Changes In Ambient Light Research Articles

Fast visual inertial odometry with point–line features using adaptive EDLines algorithm

In mainstream visual inertial odometry (VIO) systems, the method of positional solution by feature point extraction and matching in the image is widely used. However, the tracking accuracy of point features is dependent on the texture richness in the environment. Although many existing algorithms introduce line features in the front end to improve the system’s environmental adaptability, most of them sacrifice system real-time in exchange for higher positioning accuracy. The extraction and matching of line features often require more time, thus failing to meet the real-time requirements of the system for localization. In this paper, we therefore propose a fast VIO fused with point and line features, which enables the system to maintain a high level of positioning robustness in dim and changing light environments with low time cost. The point–line features VIO algorithm is based on adaptive thresholding of EDLines. By adding an adaptive thresholding component to the EDLines algorithm, the robustness of line feature extraction is enhanced to better adapt to changes in ambient lighting. The time needed for line feature extraction is also significantly reduced. A line feature matching algorithm based on geometric information and structural similarity is proposed, which enables fast and accurate line feature matching. The algorithm is compared with point-line visual-inertial odometry and monocular visual-inertial state estimator algorithms on the European robotics challenge dataset and real-world scenes. Many experiments prove that the algorithm has improved in both real time and accuracy.

681-P: Evaluation of Data Processing Performance for Subcutaneous Glucose Monitoring with an Optical Investigational Device

Background and Aims: Lumee Glucose is a small soft glucose-sensitive hydrogel (5.0 x 0.75 x 0.65mm hydrated) currently in clinical development and intended for permanent subcutaneous placement by injection to continuously monitor glucose. The sensor is comprised of a hydrogel scaffold containing fluorescent molecules that produce near infrared light proportional to glucose concentration when interrogated by an optical skin reader placed over the hydrogel. The reader utilizes a set of LEDs to excite the fluorophores within the hydrogel and communicates wirelessly with a tablet. Machine learning and quality data processing algorithms are also being assessed to establish monitoring accuracy using MARD. Methods: Glucose monitoring including a carbohydrate-rich meal was conducted during 8-hour in-clinic visits in 28 subjects with insulin-treated diabetes (female, 48 yrs [20-82], BMI 26.2kg/m2 [19.7-40.3]) over a 3-month period. Frequent venous blood glucose measurements were obtained as reference (Super GL analyzer) . Sensor signals from a total of 188 monitoring traces and 3030 paired values were subjected to retrospective data processing with 3-point glucose reference calibration using machine learning and quality filters to remove potentially compromised optical signals. Results: The average MARD for all 3030 paired values during the 6 to 91-day monitoring period was 12.3%. The MARD was 11.8% when applying additional signal quality filters. Conclusions: Following injection, the hydrogel is suitable for long-term non-invasive glucose monitoring for a minimum period of three months. Projected performance parameters are within the range of currently available glucose monitoring devices. Quality signal processing algorithms revealed marginal performance improvements and should be further optimized with respect to compensating for signal perturbations resulting from motion, temperature, and ambient light changes. Disclosure J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker’s Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. K.Rebrin: Stock/Shareholder; Profusa, Inc. R.Siegl: None. A.K.Reinhard: None. J.Kopanz: None. A.Simic: None. M.Hartmann: None. S.J.Gamsey: None. S.Banerjee: None. U.Kamecke: Consultant; Profusa, Inc.

RoiSeg: An Effective Moving Object Segmentation Approach Based on Region-of-Interest with Unsupervised Learning

Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n × n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes.

A Portable, Cost-Effective and User-Friendly Instrument for Colorimetric Enzyme-Linked Immunosorbent Assay and Rapid Detection of Aflatoxin B1.

Food analysis based on the enzyme-linked immunosorbent assay (ELISA) is simple, sensitive and rapid, but requires a costly colorimetric instrument. The aim of this work was to develop a portable, low-cost and user-friendly colorimetric instrument for colorimetric ELISA and aflatoxin B1 (AFB1) detection. The principle of the developed instrument was employing a light-emitting diode to generate the signal light and using a light-dependent resistor to measure the signal light absorbed by the oxidized 3,3′,5,5′-tetramethyl benzidine. The absorption spectra revealed that the solution absorbed signal light more strongly after reaction with H2SO4, and blue light would be favorably absorbed. Evaluations on the stability and accuracy of the instrument and interference from ambient light showed that the fabricated instrument was stable, accurate, capable of quantitative detection and insensitive to ambient light changes. In addition, this instrument is user-friendly since it could calculate and report the final amount of AFB1 to the operator. Measurements of maize and peanuts showed that the instrument provided as accurate results as the professional equipment. With the low fabrication cost (about RMB 129 or USD 20), portability, and user-friendliness, this instrument presents attractive potential in the rapid detection of AFB1.

