Mobile Camera Research Articles

Nasal Light Reflex: A Useful Intraoperative Tool in Correction of Cleft Lip Nasal Deformity.

The correction of a unilateral cleft lip nasal deformity remains a challenge to cleft surgeons. It is difficult to obtain a routinely predictable outcome. This is in part due to there being no objective intraoperative method to assess the correction. We have come up with a simple objective intraoperative method to plan and assess the correction of the nasal deformity. This is done by assessing the nasal light reflex using a mobile phone camera to define the deformity. The points of the desired correction of the lower lateral cartilage are transposed onto the patient. Once the lip and nose repair is completed, another photograph is taken to assess the nasal light reflex and assess the extent of correction. If this is inadequate, further nasal correction is performed. We have used this procedure in 122 cleft lip patients with 93 complete and 29 incomplete. We have found this to be a useful objective intraoperative method to assist in obtaining improved nasal correction in cleft lip patients. As is well known, relapse of the nose is common in cleft lip repairs, but this method allows one to better gauge the correction at the primary surgery. Nasal light reflex should be added to the armamentarium of cleft surgeons to assist in superior correction of the nose, which is an item that continues to vex these surgeons.

A Study of Onychoscopic Findings in Various Dermatological Disorders

Context: About 10% of all the dermatological conditions are comprised nail disorders. It is challenging for the dermatologists to diagnose nail disorders quickly without the help of invasive procedures. Aims: The aim of the study was to evaluate onychoscopic patterns associated with various nail disorders. Settings and Designs: Hospital-based cross-sectional observational study was conducted at a tertiary care teaching institute in Maharashtra, India from November 2019 to May 2021. Materials and Methods: One hundred and fifty patients, with a history and clinical presentation suggestive of nail disorders, were recruited in the study after confirming the clinical diagnosis. Patients having secondarily modified lesions were excluded. Dry as well as wet onychoscopy using polarized and nonpolarized modes was performed with the help of DermLite 4 dermoscope with an attached OnePlus 8T mobile phone camera to capture and save the dermoscopic images. Data were compiled and statistically analyzed using SPSS version 21.0. Statistical Analysis Used: Categorical variables were expressed in frequency and percentages. Data were entered in Microsoft Excel and analyzed using SPSS version 20. P value (using Z-test for proportions) was calculated wherever needed. Results: The majority of the patients were in the age group of 21–50 years with slight male preponderance. Overall most common onychoscopic findings studied in all the disorders were the nail plate alterations followed by chromonychia. The most common nail changes associated with connective tissue disorders were changes in the proximal nail fold capillaries along with changes in the curvature. The most common change observed in onychomycosis was chromonychia. Aurora borealis, spikes, and jagged edges of onycholytic plate with ruin pattern of hyperkeratosis were specific to onychomycosis. Inflammatory disorders of the nail unit (e.g., lichen planus and nail psoriasis) showed nail plate alterations with increased nail plate fragility. Conclusions: Some onychoscopic patterns are observed consistently with certain diseases, and these can be used for their diagnosis, hence, avoiding unnecessary biopsies, thus improving the patient outcome.

Eye Based Interactive Communication System

Eye-based communication systems, like the Blink-To-Speak language, are essential tools for individuals with motor neuron disorders, enabling them to articulate their needs and emotions. These systems leverage eye movements as a means of interaction, providing a vital communication channel for those with severe speech impairments. By interpreting specific eye gestures, users can effectively convey their thoughts, fostering a sense of independence and improving their quality of life. Many of the existing eye-tracking technologies tend to be complex and costly, limiting their accessibility in low-income regions. Using a mobile phone camera, the system captures real-time video frames to detect and track the user’s eyes, leveraging advanced facial landmark detection. Four primary eye movements—Left, Right, Up, and Blink—form the basis of the Blink-To-Live communication model, allowing users to communicate a range of more than 60 daily commands. By translating sequences of three eye gestures, the system generates readable sentences that are displayed on a screen and can be vocalized through synthesized speech. Unlike other sensor-dependent systems, Blink-To-Live is designed for affordability, flexibility, and ease of use, making it accessible without the need for specialized software or hardware. A prototype has been tested on a range of participants, showing positive results in terms of ease of use, flexibility, and cost-effectiveness

