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Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model's performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints. [ABSTRACT FROM AUTHOR]

Developing deep learning network models for computer vision applications in concrete damage detection is a challenging task due to the shortage of training images. To address this issue, this study proposes a novel style-controlled image synthesis method for concrete damages based on the fusion of a convolutional encoder and an attention-enhanced conditional generative adversarial network. This makes it possible to generate effective images that can improve the damage detection performance of deep learning networks. To achieve this, a network architecture for concrete damage image synthesis, named DamageGAN-AE, was designed by fusing a convolutional encoder and an attention-enhanced conditional generative adversarial network. The DamageGAN-AE networks with different attention modules were trained, and the training results show that the well-trained DamageGAN-AE enhanced by coordinate attention is the best model for concrete damage image synthesis. The well-trained DamageGAN-AE was compared with the current competing methods to verify its performance. The DamageGAN-AE with image encoder was trained to implement the style-controlled image synthesis. Finally, the generated concrete damage images with diverse styles by the DamageGAN-AE model with image encoder were used to train deep learning networks. The results indicate that the generated style-controlled concrete damage images by the proposed method can effectively improve the concrete damage detection performance of deep learning networks. [ABSTRACT FROM AUTHOR]

Engineering materials in constructed systems in service exhibit complex patterns, including structural damage, environmental artifacts, and artificial anomalies. In recent years, machine vision methods have been extensively studied, most of which train models using regular grey or color images in the visible bands and label at pixel levels with a large volume of data. The authors propose using hyperspectral imaging (HSI) for structural material condition assessment in this work. Compared with visible images, the research challenge is that HSI pixels with high-dimensional spectral profiles are beyond human perceptive capabilities with hidden discriminative power. Learning from labeled and unlabeled data is one direct approach to unlocking this power. A deep neural network-enabled spatial-spectral feature extraction and a semisupervised learning architecture were developed in this work. A human-in-the-loop (HITL) framework was comparatively studied with three incremental training-data configuration schemes. The paper concludes with the following empirical findings: (1) fully supervised learning determines the baseline of the detection performance; (2) an extensive range of ratio values exists between the unlabeled and the labeled data for incremental semisupervised learning, and a 1∶1 ratio can be taken as a conservative and operational ratio; and (3) with parametric semisupervised learning with equal labeled and unlabeled data participation, the proposed HITL operational workflow can be implemented as a practical approach for HSI-based structural material and damage detection. [ABSTRACT FROM AUTHOR]

To bridge the gap in as-built Building Information Model (BIM) creation between the architectural, engineering, and construction (AEC) community and the computer vision community, this paper presents an automated Scan-to-BIM framework for modeling both structural and nonstructural building components using a low-cost scanning data. The state-of-the-art instance-level semantic segmentation algorithm, SoftGroup, is adopted to classify individual building components. Detected wall segments are projected onto a two-dimensional (2D) XY grid, and an interest point detection algorithm, SuperPoint, is used to extract wall corner points. Subsequently, a series of refinement steps is proposed to generate the wall boundary. With optimized parameters, an intersection-over-union of 82.56% was achieved when tested on the benchmark Stanford Three-Dimensional (3D) Indoor Scene Data Set. Our results demonstrated the usability of the proposed wall boundary extraction to the incomplete and complex indoor scan data compared to an existing as-built modeling method. Instance-level segments and the refined wall boundary were combined to generate as-built BIM via parametric modeling. [ABSTRACT FROM AUTHOR]

Human action recognition is one of the most challenging tasks in computer vision due to background noise interference and video frame redundancy. Therefore, we propose a two-stream Convolutional Neural Network based on Spatial-Temporal Multiscale Feature Optimization (ST-MFO). Specifically, multiscale features generated by a pyramid pooling network are combined with improved coordinate attention, which results in richer feature representation and reduces background noise interference. Meanwhile, we introduce density peak clustering based on a nonlinear kernel function, which can extract more representative key frames. To improve classification efficiency, we also assign varying degrees of attention to key frames through temporal attention. In addition, we propose an attention-based spatial-temporal information interaction module that optimizes temporal and spatial features with complementarity between temporal and spatial information. Experimental results on four benchmark video datasets show that ST-MFO achieves comparable or better performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The breakthroughs in Machine learning and deep neural networks have revolutionized the handling of critical practical challenges, achieving state-of-the-art performance in various computer vision tasks. Notably, the application of deep neural networks in optical character recognition (OCR) has significantly enhanced the performance of OCR systems, making them a pivotal preprocessing component in text analysis pipelines for crucial applications such as license plate recognition (LPR) systems, where the efficiency of OCR is paramount. However, despite the advancements, the integration of deep neural networks in OCR introduces inherent security vulnerabilities, particularly susceptibility to adversarial examples. Adversarial examples in LPR systems are crafted by introducing perturbations to original license plate images, which can effectively compromise the integrity of the license plate recognition process, leading to erroneous license plate number identification. Given that the primary goal of OCR in this context is to accurately recognize license plate numbers, even a single misinterpreted character can significantly impact the overall performance of the LPR system. The vulnerability of LPR systems to adversarial attacks underscores the urgent need to address the security weaknesses inherited from deep neural networks. In response to these challenges, the exploration of alternative defense mechanisms, such as image denoising and in-painting, presents a compelling approach to bolstering the resilience of LPR systems against adversarial attacks. By prioritizing practical implementation and integration of image denoising and inpainting techniques align with the operational requirements of real-world LPR systems. These methods can be seamlessly integrated into existing pipelines, offering pragmatic and accessible means of enhancing security without imposing significant computational overhead. By embracing a multi-faceted approach that combines the strengths of traditional image processing techniques, the research endeavors to develop comprehensive and versatile defense strategies tailored to the specific vulnerabilities and requirements of LPR systems. This holistic approach aims to fortify LPR systems against adversarial threats, thereby fostering increased trust and reliability in the deployment of OCR and LPR technologies across various domains and applications. [ABSTRACT FROM AUTHOR]

