Illumination Changes Research Articles

Designing Face Detection Systems with Gray Wolf Optimization

The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method's resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems' performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution.

Optimization design and application of library face recognition access control system based on improved PCA.

The application of face recognition technology in Library Access Control System (LACS) has an important impact on improving the security and management efficiency of the library. However, the traditional face recognition methods have some limitations in the face of complex environmental conditions such as illumination and posture change. To solve this problem, an improved method combining the Aggregating Spatial Embeddings for Face Recognition (ASEF) algorithm and Principal Component Analysis (PCA) is proposed. The PCA algorithm is optimized by introducing beta prior and full probability Bayesian model. In addition, the research also integrates K-means Clustering Algorithm (KA) to further improve the accuracy and efficiency of face recognition. The experiment showed that the improved PCA method had an average recognition rate of 92.6%, an average recognition speed of 0.40s, and higher accuracy compared to other related methods, reaching 96%. In practical applications, the system quickly and accurately completes the identification of personnel entry and exit, and improves the efficiency and security of library access management.

License Plate Detection Methods Based on OpenCV

---Since the technology was discovered with the automobile, this advancement in computer vision detection technology has slowly become part of the intelligent traffic management system. This technology can be used to divide vehicle images into different parts and outline specific regions for further identification across many systems. It is also highly applied in intelligent traffic management and video surveillance for automobiles among other applications. This paper introduces a new state-of-the-art License Plate Detection system using the CV2 Algorithm. Existing approaches have been highly prone to substantial sensitivity of the changes in illumination, not well defined and rather complex backgrounds themselves together with the plates which deterd recognition. Generally, the proposed future system is expected to exhibit high accuracy improvement and the cost of recognition for the present system may offset the problems that the current system has. All these considerations are taken into account when designing the proposed system. The proposed system based on CV2 Algorithm is supposed to exhibit considerable efficiency and robustness in handling noisy data. In this context, we gave our working model the analytical results to prove that our proposed model is much more beyond the current system since it uses the Python programming language. KEYWORDS--- OpenCV2 Algorithm, OCR (Optical Character Recognition system), Automatic license plate recognition (ALPR), BINARIZATION, Gray threshold (GT), EDGE DETECTION, NUMBER PLATE LOCALIZATION, DESKEWING, SPYDER3.

Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets

Scale-invariant feature transform (SIFT) is widely used as an image local feature extraction method because of its invariance to rotation, scale, and illumination change. SIFT has been implemented in different program libraries. However, studies that analyze the performance of SIFT implementations have not been conducted. This study examines the keypoint extraction of three well-known SIFT libraries, i.e., David Lowe's implementation, OpenSIFT, and vlSIFT in vlfeat. Performance analysis was conducted on multiclass small-scale image datasets to capture the sensitivity of keypoint detection. Although libraries are based on the same algorithm, their performance differs slightly. Regarding execution time and the average number of keypoints detected in each image, vlSIFT outperforms David Lowe’s library and OpenSIFT.

Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision

Icing on wind turbine blades in cold and humid weather has become a detrimental factor limiting their efficient operation, and traditional methods for detecting blade icing have various limitations. Therefore, this paper proposes a non-contact ice volume estimation method based on binocular vision and improved image processing algorithms. The method employs a stereo matching algorithm that combines dynamic windows, multi-feature fusion, and reordering, integrating gradient, color, and other information to generate matching costs. It utilizes a cross-based support region for cost aggregation and generates the final disparity map through a Winner-Take-All (WTA) strategy and multi-step optimization. Subsequently, combining image processing techniques and three-dimensional reconstruction methods, the geometric shape of the ice is modeled, and its volume is estimated using numerical integration methods. Experimental results on volume estimation show that for ice blocks with regular shapes, the errors between the measured and actual volumes are 5.28%, 8.35%, and 4.85%, respectively; for simulated icing on wind turbine blades, the errors are 5.06%, 6.45%, and 9.54%, respectively. The results indicate that the volume measurement errors under various conditions are all within 10%, meeting the experimental accuracy requirements for measuring the volume of ice accumulation on wind turbine blades. This method provides an accurate and efficient solution for detecting blade icing without the need to modify the blades, making it suitable for wind turbines already in operation. However, in practical applications, it may be necessary to consider the impact of illumination and environmental changes on visual measurements.

