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نتیجه جستجو - PIXE

تعداد مقالات یافته شده: 121
ردیف عنوان نوع
1 ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022
Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using com- puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of input image characteristics, the method was evaluated using images with and without depth information in resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of subjective human annotations, plumage condition predictions were compared to manual assessments of one observer and to matching annotations of two observers. Among all tested settings, performance metrics based on matching manual annotations of two observers were equal or better than the ones based on annotations of a single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of 98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.
keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation
مقاله انگلیسی
2 Prediction of total volatile basic nitrogen (TVB-N) and 2-thiobarbituric acid (TBA) of smoked chicken thighs using computer vision during storage at 4 °C
پیش‌بینی کل نیتروژن بازی فرار (TVB-N) و اسید ۲-تیوباربیتوریک (TBA) ران مرغ دودی با استفاده از بینایی رایانه در طول نگهداری در دمای ۴ درجه سانتی‌گراد-2022
As the traditional indicators of freshness measurement of meat products, TVB-N and TBA have the disadvantage of time-consuming, labor-intensive and destructive to the sample. The objective of this study was to investigate the possibility of computer vision techniques to visualize the variation of TVB-N and TBA during the storage of smoked chicken thighs. In this study, freshness indicators (TVB-N and TBA) and images of smoked chicken thighs were obtained simultaneously every 3 days during storage at 4 ◦C. Then, the RGB color space was converted to HSI and L*a*b* color spaces by color conversion algorithm, and the color parameters (RGB, HSI and L*a*b*) were correlated with TVB-N and TBA, respectively, for establishing multiple regression models. Finally, visu- alization maps of the spoilage were established by applying the multiple regression model to each pixel in the image. The results showed that the multiple linear regression models of TBA and TVB-N based on the color parameters L*, a*, I, S and R were well correlated (R 2 = 0.993 for TBA and R 2 = 0.970 for TVB-N). Distribution maps of TBA and TVB-N changed color gradually from blue to red during storage, respectively. In conclusion, this study demonstrated that distribution maps can be employed as a rapid, objective, and non-destructive method to predict the TBA and TVB-N values of smoked chicken thighs during storage.
keywords: ران مرغ دودی | بینایی کامپیوتر | خنکی | TVB-N | TBA | Smoked chicken thigh | Computer vision | Freshness
مقاله انگلیسی
3 Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision
مناظر معنایی رودخانه: درک و ارزیابی مناظر خطی از تصاویر مایل با استفاده از بینایی کامپیوتری-2022
Traditional approaches for visual perception and evaluation of river landscapes adopt on-site surveys or as- sessments through photographs. The former is expensive, hindering large-scale analyses, and it is conducted only on street-level or top-down imagery. The latter only reflects the subjective perception and also entails a laborious process. Addressing these challenges, this study proposes an alternative: a novel workflow for visual analysis of urban river landscapes by combining unmanned aerial vehicle (UAV) oblique photography with computer vision (CV) and virtual reality (VR). The approach is demonstrated with an experiment on a section of the Grand Canal in China where UAV oblique panoramic imagery has been processed using semantic segmentation for visual evaluation with an index system we designed. Concurrent surveys, immersive and non-immersive VR, are used to evaluate these photos, with a total of 111 participants expressing their perceptions across multiple dimensions. Then, the relationship between the people’s subjective visual perception and the river landscape environment as seen by computers has been established. The results suggest that using this approach, rivers and surrounding landscapes can be analyzed automatically and efficiently, and the mean pixel accuracy (MPA) of the developed model is 90%, which advances state of the art. The results of this study can benefit urban planners in formulating riverside development policies, analyzing the perception of plans for a future scenario before an area is rede- veloped, and the method can also aid relevant parties in having a macro understanding of the overall situation of the river as a basis for follow-up research. Due to simplicity, accuracy and effectiveness, this workflow is transferable and cost-effective for large-scale investigations of riverscapes and linear heritage. We openly release Semantic Riverscapes—the dataset we collected and processed, bridging another gap in the field.