Automatic gender classification is an important task in facial analysis. Recently, deep learning has achieved remarkable progress in extracting deep features for face image analysis, particularly in gender classification. Gender classification through facial analysis poses a difficult challenge in extracting reliable and relevant features from facial images and classifying them accurately, especially in uncontrolled environments. So, in this study, a novel deep convolutional neural network (DCNN) aided with convolutional long short-term memory (ConvLSTM) layer and squeeze-and-excitation (SE) block is designed to extract most distinguishable and relevant features from facial images. The ConvLSTM layer aims to establish spatial relationship among the extracted feature maps, while the SE block enhances the emphasis on critical features by directing more attention towards them. The extracted features are pre-processed and deployed to Kernel principle component analysis (KPCA) for dimensionality reduction. Finally, these reduced features are fed into a stacking classifier that utilizes several machine learning classifiers such as K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost, Extra Tree Classifier (ETC), Gaussian Naive Bayes (GNB), XGBoost classifier (XBC), Support Vector classifier (SVC), Bagging Classifier, NuSVC, Gradient boosting classifier (GBC) for the first stage,and SVC as a meta-classifier for final gender prediction with Stratified K-fold cross-validation in order to mitigate the issue of overfitting. The proposed method was evaluated on two publicly available datasets, label faces in the wild (LFW) and Adience, and achieved accuracies of 99.20% and 98.01%, respectively. These results indicate that our proposed approach outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Conventional manual evaluation techniques for MGD face limitations characterized by inefficiencies, high subjectivity, limited big data processing capabilities, and a dearth of quantitative analytical tools. With rapidly advancing artificial intelligence (AI) techniques revolutionizing ophthalmology, studies are now leveraging sophisticated AI methodologies--including computer vision, unsupervised learning, and supervised learning--to facilitate comprehensive analyses of meibomian gland (MG) evaluations. These evaluations employ various techniques, including slit lamp examination, infrared imaging, confocal microscopy, and optical coherence tomography. This paradigm shift promises enhanced accuracy and consistency in disease evaluation and severity classification. While AI has achieved preliminary strides in meibomian gland evaluation, ongoing advancements in system development and clinical validation are imperative. We review the evolution of MG evaluation, juxtapose AI-driven methods with traditional approaches, elucidate the specific roles of diverse AI technologies, and explore their practical applications using various evaluation techniques. Moreover, we delve into critical considerations for the clinical deployment of AI technologies and envisages future prospects, providing novel insights into MG evaluation and fostering technological and clinical progress in this arena. [ABSTRACT FROM AUTHOR]

Eye-controlled human-computer interaction (ECHCI) attracts attention for its human-centered, natural and direct operation characteristics. The most common ECHCI trigger motion is "gazing," which causes Midas Touch problems, lowering usability and operation experience. This paper innovatively proposed using motion combinations based on blinking as the trigger of interactive objects (IOs). Trigger motions and IO design parameters were explored on the platform consisting of Tobii eye-tracker and computer vision kits. In the motion experiment, it was concluded 2-blinks could bring a low task load and high success rate of 95%. In the IO size experiment, the larger the IO diameter, the shorter the users' response time and the higher users' interaction success rate, and 55.5 mm was the optimal setting. Based on this, it was found a larger spacing could bring higher operation efficiency, and 33.3 mm was taken as the recommended value. Based on the conclusion above, an eye-controlled multimedia player prototype was built for usability evaluation, which showed an equally high efficiency compared to mouse-controlled HCI. The findings in this paper overcame Midas Touch issues and avoided the interference of unavoidable involuntary blinking, which brought a significant increase in system robustness and has a wide range of potential applications. [ABSTRACT FROM AUTHOR]

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios. [ABSTRACT FROM AUTHOR]