사진 편집 앱의 색상 조정 기능의 효율성 향상을 위한 UI 디자의 비교분석 연구 : VSCO, Hypic, Lightroom, Snapseed를 중심으로

With the development of smartphones, there are more and more kinds of functions, among which the most used by users is the camera.In fact, Some smartphones even use the photo function as the main means of promotion. As smartphone cameras advance, so does their popularity. A key feature in photo editing apps is color adjustment, which lets users tweak the mood and style of their images. A well-crafted UI is crucial for making this feature efficient, affecting how easily users can find and use it, and their overall visual and interactive experience. This study zeroes in on the UI design of color adjustment in four top photo editing apps: VSCO, Hypic, Lightroom, and Snapseed. The findings are straightforward: First, there's a clear variation in how these apps navigate, design icons, and lay them out. Second, each app excels in organizing content and has its own distinct flair. Third, by examining their weaknesses and conducting market research, the study offers suggestions to enhance UI efficiency for color adjustment. Finally, it is hoped that this will be helpful to mobile platforms that want to create an overall user experience that allows users to quickly and accurately use the features they need and are accustomed to.

Estimating Rainfall Intensity Using an Image-Based Convolutional Neural Network Inversion Technique for Potential Crowdsourcing Applications in Urban Areas

High-quality rainfall data are essential in many water management problems, including stormwater management, water resources management, and more. Due to the high spatial–temporal variations, rainfall measurement could be challenging and costly, especially in urban areas. This could be even more challenging in tropical regions with their typical short-duration and high-intensity rainfall events, as some of the undeveloped or developing countries in those regions lack a dense rain gauge network and have limited resources to use radar and satellite readings. Thus, exploring alternative rainfall estimation methods could be helpful to back up some shortcomings. Recently, a few studies have examined the utilisation of citizen science methods to collect rainfall data as a complement to the existing rain gauge networks. However, these attempts are in the early stages, and limited works have been published on improving the quality of such data. Therefore, this study focuses on image-based rainfall estimation with potential usage in citizen science. For this, a novel convolutional neural network (CNN) model is developed to predict rainfall intensity by processing the images captured by citizens (e.g., by smartphones or security cameras) in an urban area. The developed model is merely a complementary sensing tool (e.g., better spatial coverage) to the existing rain gauge network in an urban area and is not meant to replace it. This study also presents one of the most extensive datasets of rain image data ever published in the literature. The estimated rainfall data by the proposed CNN model of this study using images captured by surveillance cameras and smartphone cameras are compared with observed rainfall by a weather station and exhibit strong R2 values of 0.955 and 0.840, respectively.

Automated analysis of digital medical images in cervical cancer screening: A systematic review.

Cervical cancer screening programs are poorly implemented in LMICs due to a shortage of specialists and expensive diagnostic infrastructure. To address the barriers of implementation researchers have been developing low-cost portable devices and automating image analysis for decision support.However, as the knowledge base is growing rapidly, progress on the implementation status of novel imaging devices and algorithms in cervical cancer screening has become unclear. The aim of this project was to provide a systematic review summarizing the full range of automated technology systems used in cervical cancer screening. A search on academic databases was conducted and the search results were screened by two independent reviewers. Study selection was based on eligibility in meeting the terms of inclusion and exclusion criteria which were outlined using a Population, Intervention, Comparator and Outcome framework. 17 studies reported algorithms developed with source images from mobile device, viz. Pocket Colposcope, MobileODT EVA Colpo, Smartphone Camera, Smartphone-based Endoscope System, Smartscope, mHRME, and PiHRME. While 56 studies reported algorithms with source images from conventional/commercial acquisition devices. Most interventions were in the feasibility stage of development, undergoing initial clinical validations. Researchers have proven superior prediction performance of computer aided diagnostics (CAD) in colposcopy (>80% accuracies) versus manual analysis (<70.0% accuracies). Furthermore, this review summarized evidence of the algorithms which are being created utilizing portable devices, to circumvent constraints prohibiting wider implementation in LMICs (such as expensive diagnostic infrastructure). However clinical validation of novel devices with CAD is not yet implemented adequately in LMICs.