Relative Contributions of sc-DER, mel-DER, Color Rendition, Chromaticity, and Illuminance to Spatial Brightness Perception

ABSTRACT An experiment was conducted to examine spectrally based factors that contribute to the visual perception of interior environments, with a focus on brightness perception. Thirty-two participants evaluated 60 different lighting scenes in a mock office. The lighting spectral power distributions varied systematically in illuminance, chromaticity (CCT and Duv), s cone opic daylight efficacy ratio (sc-DER), melanopic daylight efficacy ratio (mel-DER), and color rendition (R f, R g, and R cs,h1). At the operationalized levels of these variables, illuminance had the largest effect on brightness perception. Notably, the second largest effect was due to changes in red chroma (R cs,h1). The effect of sc-DER was also statistically significant but was a tertiary effect. The effects of mel-DER, CCT, and Duv were not statistically significant. This large effect of red chroma is consistent with the existing understanding that changes in color perception are often perceived when illuminance changes. With appropriate changes in color rendition and other factors held constant, spatial brightness perception was preserved through a decrease from 500 lux to 250 lux.

Genotyping peculiarities of wheat photosynthesis light induction and productivity under the drought effect

Background. In agrocenoses, leaf light conditions are known to be unstable due to intermittent cloud cover and shading by other leaves or spikes. However, with a change in irradiance, photosynthesis does not reach its final value instantly, but with a certain delay. Due to this photosynthetic efficiency of leaves and crops is generally lower compared to stationary conditions. At the same time, the vast majority of works devoted to the problems of photosynthetic apparatus functioning under unstable light conditions do not take into account an adverse impact on photosynthesis of such a common stressor as drought. The aim of the present work was to study the peculiarities of flag leaves CO2 and H2O gas exchange parameters with changes in illumination under conditions of optimal and insufficient water supply in order to explore the pattern of drought effect on the photosynthetic induction processes in connection with productivity of wheat plants of different genotypes. Materials and Methods. The research was carried out on bread winter wheat varieties Yednist, Bohdana, Perlyna Podillia under conditions of pot experiment. Control plants were grown under an optimal soil moisture of 70 % field capacity (FC). In the experimental pots, soil drought was created at the level of 30 % FC for 7 days during the earing–flowering period, after that the soil moisture was restored to the optimal level. The parameters of flag leaf gas exchange were measured on the seventh day of drought. Components of plants grain productivity were determined after reaching full grain maturity. Results. It was found that according to the parameters of light induction curves of CO2 assimilation and transpiration, wheat plants of different genotypes under drought conditions are differentiated more clearly than under normal water supply. An increase in the limiting role of stomata in the induction of photosynthesis under drought conditions and changes in illumination was shown. Drought disrupts the coherence of stomatal conductance regulation in interaction with CO2 assimilation processes. This affects the light induction curves of photosynthesis and transpiration, and ultimately leads to a decrease in grain productivity. Conclusions. It was shown that in order to assess the efficiency of the photosynthetic apparatus in providing wheat plants with assimilates and maintaining their grain productivity under unfavorable conditions, the parameters of the response to the changes in illumination must be taken into account.

Influences on rPPG-Based Spatial Blood Perfusion Maps

Abstract Recent studies show the feasibility of using local remote photoplethysmography (rPPG) for non-contact blood perfusion assessment by creating spatial pulsatile blood perfusion maps. While global rPPG has been widely studied for its robustness, e.g. for non-contact measurement of heart rate, local analyses pose greater challenges in terms of noise suppression and thus reliability. In this paper, the effect of temperature and illumination changes on signal-to-noise ratio (SNR) perfusion maps is analysed. The results show the importance of consistent temperature and controlled illumination for improving SNR and ensuring reliable blood perfusion measurements using rPPG. This emphasises the need for standardised external conditions for accurate interpretation of the results and medical applicability.

