Types Of Cameras Research Articles

First Step toward Gestural Recognition in Harsh Environments.

We are witnessing a rise in the use of ground and aerial robots in first response missions. These robots provide novel opportunities to support first responders and lower the risk to people’s lives. As these robots become increasingly autonomous, researchers are seeking ways to enable natural communication strategies between robots and first responders, such as using gestural interaction. First response work often takes place in harsh environments, which hold unique challenges for gesture sensing and recognition, including in low-visibility environments, making the gestural interaction non-trivial. As such, an adequate choice of sensors and algorithms needs to be made to support gestural recognition in harsh environments. In this work, we compare the performances of three common types of remote sensors, namely RGB, depth, and thermal cameras, using various algorithms, in simulated harsh environments. Our results show 90 to 96% recognition accuracy (respectively with or without smoke) with the use of protective equipment. This work provides future researchers with clear data points to support them in their choice of sensors and algorithms for gestural interaction with robots in harsh environments.

Tracking Fluorescent Dye Dispersion from an Unmanned Aerial Vehicle

Commercial unmanned aerial vehicles continue to gain popularity and their use for collecting image data and recording new phenomena is becoming more frequent. This study presents an effective method for measuring the concentration of fluorescent dyes (fluorescein and Rhodamine WT) for the purpose of providing a mathematical dispersion model. Image data obtained using a typical visible-light camera was used to measure the concentration of the dye floating on water. The reference measurement was taken using a laboratory fluorometer. The article presents the details of three extensive measurement sessions and presents elements of a newly developed method for measuring fluorescent tracer concentrations. The said method provides tracer concentration maps presented on the example of an orthophoto within a 2 × 2 m discrete grid.

Cross‐scene pavement distress detection by a novel transfer learning framework

AbstractDeep learning has achieved promising results in pavement distress detection. However, the training model's effectiveness varies according to the data and scenarios acquired by different camera types and their installation positions. It is time consuming and labor intensive to recollect labeled data and retrain a new model every time the scene changes. In this paper, we propose a transfer learning pipeline to address this problem, which enables a distress detection model to be applied to other untrained scenarios. The framework consists of two main components: data transfer and model transfer. The former trains a generative adversarial network to transfer existing image data into a new scene style. Then, attentive CutMix and image melding are applied to insert distress annotations to synthesize the new scene's labeled data. After data expansion, the latter step transfers the feature extracted by the existing model to the detection application of the new scene through domain adaptation. The effects of varying degrees of knowledge transfer are also discussed. The proposed method is evaluated on two data sets from two different scenes with more than 40,000 images totally. This method can reduce the demand for training data by at least 25% when the model is applied in a new scene. With the same number of training images, the proposed method can improve the model accuracy by 26.55%.

Using a trail camera for recording plant-insect interactions with Venus flytrap as an example

Trail cameras can be used for the study of the interaction between plants and insects if they have a close-up lens and the capability of doing time-lapse photographs or videos. Typical trail cameras are mounted vertically, often on unstable camera stands. We have used the Bushnell NatureView HD Cam Model# 119740 and RC Williams Co. TopView Quick-Mount camera-stand to study the behavior of insects interacting with Venus flytraps (Dionaea muscipula). The combination allows photographs and videos to be made of insects and plants from above, with the camera held in a position that allows the plants to be observed from the exact same position for months at a time. Every five minutes a photograph and a one-minute video are recorded. We have developed methods of searching through this data for important events that involve producing “slide shows” of the photographs overlain by a grid, that can be quickly scanned for events of interest. Video of these events near the time of the sequentially numbered photograph can then be viewed. The slides and videos can also be sampled at intervals for more gradual changes.

Calibration of Timing Parameters of Still Image Cameras and Video Cameras by a Two Dimensional LED Array

This paper presents a t wo-dimensional 10 x 10 LED array system developed in-house at t he Standards and Calibration Laboratory (SCL) for the calibration of timing parameters of still image cameras and video cameras. In this paper, the circuit design and the calibration methods for cameras of different shutter types (rolling shutter or global shutter) are presented. This LED array may also be used to verify the shutter type of cameras. The frequency of the clock signal applied in the calibration is traceable to the SI through the cesium beam frequency standard maintained at SCL.

