Non-uniform Lighting Research Articles

A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea.

Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps.

A STUDY ON QUANTIFICATION OF “THE CENTER OF GRAVITY OF LIGHT” IN NON-UNIFORM LIGHTING ENVIRONMENT

In this paper, in order to measure non-uniform lighting environments, basic studies are conducted on whether “the center of gravity of light” used by lighting designers can be estimated from the luminance distribution. Evaluation experiments were conducted to answer the position where examinees felt the center of gravity of light in 19 patterns of non-uniform lighting environments. As a result, it is shown that it is possible to estimate the center of gravity of light that people perceive by calculating the luminance distribution in the guided field of view with reference to the calculation method of the center of gravity.

Elimination of Leaf Angle Impacts on Plant Reflectance Spectra Using Fusion of Hyperspectral Images and 3D Point Clouds

During recent years, hyperspectral imaging technologies have been widely applied in agriculture to evaluate complex plant physiological traits such as leaf moisture content, nutrient level, and disease stress. A critical component of this technique is white referencing used to remove the effect of non-uniform lighting intensity in different wavelengths on raw hyperspectral images. However, a flat white tile cannot accurately reflect the lighting intensity variance on plant leaves, since the leaf geometry (e.g., tilt angles) and its interaction with the illumination severely impact plant reflectance spectra and vegetation indices such as the normalized difference vegetation index (NDVI). In this research, the impacts of leaf angles on plant reflectance spectra were summarized, and an improved image calibration model using the fusion of leaf hyperspectral images and 3D point clouds was built. Corn and soybean leaf samples were imaged at different tilt angles and orientations using an indoor desktop hyperspectral imaging system and analyzed for differences in the NDVI values. The results showed that the leaf's NDVI largely changed with angles. The changing trends with angles differed between the two species. Using measurements of leaf tilt angle and orientation obtained from the 3D point cloud data taken simultaneously with the hyperspectral images, a support vector regression (SVR) model was successfully developed to calibrate the NDVI values of pixels at different angles on a leaf to a same standard as if the leaf was laid flat on a horizontal surface. The R-squared values between the measured and predicted leaf angle impacts were 0.76 and 0.94 for corn and soybean, respectively. This method has a potential to be used in any general plant imaging systems to improve the phenotyping quality.

Accounting for Non-Uniform Ambient Lighting in Ray Traced Ambient Occlusion

Ambient occlusion is a popular method to enhance the visuals of real-time applications. The generally accepted equation assumes that incoming ambient lighting is uniform in the enclosing hemisphere around the surface point. This assumption does not always hold and can cause artifacts. With screen-space techniques the artifact may not be present, because of the lack of information, but it becomes visible with more accurate methods. This is especially true for ray tracing based implementations which are widely used to create the ground truth image to measure other techniques. With recent advancements of the graphics hardware, they also found their way into real-time applications where these shortcomings were neither discussed nor addressed. In this paper we analyze the problem and demonstrate its presence in popular game engines. We also propose a ray tracing based extension to eliminate these artifact in a physically plausible way.

MIXED REALITY CONTENT ALIGNMENT IN MONUMENTAL ENVIRONMENTS

Abstract. Mixed reality provides on-the-spot and real-time data access capabilities by making virtual models and information more intuitive and accessible. Furthermore, allowing the operator to perceive 3D as holograms would allow for a more natural and straightforward manipulation of the perceived 3D content by permitting the augmentation of real objects with various levels of data. This can be accomplished by appropriately registering and superimposing the presented 3D models with the surrounding environment. This work aims to provide a quantitative evaluation of HoloLens 2 capabilities in registering virtual content inside monumental spaces. Two different methodologies are evaluated: Vuforia image targets and Microsoft World Locking Tools (WLTs). Tests have been performed inside Milan Cathedral's monumental spaces. Here, ambience dimensions, single architectural element repetition and non-uniform lighting conditions push out-of-the-box methods to their limits. Results show that WLTs with their space pins API can correctly reference virtual content keeping deviations in the order of 15 cm coping with the scale error produced from sensors’ drifts.

Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey

Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research.

Trainable watershed-based model for cornea endothelial cell segmentation

AbstractSegmentation of the medical image plays a significant role when it comes to diagnosis using computer aided system. This article focuses on the human corneal endothelium’s health, which is one of the filed research interests, especially in the human cornea. Various pathological environments fasten the extermination of the endothelial cells, which in turn decreases the cell density in an abnormal manner. Dead cells worsen the hexagonal design. The mutilated endothelial cells can no longer revive back and that gives room for neighbouring cells to migrate and expand so that they can fill in the space. The latter results in cell elongation that is unpredictable as well as increase in size and thinning. Cell density and shape are therefore considered major parameters when it comes to explaining the health condition attributed to corneal endothelium. In this study, medical feature extraction was obtained depending on the segmentation of the endothelial cell boundary, and the task of segmentation of such objects especially the thin, transparent, and unclear cell boundary is considered challenging due to the nature of the image capture during endothelium layer examination by ophthalmologists using confocal or specular microscopy. The resulting image suffers from various issues that affect the quality of the image. Low quality is due to non-uniformity of illumination and the presence of a lot of noise and artefacts resulting from high amounts of distortion, and most of these limitations are present because of the nature of the imaging modality. Usually, images contain certain kind of noise and also continuous shadow. Furthermore, the cells are separated by poor border, thereby leading to great difficulty in the segmentation of the images. The irregular shape of cell and also the contrast of such images seem to be low as they possess blurry boundaries with diverse objects existing in addition to the lack of homogeneity. The main aim of the study is to propose and develop a totally automatic, robust, and real-time model for the segmentation of endothelial cells of the human cornea obtained byin vivomicroscopy and computation of different clinical features of endothelial cells. To achieve the aim of this study a new scheme of image enhancement was proposed such as the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) technique to enhance contrast. After that, a new image denoising technique called Wavelet Transform Filter and Butterworth Bandpass for Segmentation is used. Subsequently, brightness level correction is applied by using the moving average filter and the CLAHE to reduce the effects of the non-uniform image lighting produced as a result of the previous step. The main aim of this article is the segmentation of endothelial cells, which involves precise detection of the endothelial contours. So a new segmentation model was proposed such that the shape of the cells will be extracted, and the contours were highlighted. This stage is followed by clinical feature extraction and uses the features for diagnosis. In this stage, several relevant clinical features such as pleomorphism mean cell perimeter, mean cell density, mean cell area, and polymegathism are extracted. The role of these clinical features is crucial for the early detection of corneal pathologies as well as the evaluation of the health of the corneal endothelium layer. The findings of this study were promising.

A new scheme of polar Fast Fourier Transform Code for iris recognition through symbolic modelling approach

The rich random structure of the iris, non-invasiveness and temporal stability properties makes it one of the most powerful biometric trait for person identification. Iris recognition techniques have demonstrated very low false acceptance rates and very high matching efficiency in large iris datasets. However, iris recognition systems face several challenges including non-uniform lighting conditions, occlusion of the eye by the eyelid, variation of head pose (causes dimensional inconsistencies of iris part), the reflection of surrounding objects and many others. The paper has addressed some of the reported issues of iris recognition technique by exploring new feature extraction technique and devising a new symbolic modelling approach for feature representation and classification, along with other classification techniques namely k-NN, SVM and PNN. The symbolic approach employed in the proposed research to represent iris feature reduces the dimensionality of feature space and also reduces the actual time required for iris recognition. The symbolic approach with new feature extraction technique has attained recognition accuracy of 100%, 98.26%, 99.25% and 96.67% on CASIA 1.1, CASIA 4.0 Interval, SGGSIE&T and the newly formed VISA Iris datasets respectively. Additionally, authors deployed previous algorithms and their findings of the results are discussed in comparison with proposed techniques.