Ultrasound for Gaze Estimation-A Modeling and Empirical Study.

Most eye tracking methods are light-based. As such, they can suffer from ambient light changes when used outdoors, especially for use cases where eye trackers are embedded in Augmented Reality glasses. It has been recently suggested that ultrasound could provide a low power, fast, light-insensitive alternative to camera-based sensors for eye tracking. Here, we report on our work on modeling ultrasound sensor integration into a glasses form factor AR device to evaluate the feasibility of estimating eye-gaze in various configurations. Next, we designed a benchtop experimental setup to collect empirical data on time of flight and amplitude signals for reflected ultrasound waves for a range of gaze angles of a model eye. We used this data as input for a low-complexity gradient-boosted tree machine learning regression model and demonstrate that we can effectively estimate gaze (gaze RMSE error of 0.965 ± 0.178 degrees with an adjusted score of 90.2 ± 4.6).

A Novel Gaze Input System Based on Iris Tracking With Webcam Mounted Eyeglasses

AbstractDue to the high cost of eye-tracking systems based on pupillary corneal reflections, efforts to develop a webcam-based eye-tracking system have increased to provide an affordable alternative to disabled people in recent years. However, due to the camera specification and location, ambient light changes and positional changes of the users, the gazing point of the eyes has not yet been determined precisely by such a system. Therefore, only 8 different gaze directions or up to 10 gaze regions could be detected in the previous webcam-based human–computer interaction studies. In this study, a novel gaze input system has been proposed to make the best use of the limited performance of webcam-based eye tracking and offer an economical alternative for disabled people. To reduce the impact of head movements, the webcam has been mounted to an ordinary glasses frame and positioned in front of the eye. For estimation of the gaze regions, a feature-based method (Hough transformation) was used by considering the circular shape of the iris and the contrast between the iris and sclera. The central coordinates of the iris image captured by the webcam were given to the k-nearest neighbor classifier. We performed a series of experiments with 20 subjects to determine the performance of the system and to investigate the effect of ambient light on the system’s accuracy. The 23 regions that were gazed at by subjects were determined with an average accuracy of 99.54%. When the ambient light level was reduced by half, the accuracy decreased to 94.74%. As a result, it has been found that the proposed prototype allows more accurate recognition of a larger number of regions on the screen than previous webcam-based systems. It has been observed that system performance decreases if the ambient light is reduced byhalf.

Pyramidal Neurons of the Zebrafish Tectum Receive Highly Convergent Input From Torus Longitudinalis.

The torus longitudinalis (TL) is a midbrain structure unique to ray finned fish. Although previously implicated in orienting behaviors elicited by changes in ambient lighting, the role of TL in visual processing is not well-understood. TL is reciprocally connected to tectum and is the only known source of synaptic input to the stratum marginalis (SM) layer of tectal neuropil. Conversely, tectal pyramidal neurons (PyrNs) are the only identified tectal neuron population that forms a dendrite in SM. In this study we describe a zebrafish gal4 transgenic that labels TL neurons that project to SM. We demonstrate that the axonal TL projection to SM in zebrafish is glutamatergic. Consistent with these axons synapsing directly onto PyrNs, SM-targeted dendrites of PyrNs contain punctate enrichments of the glutamatergic post-synaptic marker protein PSD95. Sparse genetic labeling of individual TL axons and PyrN dendrites enabled quantitative morphometric analysis that revealed (1) large, sparsely branched TL axons in SM and (2) small, densely innervated PyrN dendrites in SM. Together this unique combination of morphologies support a wiring diagram in which TL inputs to PyrNs exhibit a high degree of convergence. We propose that this convergence functions to generate large, compound visual receptive fields in PyrNs. This quantitative anatomical data will instruct future functional studies aimed at identifying the precise contribution of TL-PyrN circuitry to visual behavior.

Enhanced Contactless Vital Sign Estimation from Real-Time Multimodal 3D Image Data.

The contactless estimation of vital signs using conventional color cameras and ambient light can be affected by motion artifacts and changes in ambient light. On both these problems, a multimodal 3D imaging system with an irritation-free controlled illumination was developed in this work. In this system, real-time 3D imaging was combined with multispectral and thermal imaging. Based on 3D image data, an efficient method was developed for the compensation of head motions, and novel approaches based on the use of 3D regions of interest were proposed for the estimation of various vital signs from multispectral and thermal video data. The developed imaging system and algorithms were demonstrated with test subjects, delivering a proof-of-concept.