Cinema as a form of composition

Cinema as a form of composition

Michigan ZoomIN: Validating Crowd‐Sourcing to Identify Mammals from Camera Surveys

ABSTRACTCamera trap studies have become a popular medium to assess many ecological phenomena including population dynamics, patterns of biodiversity, and monitoring of endangered species. In conjunction with the benefit to scientists, camera traps present an unprecedented opportunity to involve the public in scientific research via image classifications. However, this engagement strategy comes with a myriad of complications. Volunteers vary in their familiarity with wildlife, thus, the accuracy of user‐derived classifications may be biased by the commonness or popularity of species and user‐experience. From an extensive multi‐site camera trap study across Michigan, U.S.A, we compiled and classified images through a public science platform called Michigan ZoomIN. We aggregated responses from 15 independent users per image using multiple consensus methods to assess accuracy by comparing to species identification completed by wildlife experts. We also evaluated how different factors including consensus algorithms, study area, wildlife species, user support, and camera type influenced the accuracy of user‐derived classifications. Overall accuracy of user‐derived classification was 97%; although, several canid (e.g., Canis lupus, Vulpes vulpes) and mustelid (e.g., Neovison vison) species were repeatedly difficult to identify by users and had lower accuracy. When validating user‐derived classification, we found that study area, consensus method, and user support best explained accuracy. To overcome hesitancy associated with data collected by untrained participants, we demonstrated their value by showing that the accuracy from volunteers was comparable to experts when classifying North American mammals. Our hierarchical workflow that integrated multiple consensus methods led to more image classifications without extensive training and even when the expertise of the volunteer was unknown. Ultimately, adopting such an approach can harness broader participation, expedite future camera trap data synthesis, and improve allocation of resources by scholars to enhance performance of public participants and increase accuracy of user‐derived data. © 2021 The Wildlife Society.

Investigation of thermal processes in hydrogen-oxygen steam generators of kilowatt power class

PURPOSE. Testing of a hydrogen-oxygen steam generator (HOSG) of the kilowatt power class for the study of heat and mass transfer processes. METHODS. At the first stage, the technological system of diagnostics and control was considered, with the help of which preliminary tests were carried out. According to the results of which the structural elements of the HOSG were modernized. Further, at the second stage, an automated process control system was created, which ensured the conduct of multi-mode tests of the HOSG. RESULTS. The design of the HOSG showed its efficiency. The changes in the cooling water flow rate, pressure and temperature in the evaporation chamber during multi-mode tests are presented, as well as the generalized results of experimental studies, which show the dependence of the steam temperature on the mass fraction of water at different coefficients of the excess oxidizer.CONCLUSION. During the preliminary tests, the development of upgraded components of the HOSG was carried out, ensuring an increase in the efficiency of its operation. The created automated control system made it possible to successfully conduct subsequent multi-mode tests with two different types of cameras. The indicators of unreacted hydrogen are comparable to those already achieved in existing devices. The characteristic transition times from mode to mode show compliance with the requirements for creating autonomous power plants based on renewable energy sources.

UAV Photogrammetry under Poor Lighting Conditions-Accuracy Considerations.

The use of low-level photogrammetry is very broad, and studies in this field are conducted in many aspects. Most research and applications are based on image data acquired during the day, which seems natural and obvious. However, the authors of this paper draw attention to the potential and possible use of UAV photogrammetry during the darker time of the day. The potential of night-time images has not been yet widely recognized, since correct scenery lighting or lack of scenery light sources is an obvious issue. The authors have developed typical day- and night-time photogrammetric models. They have also presented an extensive analysis of the geometry, indicated which process element had the greatest impact on degrading night-time photogrammetric product, as well as which measurable factor directly correlated with image accuracy. The reduction in geometry during night-time tests was greatly impacted by the non-uniform distribution of GCPs within the study area. The calibration of non-metric cameras is sensitive to poor lighting conditions, which leads to the generation of a higher determination error for each intrinsic orientation and distortion parameter. As evidenced, uniformly illuminated photos can be used to construct a model with lower reprojection error, and each tie point exhibits greater precision. Furthermore, they have evaluated whether commercial photogrammetric software enabled reaching acceptable image quality and whether the digital camera type impacted interpretative quality. The research paper is concluded with an extended discussion, conclusions, and recommendation on night-time studies.