An Investigation of the Implications of Visual Impairment for Illumination Requirements

Introduction The present contribution mainly focuses on the evaluation of the visual performance of people with impaired vision and, for comparison purposes, individuals with typical vision under different lighting conditions. Methods: A monitor with adjustable brightness facilitated various test runs to determine the visual performance as a function of the adaptation luminance and glare. In addition, the subjective impressions of the participants with impaired vision were queried via interviews. The study included 98 people with impaired vision and 38 people without visual impairments. Results: The interview results suggest that most people with visual impairments require special lighting conditions. An increased lighting requirement is observed amongst 50% of this group. Moreover, 75% of this group display increased glare sensitivity. Likewise, adaptation problems and critical issues related to nonuniform lighting are manifest. Individuals with impaired vision included in this study display a greatly reduced contrast threshold and a higher subjective level of discomfort compared to individuals with typical vision.Discussion: Most visually impaired participants state that they require a higher degree of brightness to achieve their maximum personal visual performance. However, about a quarter of the participants reported that they achieve a better visual performance at lower brightness, while displaying an increased sensitivity to glare. In general, glare has a decisive influence on the visual performance of the participants with impaired vision in our study. Implications for Practitioners: The study reconfirms the essential importance of glare-free and uniform general lighting strategies (e.g., via predominantly indirect lighting) as well as avoiding abrupt spatiotemporal luminance changes (e.g., via provision of a transition zone between locations with very different luminance levels).

A Scale and Rotational Invariant Key-point Detector based on Sparse Coding

Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this article, we propose a solution to this challenging problem by incorporating Scale and Rotation Invariant design (named SRI-SCK) into a recently developed Sparse Coding based Key-point detector (SCK). The SCK detector is flexible in different scenarios and fully invariant to affine intensity change, yet it is not designed to handle images with drastic scale and rotation changes. In SRI-SCK, the scale invariance is implemented with an image pyramid technique, while the rotation invariance is realized by combining multiple rotated versions of the dictionary used in the sparse coding step of SCK. Techniques for calculation of key-points’ characteristic scales and their sub-pixel accuracy positions are also proposed. Experimental results on three public datasets demonstrate that significantly high repeatability and matching score are achieved.

Adaptive Window Selection for Non-uniform Lighting Image Thresholding

Selection of appropriate size of windows or subimages is the most important step for thresholding images with non-uniform lighting. In this paper, a novel criteria function is developed to partition images into different size of sub images appropriate for thresholding. After the partitioning, each subimage is segmented by Otsu’s thresholding approaches. The performance of the proposed method is validated on benchmark test images with different degree of uneven lighting. Based on the qualitative and quantitative measures, the proposed method is fully automatic, fast and efficient in comparison to many landmark approaches.

Automated Object Detection for Visual Inspection of Nuclear Reactor Cores

Remote visual inspection is a common approach to understanding the health of key components and substructures within nuclear power plants, particularly in difficult to access and high dosage areas. Interpretation of inspection footage is a manually intensive procedure and challenges arise in localizing and dimensioning defects directly from a video feed, which may be subject to uncertainty from a range of sources such as lens distortion, nonuniform lighting, and lack of depth from a monocular camera system. A common approach to addressing these issues is to develop a scaling factor based on identifying a reference object of known dimensions in the image and using this to size regions of interest. Manual, accurate identification of these reference objects is onerous, time consuming, and prone to variation across different human experts, therefore, robust identification of suitable reference objects in an automated, reliable, and repeatable manner is of significant value. In this paper we evaluate two approaches for the automated detection of reference objects in the inspection of graphite cores in the United Kingdom’s fleet of advanced gas-cooled reactors (AGRs). The first method is a multistep approach using tools from mathematical morphology. The approach uses a genetic algorithm to “grow” suitable structuring elements, refine the order of operations, and remove operations proposed by the human designer that have a negative impact on performance. The second approach uses semantic segmentation, a technique which is normally applied to scene labeling in computer vision applications, applied to produce a binary mask, separating the reference object from the background. We show that this second method performs significantly better than the mathematical morphology approach when applied to the identification of brick interface keyways in AGR inspection images. Though improved in terms of accuracy, it is recognized that a greater initial effort is required to train the approach, and as it utilizes black-box neural network approaches, the greater transparency offered by the mathematical morphology approach is lost. While explicability of techniques is often a highly desirable characteristic of automated analysis techniques applied to health assessment within nuclear power plants, the results of the reference object detection can be made explicit to the end user, ensuring that the human analyst is retained within the decision-making process thus mitigating the need for transparency.