In today's fast-moving digital era, video technology plays an important role. An effective video summarising approach is urgently needed to handle a lot of video data due to the ever-growing number of video content. In this paper, the authors have proposed, A hybrid summarization methodology for video summary evaluation using multimedia features (Text, Images, and Audio) that assess how well a video summary can keep the ranking of vital video frames, semantic data, and audio present in the original video. Video summary can be evaluated by ranking text, audio, and semantics of video frames, giving more accurate summarisation results. The proposed methodology works in three phases: The first part takes the text in the video, the second phase takes the audio to the file, and the last phase focuses on the video frames rather than images in the video. TVSum dataset has been used for the experimentation. F1 has been used as the evaluation metric for checking the efficacy and efficiency of the proposed methodology. The result shows that the proposed hybrid model achieves the highest F1 score of 69.9% and saves 75-80% of user time in watching video summaries instead of the whole video. [ABSTRACT FROM AUTHOR]

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Copyright of Wiener Klinisches Magazin is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all‐day environmental perception. This paper proposes a novel method named FCE‐RCNN, which integrates saliency detection as a sub‐task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw‐data level before feature extraction. Utilizing a dual‐stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high‐quality global semantic information for lower‐level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross‐spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE‐RCNN significantly improves nighttime detection, achieving a log‐average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE‐RCNN, and the method also maintains competitive inference speed, making it suitable for real‐time applications. [ABSTRACT FROM AUTHOR]

The steel manufacturing process is inherently continuous, meaning that if defects are not effectively detected in the initial stages, they may propagate through subsequent stages, resulting in high costs for corrections in the final product. Therefore, detecting surface defects and obtaining segmentation information is critical in the steel manufacturing industry to ensure product quality and enhance production efficiency. Specifically, segmentation information is essential for accurately understanding the shape and extent of defects, providing the necessary details for subsequent processes to address these defects. However, the time-consuming and costly process of generating segmentation annotations poses a significant barrier to practical industrial applications. This paper proposes a cost-efficient segmentation labeling framework that combines deep learning-based anomaly detection and label enhancement to address these challenges in the steel manufacturing process. Using ResNet-50, defects are classified, and faster region convolutional neural networks (faster R-CNNs) are employed to identify defect types and generate bounding boxes indicating the defect locations. Subsequently, recursive learning is performed using the GrabCut algorithm and the DeepLabv3+ model based on the generated bounding boxes, significantly reducing annotation costs by generating segmentation labels. The proposed framework effectively detects defects and accurately defines them, even in randomly collected images from the steel manufacturing process, contributing to both quality control and cost reduction. This study presents a novel approach for improving the quality of the steel manufacturing process and is expected to enhance overall efficiency in the steel manufacturing industry. [ABSTRACT FROM AUTHOR]

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected. [ABSTRACT FROM AUTHOR]

The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 × 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system's ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction. [ABSTRACT FROM AUTHOR]

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP). [ABSTRACT FROM AUTHOR]

Single-stage detectors have drawbacks of insufficient accuracy and poor coverage capability. YOLOF (You Only Look One-level Feature) has achieved better performance in this regard, but there is still room for improvement. To enhance the coverage capability for objects of different scales, we propose an improved single-stage object detector: Dq-YOLOF. We have designed an output encoder that employs a series of modules utilizing deformable convolution and SimAM (Simple Attention Module). This module replaces the dilated convolution in YOLOF. This design significantly improves the ability to express details. Simultaneously, we have redefined the sample selection strategy, which optimizes the quality of positive samples based on SimOTA. It can dynamically allocate positive samples according to their quality, reducing computational load and making it more suitable for small objects. Experiments conducted on the COCO 2017 dataset also verify the effectiveness of our method. Dq-YOLOF achieved 38.7 A P , 1.5 A P higher than YOLOF. To confirm performance improvements on small objects, our method was tested on urinary sediment and aerial drone datasets for generalization. Notably, it enhances performance while also lowering computational costs. [ABSTRACT FROM AUTHOR]

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops. [ABSTRACT FROM AUTHOR]

