Illumination Variations Research Articles

A change detection algorithm for the SAR images based on DWT and DE optimization

ABSTRACT In this paper, a novel change detection algorithm is proposed for the remote sensing Synthetic Aperture Radar (SAR) images. Change detection in SAR images is one of the critical tasks in the field of remote sensing as the images contain significant speckle noise and illumination variations. In order to address these issues, a novel change detection algorithm is proposed using Discrete Wavelet Transform (DTW) and Differential Evaluation (DE). At first, the DWT is utilized to remove the speckle noise from the images. Then, a DE optimization-based Support Vector Machine (SVM) algorithm is proposed to detect the changes in SAR images. The proposed method can give comparatively better correct classification values, and lesser false alarm as well as total error values than the recently developed change detection methods.

Illusory light drives pupil responses in primates.

In humans, the eye pupils respond to both physical light sensed by the retina and mental representations of light produced by the brain. Notably, our pupils constrict when a visual stimulus is illusorily perceived brighter, even if retinal illumination is constant. However, it remains unclear whether such perceptual penetrability of pupil responses is an epiphenomenon unique to humans or whether it represents an adaptive mechanism shared with other animals to anticipate variations in retinal illumination between successive eye fixations. To address this issue, we measured the pupil responses of both humans and macaque monkeys exposed to three chromatic versions (cyan, magenta, and yellow) of the Asahi brightness illusion. We found that the stimuli illusorily perceived brighter or darker trigger differential pupil responses that are very similar in macaques and human participants. Additionally, we show that this phenomenon exhibits an analogous cyan bias in both primate species. Beyond evincing the macaque monkey as a relevant model to study the perceptual penetrability of pupil responses, our results suggest that this phenomenon is tuned to ecological conditions because the exposure to a "bright cyan-bluish sky" may be associated with increased risks of dazzle and retinal damages.

Application of Digital Tools and Artificial Intelligence to the Myasthenia Gravis Core Examination.

Advances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation through objective digital evaluation. We assessed ability to quantitate Zoom video recordings of a standardized neurological examination the myasthenia gravis core examination (MG-CE), which had been designed for telemedicine evaluations. We used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. Computer vision in combination with artificial intelligence methods were used to build algorithms to analyze videos with a focus on eye or body motions. For the assessment of examinations involving vocalization, signal processing methods were developed, including natural language processing. A series of algorithms were built that could automatically compute the metrics of the MG-CE. Fifty-one patients with MG with videos recorded twice on separate days and 15 control subjects were assessed once. We were successful in quantitating lid, eye, and arm positions and as well as well as develop respiratory metrics using breath counts. Cheek puff exercise was found to be of limited value for quantitation. Technical limitations included variations in illumination, bandwidth, and recording being done on the examiner side, not the patient. Several aspects of the MG-CE can be quantitated to produce continuous measures via standard Zoom video recordings. Further development of the technology offer the ability for trained, non-physician, health care providers to perform precise examination of patients with MG outside the clinic, including for clinical trials. Advances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation. Here, we asked whether video recordings of the typical telemedicine examination for the patient with myasthenia gravis be used to quantitate examination findings? Despite recordings not made for purpose, we were able to develop and apply computer vision and artificial intelligence to Zoom recorded videos to successfully quantitate eye muscle, facial muscle, and limb fatigue. The analysis also pointed out limitations of human assessments of bulbar and respiratory assessments. The neuromuscular examination can be enhanced by advance technologies, which have the promise to improve clinical trial outcome measures as well as standard care.

Universal Facial Encoding of Codec Avatars from VR Headsets

Faithful real-time facial animation is essential for avatar-mediated telepresence in Virtual Reality (VR). To emulate authentic communication, avatar animation needs to be efficient and accurate: able to capture both extreme and subtle expressions within a few milliseconds to sustain the rhythm of natural conversations. The oblique and incomplete views of the face, variability in the donning of headsets, and illumination variation due to the environment are some of the unique challenges in generalization to unseen faces. In this paper, we present a method that can animate a photorealistic avatar in realtime from head-mounted cameras (HMCs) on a consumer VR headset. We present a self-supervised learning approach, based on a cross-view reconstruction objective, that enables generalization to unseen users. We present a lightweight expression calibration mechanism that increases accuracy with minimal additional cost to run-time efficiency. We present an improved parameterization for precise ground-truth generation that provides robustness to environmental variation. The resulting system produces accurate facial animation for unseen users wearing VR headsets in realtime. We compare our approach to prior face-encoding methods demonstrating significant improvements in both quantitative metrics and qualitative results.