An Advanced Methodology of Visibility Used for Underwater Image Enhancement

This paper concentrates towards submerged processing of image so as to improve the picture quality. As the greater part of the pictures of seaward establishments, drinking water store and so forth are caught and examined physically by divers. And manual intervention in this regard is dangerous, costly, and time-consuming and yet does not often enable a full assessment. Hence camera based inspection is used to capture the images under water. Using cameras underwater poses major technological challenges. The objects in the submerged images are pale, difficult to sight and examine because the images of such environment loses the details of the object. The articles in the submerged pictures are swoon, hard to see and examine in light of the fact that the pictures of such condition loses the subtleties of the item. The submerged pictures typically experience the ill effects of non-uniform lighting, low difference, slant, obscures and lessened hues. Also, consequently in this examination work, a novel technique has been proposed for dealing with submerged picture slanting and obscuring if there should be an occurrence of unidirectional cyclic waves and roundabout waves to upgrade the perceivability of submerged pictures. The proposed work develops a geometrical model for image restoration from these distortions with good accuracy

Photometric camera characterization from a single image with invariance to light intensity and vignetting

Photometric characterization of a camera entails describing how the camera transforms the light reaching its sensors into an image and how this image can be defined in a standard color space. Although the research in this area has been extensive, the current literature lacks practical methods designed for cameras operating under near light. There are two major application scenarios considered in this paper that would benefit from this type of approaches. Camera rigs for minimally-invasive-procedures cannot be calibrated in the operating room with the current methods. This comes from the fact that existing approaches need multiple images, assume uniform lighting, and/or use over-simplistic camera models, which does not allow for the calibration of near light setups in a fast and reliable way. The second scenario refers to the calibration of cellphone cameras, which currently cannot be calibrated at close range with a single image, specially if the flash is used, as there would be non-uniform lighting on the scene. In this work, we describe a method to characterize cameras from a single image of a known target. This enables both geometric and photometric calibrations to be performed on-the-fly without making assumptions on the vignetting nor on the spatial properties of the light. The presented method showed good repeatability and color accuracy even when compared to multiple-image approaches. Applications to laparoscopic cameras, generic cameras (such as cellphone cameras), and cameras other than trichromatic are shown to be viable.

Automating RTI: Automatic light direction detection and correcting non-uniform lighting for more accurate surface normals

Reflectance Transformation Imaging (RTI) (Malzbender et al., 2001) is a photometric stereo technique that enables the interactive relighting of the object of interest from novel lighting directions, and an estimation of surface topography through the calculation of surface normal vectors. We propose a novel, fully automated technique for correcting common lighting errors in RTI and markedly improve the accuracy of surface normal estimation, as well as increasing the legibility of low relief surface variations. This moves RTI from the qualitative domain (e.g. enabling the reading of weathered inscriptions) into the quantitative domain of computer vision. RTI assumes only light direction, and not received intensity, changes as the object is imaged. Like other authors we show that this assumption is false and propose a novel method to correct for it. However, we estimate the lighting directions automatically, unlike other proposed correction techniques. Our method also requires no calibration equipment, meaning it can be easily retrofitted to any existing stack of RTI photographs. We increase the simplicity of the standard highlight RTI method by automatically detecting lighting directions and maintain its appeal to non-imaging professionals.

