) followed by drying at 60 C to a final dry
basis moisture content of 0.18 g g1. The CV system was used as an in-line process
analytical technology (PAT) tool to capture images reflecting the physico-chemical changes
during product drying coupled with in-line mass changes and off-line reference analyses.
The spatial and colour changes from the image analysis described well the complex and
non-homogenous nature of apple drying. The results of spatial changes allowed successful
development of accurate linear prediction models for moisture content as a function of
area shrinkage (on scaled variables) with excellent prediction capability (|BIAS| < 8.5 103,
RMSE < 0.04, Adj-R2 ~ ¼ 99%). Also, the CV system identified the differently pre-treated
samples, particularly the dipping pressures as reflected by two linear models and their
respective parameters. The obtained results demonstrate the versatile advantages of CV
systems as an in-line tool for continuous, real-time monitoring of apples during drying.
The insights from this study can provide a platform for applications of CV embedded
‘Smart dryers’ as an efficient monitoring and control system for industrial drying
processes.
Keywords: Malus domestica B. | Image analysis | Dipping treatments | Vacuum impregnation | Smart drying | Area shrinkage |
مقاله انگلیسی |