A robust computer vision system is proposed to visualize subsurface barely visible impact damage (BVID) in composite structures through a simple image correlation technique together with a damage imaging condition. This system uses a digital camera to record a video of the surface motion, capturing micron-scale dynamic movement from guided waves propagating on the surface of the structure generated via a sweeping frequency excitation (chirp) up to the ultrasonic frequency range. As the excitation frequency changes during the chirp, waves become trapped within this damaged region, forming standing waves to generate local resonance at specific frequencies. This localized resonance accumulates high wave energy in the region, generating a higher transverse displacement at the damage site compared to the remaining part of the structure. In this work, a simple image correlation technique is proposed to correlate each filtered video frame with the temporal mean of the filtered wavefield video to highlight standing waves at localized damage. The proposed image correlation technique is distinct from digital image correlation (DIC) since it does not use correlation for subset matching to track the movement of surface patterns between two images (video frames). Instead, it uses correlation to directly quantify similarities between corresponding image pixels (windows). Utilizing a single camera greatly simplifies system complexity and hence enhances the practicality and potential for real-time performance over a recently developed technique that utilized 3D DIC with a stereo camera for vision-based BVID detection. To realize the aim, work was conducted in two sequential steps: (1) off-axis 2D DIC was employed rather than 3D DIC to examine the potential of employing a single camera to capture images and extract not only in-plane displacement but also a fraction of transverse displacement in which local resonance is dominant and (2) image correlation was then employed, supplanting the off-axis 2D DIC image processing involved in the first step, to highlight the damage with significantly less processing complexity. This proposed technique using a zero-mean normalized cross-correlation imaging condition, rather than the total wave energy used for DIC-based approaches, is efficient and effective for identifying regions of minute surface movement from local resonance within the damage region without the use of the computationally intensive interpolation to get the sub-pixel gray level information followed by subset matching employed in DIC-based image processing. Two geometrically identical CFRP composite honeycomb panels that had been subjected to low-velocity impacts were used for verification and validation, and two excitation location configurations were tested for each panel. Damaged images produced with correlation for a 100 mm × 100 mm field of view using a single 3-s video, or a total of 19,200 video frames, show accurate damage imaging capabilities regardless of excitation location that exceed that of DIC techniques and is comparable to benchmark damage images obtained from laser Doppler vibrometry, ultrasonic C-scans, and X-ray CT scans. This success of correlation-based imaging of subsurface BVID demonstrates substantial improvements in efficiency and practicality and shows high potential for in situ and real-time computer vision-based nondestructive inspection or structural health monitoring of subsurface BVID in composite aircraft and other critical structures. [ABSTRACT FROM AUTHOR]

Concrete crack detection is a crucial task for the safety and durability of engineering structures. Extensive research has been conducted on deep-learning methods employing horizontal bounding boxes (HBBs) for crack detection. However, due to the inherently random distribution of concrete cracks, HBB-based methods often produce excessive overlaps and encompass extensive background regions, obstructing the effective interpretation and adaptation of the detection results. To address this issue and achieve efficient utilization of bounding box space for detecting cracks at any orientation, a rotated bounding box (RBB)-based method, that is, Rotated Faster R-CNN with a post processing strategy (RFR-P), was proposed. To realize this method, an RBB-based crack annotation strategy was introduced to standardize the annotation baseline for the evolutionarily established RBB-based crack detection dataset. Then, an RBB-based post-processing strategy was inventively developed to quantify the patterns of cracks with their corresponding rotation angles encompassing longitudinal cracks, transverse cracks, and diagonal cracks. Subsequently, experimental results showed that the RFR-P method provides more reasonable and elaborate detection results in terms of crack distribution patterns when compared to HBB-based methods. Based on the comprehensive consideration of evaluation metrics and detected results, it can be concluded that the RFR-P is aptly designed for detecting cracks at any rotation angle with relatively high accuracy. Finally, an RBB-based concrete crack detection platform was established to automatically detect in situ concrete bridge cracks for real-world applications. The proposed RFR-P model introduces a new perspective on crack detection methods and offers practical references for structural condition evaluation. [ABSTRACT FROM AUTHOR]

Traditional image and video compression methods are designed to maintain the quality of human visual perception, which makes it necessary to reconstruct the image or video before machine analysis. Compression methods oriented towards machine vision tasks make it possible to use the bit stream directly for machine vision tasks, but it is difficult for them to decode high quality images. To bridge the gap between machine vision tasks and signal-level representation, researchers present plenty of the human-machine collaborative compression methods. In order to provide researchers with a comprehensive understanding of this field and promote the development of image and video compression, we present this survey. In this work, we give a problem definition and explore the relationship and application scenarios of different methods. In addition, we provide a comparative analysis of existing methods on compression and machine vision tasks performance. Finally, we provide a discussion of several directions that are most promising for future research. [ABSTRACT FROM AUTHOR]

With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within 5%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic methods are invasive, time-consuming, and costly. There is a need for non-invasive, efficient, and affordable solutions in diabetic foot care. Methods: We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. Additionally, deep-learning models were implemented to classify infection and ischemia in DFUs. The preliminary performance of the platform was tested on wound images acquired using a cell phone. Results: DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. The system successfully measured wound size and performed tissue color and textural analysis for a comparative assessment of macroscopic wound features. In clinical testing, DFUCare localized wounds and predicted infected and ischemic with an error rate of less than 10%, underscoring the strong performance of the platform. Discussion: DFUCare presents an innovative approach to wound care, offering a cost-effective, remote, and convenient healthcare solution. By enabling non-invasive and accurate analysis of wounds using mobile devices, this platform has the potential to revolutionize diabetic foot care and improve clinical outcomes through early detection of infection and ischemia. [ABSTRACT FROM AUTHOR]