Real-Time Table Tennis Scoring and Player Position Tracking System Using Computer Vision

This study focuses on developing a system that uses state-of-the-art computer vision techniques to improve the accuracy and efficiency of table tennis tracking. The table tennis ball's trajectory and movement are tracked automatically by the system using a mobile camera array, allowing for real-time score updates without the need for human participation. This method guarantees precise and seamless scoring, freeing players from worrying about maintaining score and allowing them to concentrate fully on the game. The system uses player pose estimation technologies in addition to real-time scoring to track and record player positions and motions during the match. The system may provide a thorough study of player motions, including tracking posture and finding important motion patterns, by gathering and analyzing skeletal data.

RGB‐based indices for estimating cover crop biomass, nitrogen content, and carbon:nitrogen ratio

AbstractPlant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low‐cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high‐throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red‐green‐blue (RGB) indices collected with standard settings in non‐standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R2 = 0.554, root mean square error [RMSE] = 219.29 kg ha−1), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R2 = 0.573, RMSE = 3.5 g kg−1) and carbon:nitrogen ratio (R2 = 0.574, RMSE = 1.29 g g−1). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.

Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques

This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets.

A simple color absorption analysis of colorimetric loop-mediated isothermal amplification for detection of Raillietina spp. in clinical samples using a 3D-printed tube holder coupled with a smartphone camera and notebook screen.

Asimple method has been developedfor semi-quantitative analysis of the colorimetric output of loop-mediated isothermal amplification (LAMP) using a 3D-printed tube holder with a smartphone and notebook for the detection of Raillietina, which is the cause of Raillietiniasis affecting free-range chicken farming. In this method, a light is directedfrom a notebook screen to the LAMP products in the tube holder and the color absorption of the LAMP products is measured by using the appropriate smartphone application. It was found that the malachite green dye-coupled LAMP (MaG-LAMP) assay showed the highest sensitivity and specificity for detecting Raillietina without any cross-reaction with other related parasites and hosts. The limit of detection was 10fg/μL of DNA. A total of 60 fecal samples were infectively confirmed by microscopic examination and the results of microscopy compared with those of MaG-LAMP and triplex PCR assays. Microscopy and MaG-LAMP based on the color absorption demonstrated high agreement in Raillietina detection with kappa = 1. Rapid, simple, cost-effective, and easy interpretation of colorimetric LAMP assays and their high sensitivity make them superior to PCR and morphological investigation, demonstrating the feasibility of this assay in point-of-care screening to support farm management and solve chicken health problems. Our study presentsis an alternative diagnostic method using semi-quantitative analysis of colorimetric LAMP based on the differing solution color absorptions between positive and negative reactions for infectious disease diagnosis.

Tunable Lens Driven by Electrohydrodynamic Pumping

Developing soft actuators has emerged as one of the most promising areas in robotics, with a growing demand for tunable lenses driven by the rapid advancements in intelligent mobile devices and cameras. Traditional mechanical lens systems are characterized by their heaviness, complexity, and rigidity. To address these limitations, this research introduces a novel approach: a dual‐chamber liquid lens actuated by an electrohydrodynamic (EHD) pump. The study involves the fabrication of a planar EHD pump, with an experimental investigation into the impact of electrode gap, electrode width, and electrode‐pair gap on the pump's pressure. Following the optimization of the EHD pump, a dual‐chamber lens is constructed, comprising two chambers, a rigid transparent plate, and two prestretched elastic membranes, which are connected to the EHD pump via tubes. Additionally, a mathematical model is developed to predict the focal length of the lens. Both experimental and theoretical analyses are conducted to explore the effects of the prestretch ratio of the elastic membranes and the radius ratio of the two chambers on focal length variation. The theoretical predictions of the focal length–voltage curves align closely with the experimental findings, providing validation for the proposed theoretical model.

Asymmetric Stereo High Dynamic Range Imaging with Smartphone Cameras.

Stereo high dynamic range imaging (SHDRI) offers a more temporally stable solution to high dynamic range (HDR) imaging from low dynamic range input images compared to bracketing and removes the loss of accuracy that single-image HDR solutions offer. However, few solutions currently exist that take advantage of the different (asymmetric) lenses, commonly found on modern smartphones, to achieve SHDRI. This paper presents a method that achieves single-shot asymmetric HDR fusion via a reference-based deep learning approach. Results demonstrate a system that is more robust to aperture and image signal processing pipeline differences than existing solutions.