Sustainable illumination: experimental and simulation analysis of illumination for workers wellbeing in the workplace

Incorporating an illumination optimization plan into the worker safety management process in industrials is essential since the quality of workplace illumination has a significant impact on workers' well-being. The purpose of this study was to optimize illumination conditions in industrial workspaces using light-emitting diode (LED) technology with a color temperature of 6500 K and a CRI of 85%. To accomplish this, the DIALux illumination simulation tool was utilized. It was validated using measured illumination data from a lux meter (ST-1300 model 1308). Four factors—the number of luminaires, the effective height of the luminaires, the illumination technology, and the light loss factor—were applied to configure sixteen simulation scenarios. Subsequently, eleven quadratic polynomial equations were proposed for estimating the changes in the average illumination in eleven workplaces using the multiple regression analysis technique on the simulatively prepared dataset. The final step was assessing the desirability degree of the suitable illumination scenario. It was discovered that the DIALux software's illumination simulation error percentage fell within an acceptable range. The illumination technology factor had the greatest impact on the average illumination of workplaces. The most impactful interaction terms of the factors were illumination technology and the number of luminaires. For every workplace, the optimized illumination's desirability degree was expected to be more than 0.9. The proposed approach yields promising results and can be a valuable tool for improving the illumination conditions of workers in industries.

Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition.

Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize background-subtracted silhouette sequences to extract gait features. While effective and easy to compute, these methods are susceptible to variations in clothing, carried objects, and illumination changes, compromising the extraction of discriminative features in real-world applications. In contrast, model-based approaches using skeletal key points offer robustness against these covariates. Advances in human pose estimation (HPE) algorithms using convolutional neural networks (CNNs) have facilitated the extraction of skeletal key points, addressing some challenges of model-based approaches. However, the performance of skeleton-based methods still lags behind that of appearance-based approaches. This paper aims to bridge this performance gap by introducing a multi-biometric framework that extracts features from multiple HPE algorithms for gait recognition, employing feature-level fusion (FLF) and decision-level fusion (DLF) by leveraging a single-source multi-sample technique. We utilized state-of-the-art HPE algorithms, OpenPose, AlphaPose, and HRNet, to generate diverse skeleton data samples from a single source video. Subsequently, we employed a residual graph convolutional network (ResGCN) to extract features from the generated skeleton data. In the FLF approach, the features extracted from ResGCN and applied to the skeleton data samples generated by multiple HPE algorithms are aggregated point-wise for gait recognition, while in the DLF approach, the decisions of ResGCN applied to each skeleton data sample are integrated using majority voting for the final recognition. Our proposed method demonstrated state-of-the-art skeleton-based cross-view gait recognition performance on a popular dataset, CASIA-B.

Automatic exposure strategy network for robust visual odometry in environments with high dynamic range

The success of visual-based localization requires the image features to be sufficiently captured in well-exposed images and reliably tracked among image sequences. In this paper, a learning-based exposure strategy algorithm is developed to improve the performance of visual odometry or simultaneous localization and mapping in scenes with sudden illumination changes and high dynamic range (HDR). The exposure value is predicted by optimizing a novel image information metric, which is calculated based on a multi-scale image histogram. Experimental observations show that the proposed metric is highly related to the localization performance, and achieves better localization accuracy when compared to other state-of-the-art metrics. Using this novel metric, the exposure control network is designed to predict the exposure value for the next image given a sequence of recently captured images as inputs. For different application scenarios, alternative network frameworks are proposed with the options to include an optimization module providing improved exposure values for feedback learning, but with additional computation complexity. The approach is light-weight for real-time video applications and the images captured using the predicted exposure values are well-exposed with better detected and matched features in a variety of challenging HDR scenes. Code will be available in https://github.com/leejieyu13/HDR-AE.