Scrap steel is a green resource that can substitute iron ore and is an important raw material in the modern steel industry. To address the many issues such as high risk, low accuracy in grading, and the susceptibility to questioning fairness in the manual inspection process of scrap steel, we propose an efficient intelligent scrap steel classification method based on machine vision, achieving accurate classification and grading of nine types of scrap steel. Firstly, a scrap steel quality inspection system was established at the scrap steel recycling site, where images of various types of scrap steel were collected and various image processing methods were employed for preprocessing, leading to the establishment of scrap steel datasets and carriage segmentation datasets. Secondly, a carriage segmentation model was built based on image segmentation technology to significantly reduce the influence of complex backgrounds of scrap steel images on classification and grading. Subsequently, an intelligent scrap steel classification grading model was established based on the attention mechanism in deep learning, combined with the Spatially Adaptive Heterogeneous Image Slicing (SAHI) image slicing prediction method, achieving accurate classification and grading of scrap steel under complex backgrounds and high-resolution images in scrap steel recycling. Finally, we conducted tests on the proposed method. Experimental results demonstrate the good generalization of our proposed method, accurately detecting various types of scrap steel, meeting the requirements of accuracy, real-time performance, and good generalization in scrap steel recycling classification and grading, achieving initial industrial application, and exhibiting significant advantages compared to traditional manual scrap steel quality inspection. [ABSTRACT FROM AUTHOR]

Image quality assessment of authentically distorted images constitutes a indispensable part of numerous computer vision tasks. Despite the substantial progress in recent years, accuracy and generalization performance is still unsatisfactory. These challenges are primarily attributed to the scarcity of labeled images. In order to increase the amount of images for training, we use semi-supervised learning to combine labeled images and specifically selected unlabeled images. In our new training paradigm, denominated Selected Data Retrain under Regularization, the selection criteria of unlabeled images is based on the supposition that an image and a certain of its patches ought to have approximate image quality scores. Unlabeled images that meets the aforementioned criteria, named as Highly Credible Unlabeled Images, mitigate the problem of scarcity, thus, improve accuracy. However generalization may be compromised due to selection procedure's reliance on labeled images and presence of coherent variance existed between labeled images and unlabeled images. Therefore we incorporate a sorting loss function to reduce variation within the new dataset of labeled images and specifically selected unlabeled images, and thus achieve better generalization. The effectiveness of our proposed paradigm is empirically validated using public datasets. Codes are available at https://github.com/dvstter/SDRR_IQA. [ABSTRACT FROM AUTHOR]

Image captioning is a challenging task in computer vision that automatically generates a textual description of an image by integrating visual and linguistic information, as the generated captions must accurately describe the image's content while also adhering to the conventions of natural language. We adopt the encoder-decoder framework employed by various CNN-RNN-based models for image captioning in the past few years. Recently, we observed that the CNN-Transformer-based models have achieved great success and surpassed traditional CNN-RNN-based models in the area. Many researchers have concentrated on Transformers, exploring and uncovering its vast possibilities. Unlike conventional CNN-RNN-based models in image captioning, transformer-based models have achieved notable success and offer the benefit of handling longer input sequences more efficiently. However, they are resource-intensive to train and deploy, particularly for large-scale tasks or for tasks that require real-time processing. In this work, we introduce a lightweight and efficient transformer-based model called the Efficient Transformer Captioner (ETransCap), which consumes fewer computation resources to generate captions. Our model operates in linear complexity and has been trained and tested on MS-COCO dataset. Comparisons with existing state-of-the-art models show that ETransCap achieves promising results. Our results support the potential of ETransCap as a good approach for image captioning tasks in real-time applications. Code for this project will be available at https://github.com/albertmundu/etranscap. [ABSTRACT FROM AUTHOR]

Adversarial patches can disrupt computer vision systems, seriously threatening people's lives and property security. Existing defense methods seldom consider the generalization for defending against different patches and the compatibility with various models. Furthermore, the severe security situation necessitates the combination of data defense and model defense to build a comprehensive defense system. To address these issues, we propose a defense method named PatchBreaker, which consists of three components. In data defense, PatchBreaker uses the Semantic-Cutter trained by annotated patch images to cut patches and output incomplete images. Next, the Image-Inpainter trained by clean-incomplete image pairs is used to inpaint these incomplete images and output inpainted images. In model defense, the Adversaril-Classifier will be trained by joint adversarial training with clean images and patch images. Finally, PatchBreaker inputs inpainted images into Adversarial-Classifier to output correct results. Comparative experiments show that PatchBreaker outperforms other comparative defense methods in most cases, which indicates the excellent patch generalization and model compatibility of PatchBreaker. Meanwhile, ablation studies show the effectiveness of combining data defense and model defense. Additionally, PatchBreaker has minimal impact on the clean accuracy (about 1 % ). The background, mechanisms and defense effectiveness of PatchBreaker. Intelligent systems are easily disrupted by adversarial patches. Therefore, we propose the PatchBreaker, which consists of data defense and model defense. In data defense, the Semantic-Cutter cuts patches by BiseNetV2 model and output incomplete images, then the Image-Inpainter inpaints incomplete images by a bilateral image inpainting model. In model defense, we utilize clean and patch images to train the Adversarial-Classifier for classifying inpainted images to output correct results. After defense, the green high-light regions of integrated gradients attribution are more regular, which indicates the effectiveness of PatchBreaker [ABSTRACT FROM AUTHOR]