keywords: ریورساید | باز کردن داده ها | GeoAI | بررسی های هوایی | هواپیماهای بدون سرنشین | واقعیت مجازی | Riverside | Open data | GeoAI | Aerial surveys | Drones | Virtual reality
مقاله انگلیسی
4 Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022
Computer vision-based techniques for structural displacement measurement are rapidly becoming popular in civil structural engineering. However, most existing computer vision-based displace- ment measurement methods require man-made targets for object matching or tracking, besides usually the measurement accuracies are seriously sensitive to the ambient illumination variations. A computer vision-based illumination robust and multi-point simultaneous measuring method is proposed for structural displacement measurements. The method consists of two part, one is for segmenting the beam body from its background, the segmentation is perfectly carried out by fully convolutional network (FCN) and conditional random field (CRF); another is digital image cor- relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory. The accuracy and illumination robustness are verified through three groups of elaborately designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation, numbers of locations along with the segmented beam body can be chosen and measured simul- taneously. It is verified that the method is illumination robust since the displacement measure- ments are with the smallest fluctuations to the illumination variations. The proposed method does not require any man-made targets attached on the structure, but because of the exploitation of DIC in displacement measurement, the regions centered on the measuring points need to have texture feature.
keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation
مقاله انگلیسی
5 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
6 A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022
The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing
مقاله انگلیسی
7 Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022
Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom- mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm- ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.
keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease
مقاله انگلیسی
8 Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging
روش‌های آنیل کوانتومی و ارزیابی تجربی مسئله بازکردن فاز در تصویربرداری رادار دیافراگم مصنوعی-2022
The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger sized images. Our approach involves formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved on a quantum annealer. Given that present embodiments of quantum annealers remain limited in the number of qubits they possess, we decompose the problem into a set of subproblems that can be solved individually. These individual solutions are close to optimal up to an integer constant, with one constant per subimage. In a second phase, these integer constants are determined as a solution to yet another QUBO problem. This basic idea is extended to several passes, where each pass results in an image which is subsequently decomposed to yet another set of subproblems until the resulting image can be accommodated by the annealer at hand. Additionally, we explore improvements to the method by decomposing the original image into overlapping subimages and ignoring the results on the overlapped (marginal) pixels. We test our approach with a variety of software-based QUBO solvers and on a variety of images, both synthetic and real. Additionally, we experiment using D-wave systems’ quantum annealer, the D-wave 2000Q_6 and developed an embedding method which, for our problem, yielded improved results. Our method resulted in high quality solutions, comparable to state-of-the-art phase-unwrapping solvers.
INDEX TERMS: Interferometric synthetic aperture radar (SAR) | phase unwrapping, quadratic unconstrained binary optimization (QUBO) | quantum annealing.
مقاله انگلیسی
9 Compensating over- and underexposure in optical target pose determination
Compensating over- and underexposure in optical target pose determination-2021
Optical coded targets allow to determine the relative pose of a camera, on a metric scale, from one image only. Furthermore, they are easily and efficiently detected, opening to a wide range of applications in robotics and computer vision. In this work we describe the effect of pixel saturation and non-ideal lens Point Spread Function, causing the apparent position of the corners and the edges of the target to change as a function of the camera exposure time. This effect, which we call exposure bias, is frequent in over- or underexposed images and introduces a systematic error in the estimated camera pose. We propose an algorithm that is able to estimate and correct for the exposure bias exploiting specific geometric features of a common target design based on concentric circles. Through rigorous laboratory experiments carried out in a highly controlled environment, we demonstrate that the proposed algorithm is seven times more precise and three times more accurate in the target distance estimation than the algorithms available in the literature.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Optical target | Target orientation | Image processing algorithm | Geometry | Ellipse fitting | Computer vision | Overexposure | Exposure compensation | Resection
مقاله انگلیسی
10 Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery
بینایی عمیق مبتنی بر یادگیری برای تشخیص و طبقه بندی حرکات بخیه در جراحی با کمک روبات-2021
Background: Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep learning-based computer vision to automate the identification and classification of suturing gestures for needle driving attempts.
Methods: Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2,395 videos) and classification (511 videos) datasets were compiled to train computer vision models to produce 2- and 5-class label predictions, respectively. Networks were trained on inputs of raw red/blue/green pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits.
Results: In this study, all models were able to reliably predict either the presence of a gesture (identification, area under the curve: 0.88) as well as the type of gesture (classification, area under the curve: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (long short-term memory unit versus convolutional long short-term memory unit) on performance.
Conclusion: Our results demonstrate computer vision’s ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning computer vision toward future automation of surgical skill assessment.
مقاله انگلیسی
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