Minimising ambient illumination via ambient subtraction: smartphone assessment of jaundice in liver patients via sclera images

Using smartphone images to quantify color presents a noninvasive way to assess jaundice and other color-related biomarkers of the human body. Here we focus on assessing jaundice through accurate bilirubin measurement in adult liver patients, the first time optical imaging has been applied to this cohort. These patients can suffer from very high levels of bilirubin, indicating their severity of liver disease. A smartphone assessment technique for jaundice based around the color of the sclera (white of the eye) extracted from images is being developed, as smartphone imaging enables cheap, non-invasive and quantitative readings. Variations in ambient light cause large changes to recorded pixel values so must be accounted for to ensure that any changes detected are due to changes in jaundice level. Here we suggest the use of an ambient subtraction approach to minimise the effects of ambient light. Pairs of flash/ no-flash images are captured and the extracted values subtracted to yield data as though under a pure flash illumination. We present data demonstrating the technique with a group of healthy adult volunteers. We also present data from a patient study involving adults with liver disease. Images were captured and the bilirubin (jaundice) level predicted from these images before and after subtraction was compared to the ground truth value obtained via blood test. The linear correlation coefficient increased from 0.47 to 0.85 (p < 0.001 in both cases) upon application of subtraction, demonstrating the effectiveness of the technique.

An Improved Single Shot Multibox for Video-Rate Head Pose Prediction

Head pose estimation plays a crucial role in attention detection, behavior analysis, human-computer interaction, and eye tracking, etc. The existing landmark-to-pose methods require two steps, not only to detect the key points of the human face, but also to solve the 2D to 3D mapping problem usually by the average head model. We propose an efficient and robust method to monitor the driver's attention. Firstly, the prevailing object detection algorithm SSD which has inherent capabilities of simultaneous classify and regress, is used to create a lightweight network, which avoids the shortcomings of high coupling and time-consuming of the existing methods. Then, single-scale anchors, which have a less computational cost than multi-scale anchors, are adopted for vehicle environments where the ambient light changes dramatically. Finally, by binning continuous angles into specific classes, the 3D angle regression problem is converted into angle classification and face box regression, and our model directly outputs Euler angles (Yaw, Pitch, and Roll) without detecting face landmarks. Experiments on YawDD result that our approach can efficiently perform detection tasks and estimation tasks under the actual driving environment of various luminosity. The mean average errors of prediction in AFLW2000 and 300W-LP are 6.01° and 2.38°, which demonstrates the accuracy of the proposed algorithm.

Optical health analysis of visual comfort for bright screen display based on back propagation neural network

BackgroundThe visual comfort of liquid crystal display (LCD) is the subjective evaluation of the user. It is a multi-dimensional and multi-factor problem, which is affected by the luminous characteristics of the LCD screen, the physiological factors of the user, and some other environmental factors. MethodsBased on the theory of visual comfort under the guidance of ergonomics, this paper adopts a combination of objective measurement and subjective evaluation to obtain objective data such as blink frequency and pupil size changes, and subjective evaluation data on screen parameters. Correlation analysis was used to screen subjective and objective data, and an LCD visual comfort evaluation using the back propagation (BP) neural network was constructed with the aim of a concise evaluation of the LCD's own light-emitting characteristics, user's physiological factors, and environmental factors. ResultsAfter testing, the model can successfully predict the optimal visual level of the screen. After training, the relative error between the predicted value of visual comfort and the actual evaluation value is mostly within 10%. Based on this model, the display brightness and color temperature control system combined with the ambient light sensor can automatically adjust the brightness of the screen and the temperature of color parameters in correlation to user's gender, age, and ambient light changes to achieve the effect of improving visual comfort. Setting and user parameter adjustment provide a new method. The maximum adjustment error of the system after testing is 5.378%. ConclusionOur proposed technique can serve as a useful analysis platform for understanding and evaluating the visual comfort of the bright LCD screen at home or in the workplace, and enhancing optical health of humans.

Lunar synchronization of daily activity patterns in a crepuscular avian insectivore.

Biological rhythms of nearly all animals on earth are synchronized with natural light and are aligned to day‐and‐night transitions. Here, we test the hypothesis that the lunar cycle affects the nocturnal flight activity of European Nightjars (Caprimulgus europaeus). We describe daily activity patterns of individuals from three different countries across a wide geographic area, during two discrete periods in the annual cycle. Although the sample size for two of our study sites is small, the results are clear in that on average individual flight activity was strongly correlated with both local variation in day length and with the lunar cycle. We highlight the species’ sensitivity to changes in ambient light and its flexibility to respond to such changes in different parts of the world.