Accurate Vision-Based PCB Positioning Using Cosine-Convolutional Neural Networks

Object positioning is a fundamental task for assembly and inspection operations in the electronic industry. Traditional methods such as template matching and feature-point detection have been applied for object positioning. They are however computationally expensive and are generally affected by environmental changes. Deep learning models based on Convolutional Neural Network (CNN) have also been used for object positioning. They are computationally very efficient but can be sensible under environmental variations such as illumination and noise. In this article, a Cosine-Convolution operation is proposed with the objective of minimizing the effects of illumination variations. The proposed Cosine-Convolution can substitute any convolutional operation in a CNN-based regressor for positioning tasks. The proposed convolution is based on cosine-measure that normalizes the convolution between an image window and a filter at local scope. The proposed Cosine-Convolutional Neural Network (Cosine-CNN) performs more accurate than the traditional CNN under environment variations, including illumination changes, noise, defocusing and template occlusion. The experimental results reveal that the proposed model can reach mean prediction errors smaller than 1 pixel and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^\circ$</tex-math> </inline-formula> for object shift and rotation, regardless of the environmental variations. The method is computationally very fast, with an average evaluation time of 1.2-ms for real-time PCB positioning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The training of deep learning models based on Convolutional Neural Network (CNN) can be tedious. Image samples with different illumination settings must be provided to have an efficient and robust model, which may not be easy to acquire or synthesize. This study proposes a solution to the challenge of illumination variations in images for the object positioning task in Printed Circuit Boards (PCB). A new Convolutional operation called Cosine-Convolutional that normalizes the traditional Convolution is introduced to overcome the illumination variations in the input images. Experimental results demonstrate that the proposed Cosine-Convolutional operation is less sensitive against lightning variations with respect to the training images.

Evaluating Learning-based Tie Point Matching for Geometric Processing of Off-Track Satellite Stereo

Abstract. Tie-point matching of off-track stereo images is a very challenging task, which can impact bias compensation and digital surface model (DSM) generation. Compared to in-track stereo images, off-track stereo images are more complex primarily due to the radiometric differences caused by sun illumination, sensor responses, atmospheric conditions, and seasonal land cover variations, and secondly due to the longer baseline and larger intersection angle. These challenges significantly limit the use of the vast number of images in satellite archives for automated geometric processing and mapping. Recent advances in deep learning (DL) based matching show promising results against images with diverse illuminations, viewing angles and scales through learning examples. This paper evaluates the potentials of addressing the tie point matching problems in off-track satellite stereo images. Specifically, we focus on stereo pairs that failed or underperformed in classic matching algorithms (i.e., SIFT (scale invariant feature transform)), and evaluate the DL-based tie points matchers by its resulting geometric accuracy in relative orientation, and the generated DSM. The experiments are carried out using 40 off-track satellite stereo pairs from four different regions around the world. We conclude that DL-based methods provide a significant higher success rate in matching challenging multi-temporal stereo pairs, even if their matching accuracy is slightly lower than classic algorithms.

Spatial-temporal graph Transformer for object tracking against noise spoofing interference

Achieving accurate object tracking against noise spoofing interference caused by similar backgrounds, similar objects, occlusion, or illumination variations is a challenge, especially since Siamese trackers largely ignore the spatial-temporal and part-level information of object structure. To handle this problem, we present a novel object tracking network with spatial-temporal awareness, which learns the correlation of local feature changes and extracts global spatial-temporal fusion features of the object to prevent noise spoofing interference. Specifically, our tracker integrates an object graph reconstruction representation module and a spatial-temporal graph Transformer module. The graph reconstruction representation module models the object structure and search region as part-to-part graph node correspondences, propagating object information to achieve part-level feature aggregation in the search region. The proposed spatial-temporal graph Transformer module fuses the temporal and spatial features of the object to enhance the contextual features and correlation between temporal and spatial feature variations, recognizing the noise spoofing interference to enhance the tracking performance. Meanwhile, our tracker learns object motion information to improve object state awareness, enhancing tracking accuracy under similar objects interference. Extensive experiments on six public datasets validate that our tracker outperforms related state-of-the-art trackers and achieves accurate tracking results against noise spoofing interference.

Computer vision-based displacement measurement using spatio-temporal context and optical flow considering illumination variation

Computer vision-based displacement measurement using spatio-temporal context and optical flow considering illumination variation

The effect of illumination on the visibility of steps and ramps for people with low vision.