Identifying freshness of various chilled pork cuts using rapid imaging analysis.

Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance. This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively. Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.

Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction.

Smart indoor tourist attractions, such as smart museums and aquariums, require a significant investment in indoor localization devices. The use of Global Positioning Systems on smartphones is unsuitable for scenarios where dense materials such as concrete and metal blocks weaken GPS signals, which is most often the case in indoor tourist attractions. With the help of deep learning, indoor localization can be done region by region using smartphone images. This approach requires no investment in infrastructure and reduces the cost and time needed to turn museums and aquariums into smart museums or smart aquariums. In this paper, we propose using deep learning algorithms to classify locations based on smartphone camera images for indoor tourist attractions. We evaluate our proposal in a real-world scenario in Brazil. We extensively collect images from ten different smartphones to classify biome-themed fish tanks in the Pantanal Biopark, creating a new dataset of 3654 images. We tested seven state-of-the-art neural networks, three of them based on transformers. On average, we achieved a precision of about 90% and a recall and f-score of about 89%. The results show that the proposal is suitable for most indoor tourist attractions.

Development of a smartphone-based colorimetric detection for quantification of vitamin C in dietary supplements and skin care products

This research project presents a development of an assay tool for quantification of vitamin C in dietary supplements and skin care products. The proposed method was developed based on decolorization of acidic potassium permanganate (KMnO4) by vitamin C through a redox reaction, with subsequent colorimetric detection by using digital imaging. The imaging was carried out using smartphone camera and free-downloaded RGB color detector application. To obtain optimal conditions, influential parameters such as light source position installed inside a 19 cm × 46 cm × 10 cm lightbox, reagent concentrations, and reaction time were studied. To the best conditions, good linearity (r2 &gt; 0.99) in the range 1–70 mg vitamin C/L was obtained. Relative standard deviation was less than 3.9% indicating high precision. The proposed method was validated by quantification of vitamin C content in 15 sample solutions using the proposed method in comparison with the reference iodometric titration method. The results obtained from those two methods were not significantly different using paired t-test at confidence level of 95% (texp=0.41, tcrit=2.14). The results indicated that utilizing smartphone camera with free-downloaded color detector application is possible as an alternative analysis tool for determination of vitamin C.

Myoglobin redox form prediction in fresh beef using computer vision systems and artificial intelligence

To development a computer vision system (CVS) to determine the myoglobin redox forms on beef surfaces, the reflectance spectra of reference samples of deoxymyoglobin (DMb), oxymyoglobin (OMb), and metmyoglobin (MMb) were recorded, and the surface images captured by a digital camera (CVScam) and a cell phone camera (CVScel) to train the algorithm. Meat color changes during blooming and display storage were also recorded. Higher k-fold accuracy was observed for CVScam (90.98 %) than for CVScel (86.53 %), with significantly correlation with colorimeter for OMb (r = 0.77 and 0.71), DMb (r = 0.84 and 0.71), and MMb (r = 0.87 and 0.88). The CVS MMb was lower and the OMb was higher than colorimeter, but the redox form behaviors were more consistent with the expected chemical changes on the surface. The constructed CVS showed satisfactory performance as a useful and accurate tool for predicting the myoglobin redox forms on the beef surface.

Technology pearl: Utilizing dermatoscope adapter to obtain high-resolution histopathologic photographs with modern multilens smartphone cameras

Technology pearl: Utilizing dermatoscope adapter to obtain high-resolution histopathologic photographs with modern multilens smartphone cameras

A potential biomarker for age-related macular degeneration disease: iris freckles

BackgraundTo determine the potential relationship between age-related macular degeneration and iris freckles.MethodIn this case-control study, iris photographs of 300 eyes of 300 patients diagnosed with age-related macular degeneration and 300 eyes of 300 healthy volunteers were obtained with the help of a high-resolution mobile phone camera. The evaluated iris photographs were classified according to the Descriptive Iris Color Classification Scale.ResultsThe average age of the AMD group is 73.05 ± 6.93, and the average age of the control group is 73.43 ± 5.72. (p = 0.124) While freckles were present in 200 (66.7%) of the patients in the AMD group, freckles were not observed in 100 patients (33.3%) of AMD group. While freckles were present in 142 (47.3%) of the patients in the control group, freckles were not observed in 158 of control group(52.7%). There was a significant difference in the presence of freckles between the two groups. (p < 0.001) The average number of freckles in the AMD group was 3.97 ± 3.07, and the number of freckles in the control group was 3.06 ± 2.55. (p = 0.001)ConclusionWe think that evaluation of iris details, especially the presence of iris freckles, should be used routinely in age-related macular degeneration screening. The risk of age-related macular degeneration can be predicted by evaluating iris details, which is an easy and inexpensive method.