Mathematical Morphology and Optimum Principal Curvature Based Segmentation of Blood Vessels in Human Retinal Fundus Images

The retinal abnormalities and diagnosis of Diabetic Retinopathy (DR), Glaucoma are accomplished by extraction of vessel network in human retinal images. An accurate segmentation is required for the pathological analysis. Various researchers proposed many automated systems for vessel segmentation, still this process needs an improvement due to the presence of abnormalities, different magnitude, dimension of the vessels, non-uniform lighting and variable structure of the retina. The proposed work is a new method for retinal vessel segmentation, which consists of three phases, (i) The vessels network is enhanced by using Contrast Limited Adaptive Histogram Equalization(CLAHE) and Median filtering techniques (ii) the smoothened image is segmented based on mathematical morphology and maximum principal curvature followed by cleaning operation to remove the small objects, (iii) the segmented image is compared with hand labeled Ground Truth image and is evaluated with the True Positive, False Positive , True Negative and False Negative parameters. The performance of this work is tested with the images existing in DRIVE database. This work achieves 0.965 Accuracy, 0.752 Sensitivity and 0.989 Specificity.

Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel- Based Conditional Spatial Fuzzy C-Means Clustering Algorithm

Tooth component segmentation is a crucial task in computer-aided design for forensic odontology, especially to estimate chronological age. Tooth segmentation on radiographic data is a very challenging task due to noise, low contrast, and uneven illumination. The Fuzzy C-Means clustering is generally used for image segmentation that allow pixels to be classified into one or more clusters according to their membership value. However, this clustering method still has problems associated with the shifting of cluster centers and sensitivity to the overlapping intensity distributions between classes. This paper proposes a modified strategy of the conditional spatial Fuzzy C-Means (csFCM) that incorporates global and spatial information into a weighted membership function by replacing the Euclidean distance with the Gaussian kernel distance to increase insensitivity to noise and outliers. The aim of this paper is to divide the tooth into 3 components, i.e. enamel, dentine, and pulp. Therefore, this modified algorithm is preceded by dental X-ray image pre-processing and continued by combining each dental component clusters into one composite image. The tooth image is pre-processed using Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment to enhance the dental X-ray images quality from the non-uniform lighting. The Gausian kernel-based conditional spatial Fuzzy C-Means (GK-csFCM) segments the dental image into four class clusters, namely enamel, dentine, pulp, and background. Through iterations, the resulting cluster centers are more convergent with real cluster centers, thus ensuring the proposed method improves the drawback of inherent FCM-based methods and further promoting image segmentation performance. The experimental results on the real dental X-ray images showed that GK-csFCM has better performance than the typical FCM and csFCM clustering algorithms in terms of both qualitative and quantitative metrics, i.e. accuracy, specificity, sensitivity, and precision.

Editorial

Editorial

On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, nonuniform lighting, and nonfrontal face pose. In this paper, we analyze face recognition techniques using data captured under low-quality conditions in the wild. We provide a comprehensive analysis of experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made: {\em (i)} we conduct experiments to evaluate super-resolution methods for low-resolution face recognition; {\em (ii)} we study face re-identification on various public face datasets including real surveillance and low-resolution subsets of large-scale datasets, present a baseline result for several deep learning based approaches, and improve them by introducing a GAN pre-training approach and fully convolutional architecture; and {\em (iii)} we explore low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. Evaluations are conducted on challenging portions of the SCFace and UCCSface datasets.

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Diagnosis of computer-based retinopathic hypertension is done by analyzing of retinal images. The analysis is carried out through various stages, one of which is blood vessel segmentation in retinal images. Vascular segmentation of the retina is a complex problem. This is caused by non-uniform lighting, contrast variations and the presence of abnormalities due to disease. This makes segmentation not successful if it only relies on one method. The aims of this study to segment blood vessels in retinal images. The method used is divided into three stages, namely preprocessing, segmentation and testing. The first stage, preprocessing, is to improve image quality with the CLAHE method and the median filter on the green channel image. The second stage, segmenting using a number of methods, namely, frangi filter, 2D-convolution filtering, median filtering, otsu's thresholding, morphology operation, and background subtraction. The last step is testing the system using the DRIVE and STARE dataset. The test results obtained sensitivity 91.187% performance parameters, 86.896% specificity, and area under the curve (AUC) 89.041%. Referring to the performance produced, the proposed model can be used as an alternative for blood vessel segmentation of retinal images.