Poor visibility of indoor features such as steps and ramps can pose mobility hazards for people with low vision. For purposes of architectural design, it is important to understand how design parameters such as the illumination level of an indoor space affect the visibility of steps and ramps. This study was aimed to examine the effect of typical variation in photopic illumination level in an indoor space on the visibility of steps and ramps for individuals with low vision. Steps and ramps were constructed in a large windowless room illuminated by overhead lights. Subjects with low vision completed a 5-alternative forced choice task to recognize the targets at three levels of photopic illumination, i.e., 800, 80, and 8 lux, and gave confidence ratings about their judgments on a 5-point scale. Acuities and contrast sensitivities of the subjects were also measured at each illumination level. For comparison, a group of normally sighted subjects with simulated acuity reduction also completed the step-and-ramp recognition task. For both groups of subjects, recognition accuracy was not affected by illumination level. For subjects with low vision, however, there was a significant effect of illumination level on confidence rating: subjects became more confident about their judgments with increasing illumination. There was also a weak effect of illumination level on acuity and contrast sensitivity, both worsening with decreasing illumination. Recognition performance was best predicted by contrast sensitivity, whereas confidence was best predicted by visual acuity. Illumination variation over a typical photopic range in an indoor space had minimal effect on the objective visibility of steps and ramps for people with low vision. However, illumination level affected subjects' confidence in hazard recognition. Design decisions on parameters such as illumination should consider the consequences on both the objective and the subjective accessibility of a space.

Is human perception reliable? Toward illumination robust food freshness prediction from food appearance — Taking lettuce freshness evaluation as an example

Machine vision-based food quality evaluation systems have achieved great attention in industry and academia in recent years because of their high-throughput and non-invasive properties. In practice, the environmental illumination conditions variations would cause visual evaluation bias for both human and machine perceptions. Compared to other existing studies which take sample images and human visual grading under fixed illumination conditions, this study first investigated the environmental illumination effects on the performance of visual-based food grading for both humans and machines. Taking lettuce samples as an example, an image dataset that considered the environmental illumination variations was first established. This dataset encompasses human visual grading scores obtained from sensory panels as well. In contrast to current studies that utilize a single grader or L*a*b* color features to assess sample freshness, our study reveals a substantial impact of environmental illumination on both human perception (p<0.0001) and L*a*b* color features (p<0.0001). In order to enhance the performance of machine vision-based freshness prediction, multitask learning protocols were incorporated into the proposed new network architectures. This allowed the simultaneous prediction of both sample freshness and illumination conditions. In comparison to commonly used generic convolutional neural network models and vision transformer models, the newly proposed model exhibited superior freshness prediction performance. It minimized the prediction error by 20.36%, outperforming the generic ResNet model. This research represents the first quantitative study addressing human and machine perceptions under varied illumination conditions for food quality evaluation. The findings are anticipated to play a pivotal role in expediting the integration of machine vision applications into food engineering practices.

A visual area detection algorithm based on mirror splicing for motorcycle indirect vision test

Abstract Motorcycle indirect vision devices are important safety components, and the visual area is required to meet industry standards. There is a need for effective algorithms to detect and analyze the relevant visual areas within these mirrors to enhance motorcycle safety. However, it is a challenge to test rearview mirror vision in unstructured environments given variations in illumination, occlusion, and object scale. We propose a visual area detection algorithm based on mirror stitching for a motorcycle indirect vision test. First, an edge-based VGG16-Unet (EBV16-Unet) network is employed to extract binocular mirror information and eliminate the complex background. Second, gradient-based topology-preserving image stitching and multi-band hybrid Laplacian pyramid-based image blending algorithms are utilized to complete binocular mirror information acquisition. Finally, a sequential detection method for adaptive marker color and shape features is used to establish the visual area. The EBV16-Unet algorithm achieved an accuracy of 98.63% for precision, 98.71% for recall, 98.58% for F1, and 98.37% for mean intersection-over-union (MIOU), surpassing the comparative models of PSPNet, DeepLab v3+, and HRNet and exhibited superior generalization ability. The binocular vision splicing effect experiment revealed a horizontal splicing error of 0.114322 ± 0.0674 and vertical splicing error of 0.124287 ± 0.063302, calculated using a standard checkerboard. The rearview mirror vision test operation experiment results confirm that the Motorcycle Indirect Vision Test System (MIVTS) offers convenience, simplicity and high accuracy. MIVTS successfully accomplishes the unstructured motorcycle rearview mirror vision test, thereby establishing an advanced theoretical foundation for computer vision-based automated vehicle inspection.

