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A novelty signal processing method is proposed for a technical vision system (TVS). During data acquisition of an optoelectrical signal, part of this is random electrical fluctuation of voltages. Information theory (IT) is a well-known field that deals with random processes. A method based on using of the Shannon Entropy for feature extractions of optical patterns is presented. IT is implemented in structural health monitoring (SHM) to augment the accuracy of optoelectronic signal classifiers for a metrology subsystem of the TVS. To enhance the TVS spatial coordinate measurement performance at real operation conditions with electrical and optical noisy environments to estimate structural displacement better and evaluate its health for a better estimation of structural displacement and the evaluation of its health. Five different machine learning (ML) techniques are used in this work to classify optical patterns captured with the TVS. Linear predictive coding (LPC) and Autocorrelation function (ACC) are for extraction of optical patterns. The Shannon entropy segmentation (SH) method extracts relevant information from optical patterns, and the model's performance can be improved. The results reveal that segmentation with Shannon's entropy can achieve over 95.33%. Without Shannon's entropy, the worst accuracy was 33.33%.

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The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.

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Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

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Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method. We evaluated the ability of the U-Net to segment the different regions of the WHC images and, since the images are much larger than the traditional input size of the U-Net, we also evaluated its performance when we change the input size. Results show that U-Net can be used to segment the external and internal areas of the WHC images with great precision, even though the difference in the appearance of these areas is subtle.

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Purpose: The purpose of this first in-human study was to evaluate the overall clinical performance of the VERITAS™ Vision System in patients scheduled to undergo cataract extraction and to confirm overall surgeon acceptability. Patients and methods: This prospective, open-label multinational study included adults with cataracts scheduled for planned cataract extraction and posterior chamber IOL implantation. Standard small-incision phacoemulsification cataract surgery with the VERITAS Vision System was conducted. Surgeons completed a questionnaire regarding their clinical experience with the VERITAS Vision System for each patient following surgery and 1-day postoperative. Corneal clarity and adverse events (AEs) were assessed. Surgeon acceptability was scored on a 5-point scale, with acceptability considered favorable for scores of 4 and 5. Results: A total of 115 eyes (79 patients) were treated. The El Salvador site treated 41 patients (58 eyes), and the US site treated 38 patients (57 eyes). Overall, surgeons were satisfied with the clinical performance regardless of the cataract grade. The satisfaction with anterior chamber stability, post-occlusion surge, followability, holdability, cutting efficiency, usability, and overall satisfaction with the VERITAS Vision System was clinically favorable in ≥99% of cases. Overall satisfaction with the swivel handpiece, foot pedal, and enhanced ergonomics were clinically favorable in ≥97% of cases regardless of the cataract grade. Satisfaction with corneal clarity at same-day postoperative and 1-day postoperative, and 1-day overall clinical results of surgery with the VERITAS Vision System were clinically favorable in ≥94% of cases regardless of cataract grade. Conclusion: The new dual-mode phacoemulsification system with dual-durometer tubing, gas forced infusion, new swivel handpiece, and ergonomics improvements resulted in a high rate of user satisfaction with clinical performance and ergonomics. The VERITAS Vision System is safe and effective when used as indicated.

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A mirror-based active system capable of changing the view's direction of a pre-existing fixed camera is presented. The aim of this research work is to extend the perceptual tracking capabilities of an underwater robot without altering its structure. The ability to control the view's direction allows the robot to explore its entire surroundings without any actual displacement, which can be useful for more effective motion planning and for different navigation strategies, such as object tracking and/or obstacle evasion, which are of great importance for natural preservation in environments as complex and fragile as coral reefs. Active vision systems based on mirrors had been used mainly in terrestrial platforms to capture the motion of fast projectiles using high-speed cameras of considerable size and weight, but they had not been used on underwater platforms. In this sense, our approach incorporates a lightweight design adapted to an underwater robot using affordable and easy-access technology (i.e., 3D printing). Our active system consists of two arranged mirrors, one of which remains static in front of the robot's camera, while the orientation of the second mirror is controlled by two servomotors. Object tracking is performed by using only the pixels contained on the homography of a defined area in the active mirror. HSV color space is used to reduce lighting change effects. Since color and geometry information of the tracking object are previously known, a window filter is applied over the H-channel for color blobs detection, then, noise is filtered and the object's centroid is estimated. If the object is lost, a Kalman filter is applied to predict its position. Finally, with this information, an image PD controller computes the servomotor articular values. We have carried out experiments in real environments, testing our active vision system in an object-tracking application where an artificial object is manually displaced on the periphery of the robot and the mirror system is automatically reconfigured to keep such object focused by the camera, having satisfactory results in real time for detecting objects of low complexity and in poor lighting conditions.