Vision system check for authentication of quality of industry automation for detection of system parts using raspberry pi

Typical tasks of vision systems are included in Recognition area of work – object recognition, identification, detection, content-based image retrieval, pose estimation, optical character recognition (OCR), 2-D encoding and decoding, shape recognition technology, facial recognition and many more. The process followed in order to attain these tasks generally follows the same pattern. First step is image acquisition using various types of cameras according to the requirements (light-sensitive cameras, range sensors, ultrasonic cameras and many more) and acquiring pixel intensities corresponding to light intensity or other measures. Followed by preprocessing in order to reduce noise, increase contrast, resample or scaling using filters, histogram equalization, re-sampling algorithms.

The effects of high-tech cameras on sports consumers' viewing experiences: the moderating role of sports involvement

PurposeBased on the limited-capacity model of motivated mediated message processing (LC4MP), this study aims to examine the effects of different camera types on the viewing experiences of sports consumers and their intention to follow social media channels, depending on their level of sports involvement.Design/methodology/approachThe design for this experiment utilized 2 (highly involved vs less involved sports consumers) × 2 (high-tech vs non-high-tech cameras) between-subjects design. Overall, 289 US-based sports consumers participated in this experiment.FindingsThe results indicate that sports videos recorded by high-tech cameras lead to greater perceived novelty than those recorded by non-high-tech cameras, thus enhancing sports consumers' intention to follow social media channels, especially when consumers are highly involved with the target sports. However, sports consumers who are less involved with particular sports become distracted during their viewing experiences when high-tech cameras are used to broadcast games; these consumers' intentions to follow social media channels are not strengthened.Originality/valuePrevious studies have focused mainly on the positive effects of high-tech cameras on the viewing experiences of sports consumers. Based on the LC4MP, the authors extend the existing literature not only by examining the mechanism that underpins the positive effects of high-tech cameras, but also by identifying a situation where the use of high-tech cameras results in a negative viewing experience for sports consumers.

Model-independent light field reconstruction using a generic camera calibration

Abstract Sophisticated and highly specialized optical measuring devices are becoming increasingly important for high-precision manufacturing and environment perception. In particular, light field cameras are experiencing an ever-increasing interest in research and industry as they enable a variety of new measurement methods. Unfortunately, due to their complex structure, their calibration is very difficult and usually precisely tailored to the particular type of light field camera. To overcome these difficulties, we present a method that decodes a light field from the raw data of any light field imaging system without knowing and modeling the internal optical elements. We calibrate the camera using a precise generic calibration method and transform the obtained ray set into an equivalent light field representation. Finally, we reconstruct a rectified light field from the irregularly sampled data and in addition we derive the geometric ray properties as intrinsic camera parameters. Experimental results validate the method by showing that both the information of the observed scene and the geometric structure of the light field are preserved by an adequate rectification and calibration.

Design and Implementation of 2D MIMO-Based Optical Camera Communication Using a Light-Emitting Diode Array for Long-Range Monitoring System.

Wireless technologies that use radio frequency (RF) waveforms are common in wireless communication systems, such as the mobile communication, satellite system, and Internet of Things (IoT) systems. It is more advantageous than wired communication because of the ease of installation. However, it can negatively impact human health if high frequencies are used to transmit data. Therefore, researchers are exploring the potential of optical wireless communication as an alternative, which uses the visible light bandwidth instead of RF waveforms. Three possibilities are being investigated: visible light communication, light fidelity, and optical camera communication. In this paper, we propose a multiple-input multiple-output modulation scheme using a light-emitting diode (LED) array, which is applicable to the IoT system, based on on–off keying modulation in the time domain. This scheme is compatible with the two popular types of camera in the market, rolling shutter cameras and global shutter cameras, as well as the closed-circuit television camera, which is used in factories, buildings, etc. Despite the small size of the LED array, implementing this scheme with 10 links in different positions at a communication distance of 20 m is possible for efficient performance (low error rate) by controlling the exposure time, shutter speed, focal length, channel coding and applying the matched filter.