The article discusses the technology for automated neural network monitoring of the vineyard's physiological condition. Images of leaves, obtained using an unmanned aerial vehicle (UAV), are the main indicator of the physiological vineyard's condition. The proposed solution is based on the integrated use of convolutional neural network method (CNN) and machine vision technologies. To determine the optimal neural network (NN) model, a variant analysis was carried out. In accordance with its results, the YOLOv7 model was chosen, which satisfies the introduced time limit and provides the required detection quality. The training of the YOLOv7 neural network was implemented in the Python environment using the PyTorch framework and the OpenCV computer vision library. The dataset consisting of 6320 images of grape leaves (including healthy and diseased ones) has been used for neural network training. The obtained results showed that the detection accuracy is at least 91%. Visualization of monitoring results has been carried out using heatmap, allowing to obtain information about vineyard physiological condition in dynamics. The proposed mathematical model allows to calculate the monitored vineyard's area made by one complex per day. The obtained results showed that effective monitoring area using one DJI Phantom 4 UAV per day is 2.5 hectares. [ABSTRACT FROM AUTHOR]

MR-only radiotherapy planning is beneficial from the perspective of both time and safety since it uses synthetic CT for radiotherapy dose calculation instead of real CT scans. To elevate the accuracy of treatment planning and apply the results in practice, various methods have been adopted, among which deep learning models for image-to-image translation have shown good performance by retaining domain-invariant structures while changing domain-specific details. In this paper, we present an overview of diverse deep learning approaches to MR-to-CT synthesis, divided into four classes: convolutional neural networks, generative adversarial networks, transformer models, and diffusion models. By comparing each model and analyzing the general approaches applied to this task, the potential of these models and ways to improve the current methods can be can be evaluated. [ABSTRACT FROM AUTHOR]

The rapid and repeatable characterization of individual morphology has advanced automated taxonomic classification. The most direct study of evolutionary processes is, however, not from taxonomic description, but rather of the evolution of the traits that comprise individuals and define species. Repeatable signatures of individual morphology are crucial for analysing the response to selection at scale, and thus tracking evolutionary trajectories through time and across species boundaries.Here, we introduce our 3DKMI—an open‐source MATLAB package designed for the study of morphology using three‐dimensional (3D) Krawtchouk moment invariants. The volumetric features derived from the 3D images remain stable under translation, scaling and rotation and, for an image of size 128 × 128 × 128 can be computed in less than 0.1 s.We applied our package as a case study on a collection of 300 X‐ray computed tomography scans of planktonic foraminifera specimens across five species to (1) assess the invariance of the features under different transformations and (2) analyse morphological differences among species based on the extracted characteristics.We show that 3DKMI has the capacity to efficiently and repeatedly characterize the signatures of individual morphology. In the future, we hope that the 3D feature extraction technique 3DKMI will be widely applied to digital collections to advance research in ecology and evolution. [ABSTRACT FROM AUTHOR]

In this work, we introduce a novel framework, GAN-based Image Processing (GAN-IP), to design and manipulate digital experimental settings that effectively combine computer vision and image processing technology. Using the skills of Generative Adversarial Networks (GANs), GAN-IP creates and complements virtual scenes and affords rich, adaptive surroundings for analysing and creating computationally smart and perceptive algorithms. GAN-IP solves the pressing troubles of variability and absence of data through synthesising realistic pictures and scenarios, enabling simulations of the diverse environments in which computer vision structures have to function. Our approach improves the fidelity and sort of digital scenes and introduces a way to mechanically alter and evoke a pleasant image, enabling more accurate and powerful computer vision and prescient models. Through extensive experimentation, GAN-IP demonstrates remarkable improvements in the performance of computer vision tasks, including object detection, segmentation, and recognition in complicated virtual environments. This research lays the foundation for future studies in this field and provides an adaptive tool for researchers and practitioners to simulate and test superior computer imaginative and prescient image processing technologies. [ABSTRACT FROM AUTHOR]

The creation of intelligent classrooms has been expedited by the rapid advancement of the Internet and computer vision, and intelligent teaching has increased the interactivity and effectiveness of learning. Teachers' teaching and students' classroom learning state ultimately affect the teaching effect. Students' facial expressions during class can reflect emotional changes and the current learning state. The computer camera in the smart classroom collects students' face image data, uses texture-based information, edge-based information, geometric information, and global and local feature extraction to identify and analyze and process the students' facial expressions. Research has shown that the combination of expression recognition and an intelligent teaching classroom can accurately identify and analyze students' emotions and learning status, and can effectively evaluate the teaching effect of the intelligent classroom, which helps to improve teaching quality and learning efficiency. Therefore, applying facial expression recognition in the intelligent teaching classroom has far-reaching significance. [ABSTRACT FROM AUTHOR]