MGCNet: Multi-granularity consensus network for remote sensing image correspondence pruning

Correspondence pruning aims to remove false correspondences (outliers) from an initial putative correspondence set. This process holds significant importance and serves as a fundamental step in various applications within the fields of remote sensing and photogrammetry. The presence of noise, illumination changes, and small overlaps in remote sensing images frequently result in a substantial number of outliers within the initial set, thereby rendering the correspondence pruning notably challenging. Although the spatial consensus of correspondences has been widely used to determine the correctness of each correspondence, achieving uniform consensus can be challenging due to the uneven distribution of correspondences. Existing works have mainly focused on either local or global consensus, with a very small perspective or large perspective, respectively. They often ignore the moderate perspective between local and global consensus, called group consensus, which serves as a buffering organization from local to global consensus, hence leading to insufficient correspondence consensus aggregation. To address this issue, we propose a multi-granularity consensus network (MGCNet) to achieve consensus across regions of different scales, which leverages local, group, and global consensus to accomplish robust and accurate correspondence pruning. Specifically, we introduce a GroupGCN module that randomly divides the initial correspondences into several groups and then focuses on group consensus and acts as a buffer organization from local to global consensus. Additionally, we propose a Multi-level Local Feature Aggregation Module that adapts to the size of the local neighborhood to capture local consensus and a Multi-order Global Feature Module to enhance the richness of the global consensus. Experimental results demonstrate that MGCNet outperforms state-of-the-art methods on various tasks, highlighting the superiority and great generalization of our method. In particular, we achieve 3.95% and 8.5% mAP5° improvement without RANSAC on the YFCC100M dataset in known and unknown scenes for pose estimation, compared to the second-best models (MSA-LFC and CLNet). Source code: https://github.com/1211193023/MGCNet.

Person re-identification transformer with patch attention and pruning

Person re-identification (Re-ID), which is widely used in surveillance and tracking systems, aims to search individuals as they move between different camera views by maintaining identity across various camera views. In the realm of person re-identification (Re-ID), recent advancements have introduced convolutional neural networks (CNNs) and vision transformers (ViTs) as promising solutions. While CNN-based methods excel in local feature extraction, ViTs have emerged as effective alternatives to CNN-based person Re-ID, offering the ability to capture long-range dependencies through multi-head self-attention without relying on convolution and downsampling. However, it still faces challenges such as changes in illumination, viewpoint, pose, low resolutions, and partial occlusions. To address the limitations of widely used person Re-ID datasets and improve the generalization, we present a novel person Re-ID method that enhances global and local information interactions using self-attention modules within a ViT network. It leverages dynamic pruning to extract and prioritize essential image patches effectively. The designed patch selection and pruning for person Re-ID model resulted in a robust feature extractor even in scenarios with partial occlusion, background clutter, and illumination variations. Empirical validation demonstrates its superior performance compared to previous approaches and its adaptability across various domains.

EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery.

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use.

Active vision in freely moving marmosets using head-mounted eye tracking.

Our understanding of how vision functions as primates actively navigate the real-world is remarkably sparse. As most data have been limited to chaired and typically head-restrained animals, the synergistic interactions of different motor actions/plans inherent to active sensing - e.g. eyes, head, posture, movement, etc. - on visual perception are largely unknown. To address this considerable gap in knowledge, we developed an innovative wireless head-mounted eye tracking system called CEREBRO for small mammals, such as marmoset monkeys. Our system performs Chair-free Eye-Recording using Backpack mounted micROcontrollers. Because eye illumination and environment lighting change continuously in natural contexts, we developed a segmentation artificial neural network to perform robust pupil tracking in these conditions. Leveraging this innovative system to investigate active vision, we demonstrate that although freely-moving marmosets exhibit frequent compensatory eye movements equivalent to other primates, including humans, the predictability of the visual behavior (gaze) is higher when animals are freely-moving relative to when they are head-fixed. Moreover, despite increases in eye/head-motion during locomotion, gaze stabilization remains steady because of an increase in VOR gain during locomotion. These results demonstrate the efficient, dynamic visuo-motor mechanisms and related behaviors that enable stable, high-resolution foveal vision in primates as they explore the natural world.