A novel view synthesis approach based on view space covering for gait recognition

Gait recognition has proven to be effective for long-distance human recognition. View angle, one form of the gait variations, can change the human appearance greatly and reduce its performance. For most existing gait datasets, the angle interval between the two nearest views is large. This means that the angle does not cover the entire view space and prevent better view-invariant feature extraction for CNN. Additionally, the angles between cameras and people vary widely in typical camera deployments for monitoring people. In this paper, we, therefore, propose a novel view synthesis approach based on view space covering to deal with the challenge of large-angle interval. Specifically, a Dense-View GEIs Set (DV-GEIs) is introduced to expand this view approach, from 0° to 180° with 1° interval. GEI is a popular feature representation for gait, which can be obtained by aligning human silhouettes and averaging them in a gait cycle. In order to synthesize DV-GEIs set, Dense-View GAN (DV-GAN) is proposed to model the gait attribute distribution and generate new GEIs with various views. DV-GAN consists of a generator, discriminator, and monitor, where the monitor is designed to preserve human identification and view information. Compared with our previous work DV-GAN-pre, we add a center for each object in the monitor to improve the discriminative capability of synthesized images during the modeling process. The proposed method is evaluated on the CASIA-B and OU-ISIR dataset. The experimental results show that view space covering is an effective way to light the burden of view-invariant feature extraction for CNN and make the feature more discriminative. We believe the idea of view space covering will further improve the development of gait recognition.

Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications

Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.

The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications.

In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol , which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of ~66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.

Generating Spectrum Images from Different Types — Visible, Thermal, and Infrared Based on Autoencoder Architecture (GVTI-AE)

Recently, numerous challenging problems have existed for transforming different image types (thermal infrared (TIR), visible spectrum, and near-infrared (NIR)). Other types of cameras may lack the ability and features of certain types of frequently-used cameras that produce different types of images. Based on camera features, different applications might emerge from observing a scenario under specific conditions (darkness, fog, night, day, and artificial light). We need to jump from one field to another to understand the scenario better. This paper proposes a fully automatic model (GVTI-AE) to manipulate the transformation into different types of vibrant, realistic images using the AutoEncoder method, which requires neither pre-nor post-processing or any user input. The experiments carried out using the GVTI-AE model showed that the perceptually realistic results produced in the widely available datasets (Tecnocampus Hand Image Database, Carl dataset, and IRIS Thermal/Visible Face Database).

Retinal Image Processing on Early Glaucoma Detection

Glaucoma is a habitual eye disorder which harms eye’s second cranial nerve. There are millions of second cranial nerves. The main function of these types of nerves is to sending captured visual information from retina to the brain. The escalate pressure in the human eye leads to Glaucoma. This heavy pressure is known as intraocular pressure. This heavy pressure leads to damage eye's optic nerve head and retina continuously further it tends to vision loss. In this paper there are two datasets including both normal person and affected person's eye color images. The principal aim of this project is to compare the color of the eye with these two datasets. A special camera which is attached to less power microscope is called fundus camera or retinal camera. The images captured by this type of fundus camera is called fundus picture[1]. It is a high dimensional laser image. MATLAB software tool is used to fulfill the feature extraction of these fundus images. A color pixel in the affected area of the person is measured to check whether a person is Glaucomatous or not. If the final result is positive then it is Glaucoma.

Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification

Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine learning methods. We acquired multispectral images over an almond plantation using a UAV. First, a multispectral orthoimage was generated from the acquired multispectral images using SfM (Structure from Motion) photogrammetry methods. Eleven types of vegetation indexes were proposed based on the multispectral orthoimage. Then, 593 data points that contained multispectral bands and vegetation indexes were randomly collected and prepared for this study. After comparing six machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, Linear Discrimination Analysis, Decision Tree, Random Forest, and Gradient Boosting), we selected three (SVM, KNN, and LDA) to study the fusion of multi-spectral bands information and derived vegetation index for classification. With the vegetation indexes increased, the model classification accuracy of all three selected machine learning methods gradually increased, then dropped. Our results revealed that that: (1) spectral information from multispectral images can be used for machine learning-based ground classification, and among all methods, SVM had the best performance; (2) combination of multispectral bands and vegetation indexes can improve the classification accuracy comparing to only spectral bands among all three selected methods; (3) among all VIs, NDEGE, NDVIG, and NDVGE had consistent performance in improving classification accuracies, and others may reduce the accuracy. Machine learning methods (SVM, KNN, and LDA) can be used for classifying almond plantation using multispectral orthoimages, and fusion of multispectral bands with vegetation indexes can improve machine learning-based classification accuracy if the vegetation indexes are properly selected.

Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography.

Purpose The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. Method The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed. Result The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively. Conclusion The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.