Can We See with Melanopsin?

A small fraction of mammalian retinal ganglion cells are directly photoreceptive thanks to their expression of the photopigment melanopsin. These intrinsically photosensitive retinal ganglion cells (ipRGCs) have well-established roles in a variety of reflex responses to changes in ambient light intensity, including circadian photoentrainment. In this article, we review the growing evidence, obtained primarily from laboratory mice and humans, that the ability to sense light via melanopsin is also an important component of perceptual and form vision. Melanopsin photoreception has low temporal resolution, making it fundamentally biased toward detecting changes in ambient light and coarse patterns rather than fine details. Nevertheless, melanopsin can indirectly impact high-acuity vision by driving aspects of light adaptation ranging from pupil constriction to changes in visual circuit performance. Melanopsin also contributes directly to perceptions of brightness, and recent data suggest that this influences the appearance not only of overall scene brightness, but also of low-frequency patterns.

Classification of soil aggregates: A novel approach based on deep learning

Having a powerful tool and the knowledge to classify soil aggregates, one of the most important factors in evaluating the performance of tillage implements, will result in quick and accurate classification of soil aggregates. By considering them as virtual sieve, a large part of the energy and workforce used in this sector can be reduced. In this regard, computational intelligence tools can play an important and optimal role in the evaluation of tillage quality and its real-time employment. The objective of the present study was to introduce a method known as deep learning to classify aggregates of any size in specific classes. Accordingly, stereo-pair images were used to provide multiple images simultaneously and the proper nutrition of the network. Since stereo-pair images are not dependent on changes in ambient light, imaging was done under conditions of the field with no lighting system. To train the deep models, the images of each lens were separated from each other and entered into the network. Without the extraction of the required features that is done manually in most image and vision-processing algorithms, the presented deep model began to learn to observe and could extract the required features from the lowest level to highly complex features automatically. Among the variety of neural network algorithms in deep learning, a convolutional neural network (CNN) was used in this study for its unique properties in working on images. To train the CNN, VggNet16, ResNet50, and Inception-v4 architects were used. Classification accuracy of these networks was above 95 %, but the highest accuracy achieved with ResNet50 (98.72 %). This accuracy, which was significantly different from previous studies, indicated the good performance of the deep learning method in the classification of aggregates. The results of the current study showed that the estimation of mean weight diameter (MWD) of aggregates without limitations in size and with great precision is completely practical and achievable.

Altered oscillation frequencies in the lateral geniculate complex in the rat model of absence epilepsy

Absence epilepsy (AE) is a neurological disease that manifests in spike-wave discharges not present in healthy neuronal circuits. Mutations in ion channels directly underlying this rhythmic discharge may additionally affect rhythms in multiple brain centres which disturbances contribute to the epileptic phenotype. Malfunctioning of the light detection system (from retina to subcortical visual structures), heavily dependent on oscillatory activities, could partially explain severe problems with sleep and arousal observed in epileptic patients. Therefore, the aim of our study was to evaluate characteristics of retinal-derived oscillations in the lateral geniculate complex of the thalamus; a major gateway for the light information flow for the brain. Extracellular recordings in vivo were performed on urethane-anaesthetised WAG/Rij and Wistar rats from single units in the identified parts of lateral geniculate complex to test their basic oscillatory features as well as reaction to transient and sustained changes in ambient light conditions. Here, we show altered rhythmic activity of the lateral geniculate neurons in the absence epilepsy model with the increase of both the infra-slow and fast oscillatory frequencies. Further, we describe their disturbed reaction to sustain change in ambient light and provide evidence for major changes in the intergeniculate leaflet neuronal firing; a part of the lateral geniculate complex implicated in the circadian timekeeping. Altogether, our results are the first to show a malfunctioning of light detection mechanisms in the absence epilepsy that may in turn underpin sleep-promoting system insufficiencies and other arousal disturbances contributing to epileptic phenotype.

Tracking Registration Algorithm for Augmented Reality Based on Template Tracking

Tracking registration is a key issue in augmented reality applications, particularly where there are no artificial identifier placed manually. In this paper, an efficient markerless tracking registration algorithm which combines the detector and the tracker is presented for the augmented reality system. We capture the target images in real scenes as template images, use the random ferns classi- fier for target detection and solve the problem of reinitialization after tracking registration failures due to changes in ambient lighting or occlusion of targets. Once the target has been successfully detected, the pyramid Lucas-Kanade (LK) optical flow tracker is used to track the detected target in real time to solve the problem of slow speed. The least median of squares (LMedS) method is used to adaptively calculate the homography matrix, and then the three-dimensional pose is estimated and the virtual object is rendered and registered. Experimental results demonstrate that the algorithm is more accurate, faster and more robust.