Cross-centroid ripple pattern for facial expression recognition

AbstractIn this paper, we propose a new feature descriptor Cross-Centroid Ripple Pattern (CRIP) for facial expression recognition. CRIP encodes the transitional pattern of a facial expression by incorporating a cross-centroid relationship between two ripples located at radius r1 and r2 respectively. These ripples are generated by dividing the local neighborhood region into subregions. Thus, CRIP has the ability to preserve macro and microstructural variations in an extensive region, which enables it to deal with side views and spontaneous expressions. Furthermore, gradient information between cross centroid ripples provides strength to capture prominent edge features in active patches: eyes, nose, and mouth, that define the disparities between different facial expressions. Cross-centroid information also provides robustness to irregular illumination. Moreover, CRIP utilizes the averaging behavior of pixels at subregions that yields robustness to deal with noisy conditions. The performance of the proposed descriptor is evaluated on seven comprehensive expression datasets consisting of challenging conditions such as age, pose, ethnicity, and illumination variations. The experimental results show that our descriptor consistently achieved a better accuracy rate as compared to existing state-of-the-art approaches.

Convolutional MLP orthogonal fusion of multiscale features for visual place recognition

Visual place recognition (VPR) involves obtaining robust image descriptors to cope with differences in camera viewpoints and drastic external environment changes. Utilizing multiscale features improves the robustness of image descriptors; however, existing methods neither exploit the multiscale features generated during feature extraction nor consider the feature redundancy problem when fusing multiscale information when image descriptors are enhanced. We propose a novel encoding strategy—convolutional multilayer perceptron orthogonal fusion of multiscale features (ConvMLP-OFMS)—for VPR. A ConvMLP is used to obtain robust and generalized global image descriptors and the multiscale features generated during feature extraction are used to enhance the global descriptors to cope with changes in the environment and viewpoints. Additionally, an attention mechanism is used to eliminate noise and redundant information. Compared to traditional methods that use tensor splicing for feature fusion, we introduced matrix orthogonal decomposition to eliminate redundant information. Experiments demonstrated that the proposed architecture outperformed NetVLAD, CosPlace, ConvAP, and other methods. On the Pittsburgh and MSLS datasets, which contained significant viewpoint and illumination variations, our method achieved 92.5% and 86.5% Recall@1, respectively. We also achieved good performances—80.6% and 43.2%—on the SPED and NordLand datasets, respectively, which have more extreme illumination and appearance variations.

A Novel Deep Learning Method for Detecting Strawberry Fruit

The recognition and localization of strawberries are crucial for automated harvesting and yield prediction. This article proposes a novel RTF-YOLO (RepVgg-Triplet-FocalLoss-YOLO) network model for real-time strawberry detection. First, an efficient convolution module based on structural reparameterization is proposed. This module was integrated into the backbone and neck networks to improve the detection speed. Then, the triplet attention mechanism was embedded into the last two detection heads to enhance the network’s feature extraction for strawberries and improve the detection accuracy. Lastly, the focal loss function was utilized to enhance the model’s recognition capability for challenging strawberry targets, which thereby improves the model’s recall rate. The experimental results demonstrated that the RTF-YOLO model achieved a detection speed of 145 FPS (frames per second), a precision of 91.92%, a recall rate of 81.43%, and an mAP (mean average precision) of 90.24% on the test dataset. Relative to the baseline of YOLOv5s, it showed improvements of 19%, 2.3%, 4.2%, and 3.6%, respectively. The RTF-YOLO model performed better than other mainstream models and addressed the problems of false positives and false negatives in strawberry detection caused by variations in illumination and occlusion. Furthermore, it significantly enhanced the speed of detection. The proposed model can offer technical assistance for strawberry yield estimation and automated harvesting.

A 3D Reconstruction Method Based on Homogeneous De Bruijn-Encoded Structured Light

Structured light three-dimensional reconstruction is one of the important methods for non-contact acquisition of sparse texture object surfaces. Variations in ambient illumination and disparities in object surface reflectance can significantly impact the fidelity of three-dimensional reconstruction, introducing considerable inaccuracies. We introduce a robust method for color speckle structured light encoding, which is based on a variant of the De Bruijn sequence, termed the Homogeneous De Bruijn Sequence. This innovative approach enhances the reliability and accuracy of structured light techniques for three-dimensional reconstruction by utilizing the distinctive characteristics of Homogeneous De Bruijn Sequences. Through a pruning process applied to the De Bruijn sequence, a structured light pattern with seven distinct color patches is generated. This approach ensures a more equitable distribution of speckle information.

Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case

This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements.

A survey on disguise face recognition

ABSTRACT Human face is one of the physiological traits used in biometric system and gained a significant attention in recent years due to the advancement in technology. It has been remarkable progress in face recognition system since 1960 owing to advances in 3D-face modeling and analysis, face detection, and tracking. The reason for this progress is to provide security in surveillance systems, criminal identification, border control, security access, and many more. Despite this, proposed techniques are not robust enough for many challenging issues like variations in pose, illumination, expression, and disguise. This paper provides a survey on some of the well-known deep learning methodologies focused on these issues of face recognition. Furthermore, the performance of these methodologies is also studied in terms of accuracy, computational cost, and robustness. In addition to this, it also summarizes and outlines the importance of disguise face datasets. Finally, open challenges involved in deep learning framework, disguise face dataset, and the impact of disguise co-variate on the current face recognition system are discussed for future research.

What drives the variability in AGN? Explaining the UV-Xray disconnect through propagating fluctuations

ABSTRACT Intensive broad-band reverberation mapping campaigns have shown that AGN variability is significantly more complex than expected from disc reverberation of the variable X-ray illumination. The UV/optical variability is highly correlated and lagged, with longer lags at longer wavelengths as predicted, but the observed time-scales are longer than expected. Worse, the UV/optical light curves are not well correlated with the X-rays, which should drive them. Instead, we consider an intrinsically variable accretion disc, where slow mass accretion rate fluctuations are generated in the optical-UV disc, propagating down to modulate intrinsically faster X-ray variability from the central regions. We match our model to Fairall 9, a well-studied AGN with L ∼ 0.1LEdd, where the spectrum is dominated by the UV/EUV. Our model produces light curves where the X-rays and UV have very different fast variability, yet are well correlated on longer time-scales, as observed. It predicts that the intrinsic variability has optical/UV leading the X-rays, but including reverberation of the variable EUV from an inner wind produces a lagged bound-free continuum that matches the observed UV-optical lags. We conclude that optical/UV AGN variability is likely driven by intrinsic fluctuations within the disc, not X-ray reprocessing: the observed longer than expected lags are produced by reverberation of the EUV illuminating a wind, not by X-ray illumination of the disc: the increasing lag with increasing wavelength is produced by the increased contribution of the (constant lag) bound-free continuum to the spectrum, rather than indicating intrinsically larger reverberation distances for longer wavelengths.

An illumination-robust edge detection method for nuclear fuel assembly deformation measurement

Fuel assemblies (FAs) are critical components of nuclear reactor cores. The excessive deformation of FAs can pose a serious risk of safety accidents in nuclear power plants. Hence, regular monitoring of the deformation of the FAs is necessary. To accomplish this task, the most critical challenge lies in accurately extracting the edges of FAs in the nuclear underwater environment with spatial variations in illumination. In this paper, a novel illumination-robust edge detection method is proposed. Firstly, tilt correction is applied to arrange the fuel rods vertically in the image. Then, the center of each fuel rod is located through grayscale projection and filtered by rod center spacing variance minimization. Subsequently, adaptive thresholding is employed to obtain a binary image. Finally, the edges are identified by searching in the vicinity of rod centers in the binary image. The effectiveness and robustness of the proposed method have been validated in experiments, and the approach has been successfully applied to the FA deformation measurement in nuclear power plants.

Face Sketch Creation and Recognition

Abstract: This paper presents a wisdom mapping approach to dissect the thematic elaboration of face recognition exploration. For this reason, different bibliometric tools are combined (Performance analysis, wisdom mapping and Coword analysis) to identify the most important, productive and loftiest- -impact sub fields. also, different visualization tools are used to display a graphical vision of the face recognition field to determine the thematic disciplines and their evolutionary geste. . Eventually, this study proposes the most applicable lines of exploration for the face recognition field. Findings indicate a huge increase in face recognition exploration since 2014. Mixed approaches revealed a great interest compared to original and global approaches. In terms of algorithms, the use of deep literacy styles is the new trend. On the other hand, the illumination variation impact on face recognition algorithms' performances is currently, the most important and impacting challenge for the face recognition field.