Based on the color feature of the image pixels, color image segmentation assumes that; distinct clusters of homogenous colors in the image correspond to significant objects in the image. Therefore, each cluster designates a group of pixels with comparable color characteristics. The present research work proposes a novel Modified kernel-based Fuzzy C Means Clustering (MKFCMC) method for color image segmentation using three stages: Pre-processing, Feature extraction, and Segmentation. In the pre-processing stage, the input image is filtered using Weiner Filtering model. The next stage is feature extraction in which shape index histogram-based features, improved local gradient pattern-based features, and color features are extracted. Finally, segmentation is done by the Modified Kernel Fuzzy C means (MKFCM) algorithm. In this MKFCM-based segmentation process, the optimal centroid selection is carried out using optimization algorithm named Self Improved Snake Optimization algorithm. Finally, a performance comparison is made between the proposed MKFCMC model and the standard state-of-the-art models in terms of accuracy, specificity, sensitivity, F1-score and other metrics, thereby establishing the superiority of proposed method. [ABSTRACT FROM AUTHOR]

Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified. [ABSTRACT FROM AUTHOR]

Automatic surgical workflow analysis, which aims to recognize the ongoing surgical events in videos, is fundamental for developing context-aware computer-assisted systems. This paper reviews representative surgical workflow recognition algorithms based on deep learning, outlining their merits, limitations, and future research directions. The literature survey was performed on three large bibliographic databases, covering 67 lary sources, which were comparatively analyzed in terms of spatial feature modeling, spatio-temporal feature modeling, input pre-processing, regularization and post-processing algorithms, as well as learning strategies. Then, common public datasets and evaluation metrics for surgical workflow recognition are also described in detail. Finally, we discuss all literature from different perspectives, and point out the challenges, possible solutions and future trends. The need for more diverse and larger datasets, the potential of unsupervised and semi-supervised learning approaches, comprehensive and equitable metrics, establishing complete regulatory and data standards, and interoperability will be key challenges in translating models to clinical operating rooms. And we propose that surgical activity anticipation and employing large language model as training assistant are interesting research directions in surgical workflow analysis. [ABSTRACT FROM AUTHOR]

Computer vision (CV) systems are crucial in various fields, such as manufacturing. However, the general feedback from small and medium‐sized enterprises (SMEs) in manufacturing indicates that implementing CV systems are out of reach due to their high costs, the need for expertise, and the complexities of Machine Learning (ML). To demonstrate the feasibility and applicability for SMEs, we present a cost‐effective portable CV‐based demonstrator for SMEs in manufacturing. Incorporating low‐cost hardware components, the system integrates open‐source software and employs Transfer Learning to adapt pre‐existing ML models. We present two illustrative use cases with fault detection and inventory management based on plastic brick datasets including various colors and shapes to demonstrate the system's effectiveness. Bringing this into a showable demonstrator, all components are integrated into a portable suitcase as a plug and play demonstration. We showcase the portable demonstrator at German television and four different industrial fairs, leveraging these dynamic platforms for direct interaction with SMEs and stakeholders. Firsthand insights and feedback from SMEs regarding our demonstration and their challenges, as well as opportunities for CV in manufacturing were received and summarized in this research. [ABSTRACT FROM AUTHOR]

In recent decades, liver tumors have been considered as the leading and most serious causes of death worldwide. One of the most well-known Computer Vision (CV) is medical imaging (MI) which supports healthcare professionals in the early detection and diagnosis of liver tumors. To diagnose patients, professionals read hundreds of images using manual or Computed Tomography (CT). Consequently, a completely automated technique that makes use of CT scans is a common and extensively utilized imaging modality which requires an early diagnosis and identification of tumor. To overcome the above-stated issues, Mayfly Optimization (MFO) and Fully Convolutional Networks (FCN) are proposed for the classification of liver tumors. Image resizing approach is used for preprocessing and Adding Inception Module-UNet (AIM-UNet) is used for image segmentation. The suggested MFO algorithm is used for feature selection which takes the input from the extracted features using ResNet50. FCN is used as a classifier to classify liver tumors. In comparison to the existing techniques like EfficientNetB0 and InceptionV3, the proposed FCN achieves commendable accuracies of 99.5% and 99.98%, respectively, on the LiTS and CHAOS datasets. [ABSTRACT FROM AUTHOR]