A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping.

Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping). This framework achieves tightly coupled integration at the data level using inputs from an IMU (Inertial Measurement Unit), an infrared camera, an RGB (Red, Green and Blue) camera, and LiDAR. We propose a real-time luminance calculation model and verify its conversion accuracy. Additionally, we designed a fast approximation method for the nonlinear weighted fusion of features from infrared and RGB frames based on luminance values. Finally, we optimize the VIO (Visual-Inertial Odometry) module in the R3LIVE++ (Robust, Real-time, Radiance Reconstruction with LiDAR-Inertial-Visual state Estimation) framework based on the infrared camera's capability to acquire depth information. In a controlled study, using a simulated indoor rescue scenario dataset, the IIVL-LM system demonstrated significant performance enhancements in challenging luminance conditions, particularly in low-light environments. Specifically, the average RMSE ATE (Root Mean Square Error of absolute trajectory Error) improved by 23% to 39%, with reductions from 0.006 to 0.013. At the same time, we conducted comparative experiments using the publicly available TUM-VI (Technical University of Munich Visual-Inertial Dataset) without the infrared image input. It was found that no leading results were achieved, which verifies the importance of infrared image fusion. By maintaining the active engagement of at least three sensors at all times, the IIVL-LM system significantly boosts its robustness in both unknown and expansive environments while ensuring high precision. This enhancement is particularly critical for applications in complex environments, such as indoor rescue operations.

A Comparative Study on Detection and Recognition of Nonuniform License Plates

This paper presents a comparative study on license plate detection and recognition algorithms in unconstrained environments, which include varying illuminations, nonstandard plate templates, and different English language fonts. A prime objective of this study is to assess how well these models handle such challenges. These problems are common in developing countries like Pakistan where diverse license plates, styles, and abrupt changes in illuminations make license plates detection and recognition a challenging task. To analyze the license plate detection problem Faster-RCNN and end-to-end (E2E) methods are implemented. For the license plate recognition task, deep neural network and the CA-CenterNet-based methods are compared. Detailed simulations were performed on authors’ own collected dataset of Pakistani license plates, which contains substantially different multi-styled license plates. Our study concludes that, for the task of license plate detection, Faster-RCNN yields a detection accuracy of 98.35%, while the E2E method delivers 98.48% accuracy. Both detection algorithms yielded a mean detection accuracy of 98.41%. For license plate recognition task, the DNN-based method yielded a recognition accuracy of 98.90%, while the CA-CenterNet-based method delivered a high accuracy of 98.96%. In addition, a detailed computational complexity comparison on various image resolutions revealed that E2E and the CA-CenterNet are more efficient than their counterparts during detection and recognition tasks, respectively.

Multi-Head Attention Affinity Diversity Sharing Network for Facial Expression Recognition