Toward Controllable Self‐Reduction of Mn4+ to Mn2+ by Lanthanide Ions for Luminescence Based Colorimetric Sensing of Temperature

AbstractLuminescence intensity ratio (LIR) based optical thermometers have attracted lots of attention. In case of color tunable luminescence with the variation of LIR, “RGB colorimetry” based on convenient temperature sensing using a smartphone or digital camera is available. However, narrow emission bands with primary colors are needed for avoiding color crosstalk and achieving high sensitivities. In this paper, Mn4+ and Mn2+ coactivated CaAl12O19 (CAO) phosphors are reported that show a narrow red emission of Mn4+ and green one of Mn2+ with the number of the two ions controlled by lanthanide ions through manipulation of self‐reduction of Mn4+ to Mn2+ in air. The self‐reduction in CAO via the addition of Ce4+ is mainly studied. First‐principles calculation shows that the self‐reduction is driven by Ca vacancies that are produced in aliovalent substitution of Ce4+ for Ca2+ and can reduce the formation energy of Mn2+. The comparative study of temperature sensing based on LIR and RG colorimetry is conducted. The relative sensitivity based on LIR is high as 4.5% K−1 at 373 K and that for RG colorimetry can reach 2% K−1 at 350 K. These findings provide a strategy for manipulation of manganese valance states and a phosphor for RG colorimetry temperature sensing.

Features of Bioluminescence Dynamics of Photobacterium phosphoreum IMV B-7071

The problem of the prolonged and stable intensity of bioluminescent signals is relevant in the development of any test systems that use biological objects. The aim of this work was to study the features of bioluminescence dynamics of Photobacterium phosphoreum IMV B-7071 in a liquid and on different stationary media. Methods. Bioluminescence studies were performed in liquid, agarose, and cellulose-cotton media. Bacterial suspensions were cultivated at 21°C in the mediums with standard composition. We studied both the background glow and its dynamics under conditions of mixing a liquid medium. Bioluminescence was recorded using digital photography with subsequent image processing of the samples. The measurements of luminescence were made by digital photo or video recording using Olympus digital camera SP560UZ, CANON 700D, and mobile device camera Samsung Galaxy 9 Note with specialized applications for mobile devices "Colorimeter (Lab Tools Apps)" and Camera Color Counter (Keuwsoft) at maximum light sensitivity in the automatic white balance mode at a fixed distance from the sample. Image processing was carried out using ImageJ and Origin Pro. Spectra of bacterial luminescence and its dynamics over time were measured using an LOMO MDR-23 spectrometer in the range of 200–750 nm. Results. The results of the study prove that in aqueous or solid agar and also on cellulose cotton medium, the intensity of bioluminescence of P. phosphoreum gradually increases, reaching a maximum within approximately 2 days, after which it slowly fades. It was established that the bioluminescence of photobacterium P. phosphoreum is a non-stationary process and has characteristic features of temporal dynamics associated with both the dynamics of the oxygen concentration in the environment of bacterial suspensions and the dynamics of the bacterial population density. Analysis of the luminescence spectra of bacteria shows that luminescence occurs mainly in the blue and green regions of the spectrum with luminescence maxima in the range of 460–520 nm, but the ultra-weak glow is also registered in the UV and red spectral ranges. The variability of photobacterial luminescence spectra over time in the spectral ranges of the main luminophores causes color fluctuations between the blue and green ranges. Conclusions. The key parameters of multi-day background and short-term induced bioluminescence dynamics of photobacteria in different environments were clarified, and the certain variability of the spectral characteristics of luminescent radiation over time was shown. The revealed features of the dynamics of the bioluminescence of P. phosphoreum must be taken into account in practical application to assess the toxicity of substances of various nature, as well as in environmental monitoring.