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ABSTRACT Purpose: This study aimed to determine the variation in diameters of outer and inner apertures of eyedropper tips using a computer vision system. Standardizing the size of eye drop nozzles is crucial to reduce the treatment cost of chronic eye diseases and to ensure a continued use of medication. An eyedropper volume of >20 µL (maximum storage of the conjunctival sac) causes medication wastage and increases treatment costs. Methods: We measured the diameters of the outer and inner apertures of eyedropper tips and evaluated variations in diameters using a computerized visual inspection system. Results: The computer visual inspection system identified anomalies in the apertures of eyedropper tips that resulted in diameter variations. Conclusions: The results of the present study show discrepancies in diameters of eyedropper tips, suggesting a variation in eyedropper size and medication wastage.

RESUMO Objetivo: Este estudo teve como objetivo determinar a variação dos diâmetros das aberturas externa e interna dos bicos conta-gotas utilizando sistema de visão computacional. A padronização do tamanho dos colírios conta-gotas é importante para reduzir o custo do tratamento de doenças crônicas e garantir o uso contínuo de medicamentos. O volume da gota maior do que 20 µl (volume de armazenamento máximo do saco conjuntival) gera desperdício da medicação e aumenta o custo do tratamento. Métodos: Medimos os diâmetros das aberturas externa e interna das pontas dos conta-gotas e avaliamos as variações no diâmetro usando um sistema de inspeção visual computadorizado. Resultados: O sistema de inspeção visual por computador identificou anomalias nas aberturas dos bicos dos frascos conta-gotas que resultaram em variações de diâmetro. Conclusões: Os resultados do presente estudo mostram discrepâncias nos diâmetros dos bicos dos frascos dos conta-gotas, sugerindo uma variação no tamanho das gotas e no desperdício de remédios.

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Magnetohydrodynamics (MHD) is becoming more popular every day among developers of applications based on microfluidics, such as “lab on a chip” (LOC) and/or “micro-total analysis systems” (micro-TAS). Its physical properties enable fluid manipulation for tasks such as pumping, networking, propelling, stirring, mixing, and even cooling without the need for mechanical components, and its non-intrusive nature provides a solution to mechanical systems issues. However, these are not easy tasks. They all require precise flow control, which depends on several parameters, like microfluidics conductivity, the microfluidics conduit (channel) shape and size configuration, and the interaction between magnetic and electric fields. This results in a mathematical model that needs to be validated theoretically and experimentally. The present paper introduces the design of a 3D laminar flow involving an electrolyte in an annular open channel driven by a Lorentz force. For an organized description, first of all is provided an introduction to MHD applied in microfluidics, then an overall description of the proposed MHD microfluidic system is given, after that is focused in the theoretical validation of the mathematical model, next is described the experimental validation of the mathematical model using a customized vision system, and finally conclusions and future work are stated.

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This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.