Facial expressions exhibit inherent similarities, variability, and complexity. In real-world scenarios, challenges such as partial occlusions, illumination changes, and individual differences further complicate the task of facial expression recognition (FER). To further improve the accuracy of FER, a Multi-head Attention Affinity and Diversity Sharing Network (MAADS) is proposed in this paper. MAADS comprises a Feature Discrimination Network (FDN), an Attention Distraction Network (ADN), and a Shared Fusion Network (SFN). To be specific, FDN first integrates attention weights into the objective function to capture the most discriminative features by using the proposed sparse affinity loss. Then, ADN employs multiple parallel attention networks to maximize diversity within spatial attention units and channel attention units, which guides the network to focus on distinct, non-overlapping facial regions. Finally, SFN deconstructs facial features into generic parts and unique parts, which allows the network to learn the distinctions between these features without having to relearn complete features from scratch. To validate the effectiveness of the proposed method, extensive experiments were conducted on several widely used in-the-wild datasets including RAF-DB, AffectNet-7, AffectNet-8, FERPlus, and SFEW. MAADS achieves the accuracy of 92.93%, 67.14%, 64.55%, 91.58%, and 62.41% on these datasets, respectively. The experimental results indicate that MAADS not only outperforms current state-of-the-art methods in recognition accuracy but also has a relatively low computational complexity.

Global Semantic Localization from Abstract Ellipse-Ellipsoid Model and Object-Level Instance Topology

Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this paper, we propose a visual localization method based on an ellipse-ellipsoid model, combined with object-level instance topology and alignment. First, we develop a CNN-based (Convolutional Neural Network) ellipse prediction network, DEllipse-Net, which integrates depth information with RGB data to estimate the projection of ellipsoids onto images. Second, we model environments using 3D (Three-dimensional) ellipsoids, instance topology, and ellipsoid descriptors. Finally, the detected ellipses are aligned with the ellipsoids in the environment through semantic object association, and 6-DoF (Degree of Freedom) pose estimation is performed using the ellipse-ellipsoid model. In the bounding box noise experiment, DEllipse-Net demonstrates higher robustness compared to other methods, achieving the highest prediction accuracy for 11 out of 23 objects in ellipse prediction. In the localization test with 15 pixels of noise, we achieve ATE (Absolute Translation Error) and ARE (Absolute Rotation Error) of 0.077 m and 2.70∘ in the fr2_desk sequence. Additionally, DEllipse-Net is lightweight and highly portable, with a model size of only 18.6 MB, and a single model can handle all objects. In the object-level instance topology and alignment experiment, our topology and alignment methods significantly enhance the global localization accuracy of the ellipse-ellipsoid model. In experiments involving lighting changes and occlusions, our method achieves more robust global localization compared to the classical bag-of-words based localization method and other ellipse-ellipsoid localization methods.

Detection of optic disc in human retinal images utilizing the Bitterling Fish Optimization (BFO) algorithm

Early detection and correct identification of the optic disc (OD) on scanned retinal images are significant for diagnosing and treating several ophthalmic conditions, including glaucoma and diabetic retinopathy. Conventional methods for detecting the OD often struggle with processing retinal images due to noise, changes in illumination, and complex overlapping images. This study presents the development of effective and accurate fixation of the optic disc using the Bitterling Fish Optimization (BFO) algorithm, which enhances the processes of OD imaging in speed and precision. The proposed method begins with image enhancement and noise suppression for preprocessing, followed by applying the BFO algorithm to locate and delineate the OD region. The performance evaluation of the algorithm was conducted within several public domain retinal images, including DRIVE, STARE, ORIGA, DRISHTI-GS, DiaRetDB0, and DiaRetDB1 datasets about some internal metrics: sensitivity (SE), specificity (SP), accuracy (ACC), DICE overlap coefficient, overlap and time of processing respectively. The technique based on BFO provided better results, with 99.33%, 99.94%, and 98.22% accuracy achieved for OD in DRIVE, DRISHTI-GS, and DiaRetDB 1, respectively. The approach also demonstrated high overlaps and good DICE results, with a DICE coefficient of 0.9501 for the DRISHTI-GS database. On average, the processing time per image was less than 2.5 s, proving the approach’s efficiency in computations. The BFO approach has demonstrated its effectiveness and scalability in detecting optic discs in retinal images in an automated manner. It showed impressive performance levels in terms of computation time and accuracy and was variation resistant irrespective of the quality of the image and the pathology present on it. This method holds significant potential for clinical use, providing a meaningful way of diagnosing and managing ocular disease at an early stage.