ArUco markers have gained significant attention for their utility in indoor navigation assistance, enhancing the accuracy and efficiency of navigation systems. However, traditional detection methods often need help with computational intensity and effectiveness under varying lighting conditions, necessitating the development of advanced detection techniques. This study investigates deep learning-powered ArUco marker detection using the MobileViTv2 model. MobileViTv2, a lightweight hybrid network combining Convolutional Neural Networks (CNN) and Vision Transformer (ViT) elements, is designed to balance accuracy and computational efficiency. The model was trained and fine-tuned using a transfer learning approach, with comparisons to prominent CNN architectures such as ResNet50, MobileNetV2, DenseNet121, VGG16, InceptionV3, and NASNetMobile. The MobileViTv2 model demonstrated superior performance in detecting and classifying ArUco markers for indoor navigation. The model attained a peak accuracy of 100% using the optimizer developed by Adam with a learning rate of 0.001, surpassing the performance of all models examined in the study. The results showed excellent precision, recall, and F1 scores, confirming the model's robustness and reliability. The total number of trainable parameters was slim, making the model more efficient and faster in processing, which is ideal for real-time applications. The modified MobileViTv2 model significantly enhances the accuracy and efficiency of ArUco marker detection in indoor navigation systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When a ship–bridge collision occurs, prompt assessment of substructure damage is crucial. This study presents a novel approach for ship–bridge collision damage identification, addressing challenges inherent in traditional monitoring systems. The method overcomes issues such as complex installation, low efficiency, and high costs through a unique combination of the unscented Kalman filter (UKF) and computer vision technique. The approach exerts the structural equation of motion to derive a multirate UKF in the impact process, thereby identifying the stiffness of structures. Displacement and acceleration are fused to enhance the sampling rate of vision-measured displacement. Firstly, it monitors low sampling rate displacements on piers using computer vision, complemented by high-rate accelerometer data at the collision point. Secondly, displacement and acceleration data are integrated using a multirate UKF, addressing the challenge of image storage pressure associated with vision-based measurements. Finally, validation using finite-element and experimental models confirms the effectiveness of the approach in identifying substructure stiffness and recovering lost vibration characteristics. In experiment validation, the influence of computer vision algorithms and camera shooting distance on displacement monitoring and stiffness identification is also discussed separately. This approach provides a cost-effective and efficient solution for ship–bridge collision damage identification, contributing to advancements in the field of ship–bridge collision monitoring. [ABSTRACT FROM AUTHOR]

In recent decades, the advancement of deep learning algorithms and their effectiveness in saliency detection has garnered significant attention in research. Among these methods, U Network ( U-Net ) is widely used in computer vision and image processing. However, most previous deep learning-based saliency detection methods have focused on the accuracy of salient regions, often overlooking the quality of boundaries, especially fine boundaries. To address this gap, we developed a method to detect boundaries effectively. This method comprises two modules: prediction and residual refinement, based on U-Net structure. The refinement module improves the mask predicted by the prediction module. Additionally, to boost the refinement of the saliency map, a channel attention module is integrated. This module has a significant impact on our proposed method. The channel attention module is implemented in the refinement module, aiding our network in obtaining a more accurate estimation by focusing on the crucial and informative regions of the image. To evaluate the developed method, five well-known saliency detection datasets are employed. The proposed method consistently outperforms the baseline method across all five datasets, demonstrating improved performance. [ABSTRACT FROM AUTHOR]

This research paper addresses an important challenge in book digitization, i.e., accurately predicting frames where text visibility is optimal. Existing models often suffer from high computational complexity, resulting in inefficiencies in automation and accuracy. In contrast, our proposed models offer a solution with lower complexity and higher accuracy. Leveraging a diverse dataset of book flipping videos, we introduce three novel models: the Regular CNN LeNet-5 Model, the Custom LSTM Model, and the 3D CNN Model. Evaluation reveals that our 3D CNN Model achieves an accuracy score of 99.01%, with 377,921 parameters. These models demonstrate a significant increase in efficiency in terms of accuracy metric with significantly less number of parametrers. Thereby the proposed approach enhances the process of identifying frames of interest. Our findings highlight the transformative potential of these models in streamlining book digitization workflows and improving accessibility to digitized textual content. This study contributes valuable insights at the intersection of computer vision, machine learning, and digitization efforts, offering a promising avenue for enhancing the usability of digitized textual resources. [ABSTRACT FROM AUTHOR]

Feature‐based image matching is a critical technique in photogrammetry and computer vision. Recently, various advanced image matching methods have been proposed. The effectiveness of these methods is significantly challenged in the case of multisource planetary images which often have to deal with unique surface morphologies, observed by different sensors and under different illumination and viewing conditions. This study investigates and evaluates the performances of 13 feature detectors across diverse images from Moon and Mars, captured by different sensor systems under different radiometric and geometric conditions. Also, the performances of 12 feature descriptors are assessed. A ranking for combinations of detectors and descriptors is determined. The results reveal that phase congruency‐based algorithms achieve favourable performance in both feature detection and description. On the other hand methods based on deep learning may obtain better results if training data of high quality were available. Finally, we summarise the capabilities and limitations of multisource remote sensing image matching methods and provide discussions and prospects for future research. [ABSTRACT FROM AUTHOR]

This research report introduces a learning system designed to detect the object that humans are gazing at, using solely visual feedback. By incorporating face detection, human attention prediction, and online object detection, the system enables the robot to perceive and interpret human gaze accurately, thereby facilitating the establishment of joint attention with human partners. Additionally, a novel dataset collected with the humanoid robot iCub is introduced, comprising more than 22,000 images from ten participants gazing at different annotated objects. This dataset serves as a benchmark for human gaze estimation in table-top human-robot interaction (HRI) contexts. In this work, we use it to assess the proposed pipeline's performance and examine each component's effectiveness. Furthermore, the developed system is deployed on the iCub and showcases its functionality. The results demonstrate the potential of the proposed approach as a first step to enhancing social awareness and responsiveness in social robotics. This advancement can enhance assistance and support in collaborative scenarios, promoting more efficient human-robot collaborations. [ABSTRACT FROM AUTHOR]