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OBJECTIVE: This work sought to evaluate the precision and repeatability of a telepathology prototype based on open software and hardware. MATERIALS AND METHODS: A prototype was designed with application in telepathology and telemicroscopy. Accuracy and prototype precision were evaluated by calculating the mean absolute error and the intraclass and repeatability correlation coefficients for a series of 190 displacements at 10, 25, 50, 75, and 100 µm. RESULTS AND CONCLUSIONS: This work developed a low-cost prototype that is accessible, easily reproducible, implementable, and scalable; based on the use of technology created under principles of open software and hardware. A pathologist reviewed the obtained images and found them to be of diagnostic quality. Its excellent repeatability, coupled with its good accuracy, allows for its application in telemicroscopy and static, dynamic, and whole-slide imaging pathology systems.

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A pesquisa visou avaliar a metodologia do projeto Tree Vis para determinar a nutrição de ferro, boro, zinco e cobre em plantas de milho submetidas a doses desses nutrientes. Foram utilizados tratamentos constituídos pela omissão, 1/5, 2/5 e a dose completa dos elementos com quatro repetições em cada fase de coleta, sendo essas V4, V7 e R1. Os experimentos foram realizados em casa de vegetação, em cultivo hidropônico, conduzidos em vasos com solução nutritiva. Foi determinada a produção de massa seca da parte aérea e do sistema radicular, além da determinação dos teores dos nutrientes nas folhas indicativas dos estádios fenológicos de cada época de coleta. Em cada estádio foram coletadas imagens das folhas indicativas e novas através de um scanner para as análises de visão artificial. As doses crescentes dos nutrientes promoveram maior produção de massa seca na parte aérea e nas raízes e reduziram a produção quando utilizada a dose máxima do nutriente. O sistema de visão artificial mostrou-se promissor na identificação de deficiência de ferro com 77,5% de acerto, boro com 81,7% de acerto, zinco com 81,0% e cobre com 57,2 % de acerto, tendo identificado as com boa confiabilidade

The research aimed to evaluate the methodology of the Pr oject Tree Vis for determining nutrition iron, boron, zinc and copper in maize plants subjected to doses of these nutrients. Treatments used were made by omission, 1/5, 2/5 and the full dose of the elements with four replicates at each stage of collection, these are V4, V7 and R1. The experiments ware conducted in a greenhouse in hydroponics, conducted in pots with nutrient solution. Was determined the dry mass production of the aerial part and roots, besides the determ ination of nutritional content in the leaves indicative of phenological stages of each harvest time. At each stage were collected images of indicative and new leaves through with a scanner for the analyzes of artificial vision. The increasing doses of nutr ients promoted higher dry mass production in the aerial part and roots and reduced the production when using the highest dose of the nutrient. The artificial vision system showed promise in identifying of deficiency of iron with 77.5% accuracy, of boron with 81.7% of correct, of zinc with 81.0% accuracy and copper with 57.2% accuracy, with a good reliability in the identifi

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Este artículo describe un algoritmo para realzar los bordes en una imagen digital en color basada en la extensión S-CIELAB. Esta extensión implica una serie de filtros espaciales de suavizado en el espacio de color acromático y los colores oponentes para aproximar las funciones de sensibilidad al contraste del sistema visual humano. Los filtros son combinaciones lineales de máscaras gaussianas. En el presente trabajo se combinaron estos filtros espaciales con el operador laplaciano en cada canal opuesto y el canal acromático para obtener la imagen realzada. La imagen que resulta es restada de la imagen original en cada canal por separado y se transforma de nuevo a la representación del color independiente del dispositivo (XYZ) para obtener de esta forma la imagen realzada. Este artículo describe dos ejemplos que usan el algoritmo de realce con imágenes médicas del área oftálmica.

This paper describes an algorithm to sharpen a colour digital image based on S-CIELAB extension. S-CIELAB involves a series of smoothing spatial filters in the opponent color space to approximate the contrast sensitivity functions of the human vision system. The filters are linear combinations of Gaussian masks. The algorithm combines these spatial filters with the Laplacian operator in each opponent channel to obtain the sharpened image. The resulting image is then subtracted from the original image in each opponent channel and back transformed to the device independent representation space (XYZ) to obtain the final sharpened image. This article describes two examples that use the sharpening algorithm with medical images of the ophthalmic area.