High innate preference of black substrate in the chive gnat, Bradysia odoriphaga (Diptera: Sciaridae).

The chive gnat, Bradysia odoriphaga, is a notorious pest of Allium species in China. Colour trapping is an established method for monitoring and control of Bradysia species. In order to clarify the effect of colour preference of B. odoriphaga for the perched substrate, multiple-choice tests were used to assess the response of the chive gnat to different colour hues and brightness levels under different intensities of white illumination and two spectrally different illuminations. Given the choice among four colours differing in hue under different intensities of white illumination and two spectrally different illuminations, chive gnat adults significant preferred the black substrate, a lesser preference to brown and green substrates, and the least preference to orange substrate irrespective of illumination. Given the choice among four levels of brightness under the same illumination conditions as those in the previous experiment (different intensities of white illumination and two spectrally different illuminations), chive gnats preferred black substrate over dark grey, light grey and white substrates. Meanwhile, both virgin and mated adults significantly preferred black over other colour hues and brightness. Based on our results, we conclude that the chive gnat adults significantly prefer black substrates irrespective of colour hues and brightness. This behaviour does not alter with ambient light condition changes. No difference observed between choices of female and male adults. Our results provide new insight for understanding the colour choice behaviour in chive gnat and pave a way to improve monitoring and control of chive gnats and management.

Parallel visual circuitry in a basal chordate.

A common CNS architecture is observed in all chordates, from vertebrates to basal chordates like the ascidian Ciona. Ciona stands apart among chordates in having a complete larval connectome. Starting with visuomotor circuits predicted by the Ciona connectome, we used expression maps of neurotransmitter use with behavioral assays to identify two parallel visuomotor circuits that are responsive to different components of visual stimuli. The first circuit is characterized by glutamatergic photoreceptors and responds to the direction of light. These photoreceptors project to cholinergic motor neurons, via two tiers of cholinergic interneurons. The second circuit responds to changes in ambient light and mediates an escape response. This circuit uses GABAergic photoreceptors which project to GABAergic interneurons, and then to cholinergic interneurons. Our observations on the behavior of larvae either treated with a GABA receptor antagonist or carrying a mutation that eliminates photoreceptors indicate the second circuit is disinhibitory.

Restoration of high-sensitivity and adapting vision with a cone opsin

Inherited and age-related retinal degenerative diseases cause progressive loss of rod and cone photoreceptors, leading to blindness, but spare downstream retinal neurons, which can be targeted for optogenetic therapy. However, optogenetic approaches have been limited by either low light sensitivity or slow kinetics, and lack adaptation to changes in ambient light, and not been shown to restore object vision. We find that the vertebrate medium wavelength cone opsin (MW-opsin) overcomes these limitations and supports vision in dim light. MW-opsin enables an otherwise blind retinitis pigmenotosa mouse to discriminate temporal and spatial light patterns displayed on a standard LCD computer tablet, displays adaption to changes in ambient light, and restores open-field novel object exploration under incidental room light. By contrast, rhodopsin, which is similar in sensitivity but slower in light response and has greater rundown, fails these tests. Thus, MW-opsin provides the speed, sensitivity and adaptation needed to restore patterned vision.

A Human Tracking and Physiological Monitoring FSK Technology for Single Senior at Home Care.

Respiration monitoring for apnea diagnosis and movement tracking for physical activity analysis are essential and valuable indicators of underlying medical conditions in senior health care. Compared with other health sensing technologies, radar has shown its advantages in terms of non-contact implementation, immunity to ambient light and temperature changes, hardware simplicity, and long range coverage. Among various radar types, frequency-shift keying (FSK) radar inherits the ability to measure respiration from Doppler radar, while also having the ability to track the absolute range of a moving target with much less bandwidth requirement than frequency modulated continuous wave (FMCW) radar and ultra-wideband (UWB) radar. Therefore, in this paper, an FSK radar system is employed for both remote respiration monitoring and absolute range tracking. Fundamental theory and operating principle of FSK system are detailed. Respiration monitoring and range tracking experiments of a human subject were performed in an indoor environment. Results are presented to demonstrate the practical feasibility of the FSK sensing technology being applied to single senior home care applications.