Hybrid adaptive method for lane detection of degraded road surface condition by Khaled H. Almotairi

“…The proposed method involves a set of preprocessing methods for obtaining the candidate lane borders from an image in any degraded state. Subsequently, numerical features about the boundaries of the candidate lanes are extracted and used by a model of the k-nearest neighbor algorithm and the Gaussian process for final lane discovery. …”
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THEORETICAL AND METHODOLOGICAL ASPECTS OF USING INFORMATION ANDCOGNITIVE TECHNOLOGIES IN THE TRAINING OF TRANSPORT SPECIALISTS by Lavrentieva Olena, Krupskyi Oleksandr

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THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DEVELOPMENT OF PREDICTIVE COMPETENCE IN MODERN SPECIALISTS by Viacheslav Osadchyi, Maksym Pavlenko, Liliia Pavlenko, Oleksii Sysoiev, Vladyslav Kruglyk

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Point'n Move: Interactive scene object manipulation on Gaussian splatting radiance fields by Jiajun Huang, Hongchuan Yu, Jianjun Zhang, Hammadi Nait‐Charif

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Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation by Swagata Kundu, Dimitrios Toumpanakis, Johan Wikstrom, Robin Strand, Ashis Kumar Dhara

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Real-time motion detection using dynamic mode decomposition by Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger

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Analyzing the applicability of psychometric QoE modeling for projection-based point cloud video quality assessment by Sam Van Damme, Jeroen van der Hooft, Filip De Turck, Maria Torres Vega

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Context influence on speech perception: evidence for acoustic-level mechanism across the voice onset time continuum by Shun Liu, Xiqin Liu

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Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization by Olga Ilina, Maxim Tereshonok, Vadim Ziyadinov

“…In this manuscript, we propose a method which helps to increase the robustness of neural network systems to the input adversarial images. The proposed method consists of a Deep Convolutional Neural Network to reconstruct a benign image from the adversarial one; a Calculating Maximum Error block to highlight the mismatches between input and reconstructed images; a Localizing Anomalous Fragments block to extract the anomalous regions using the Isolation Forest algorithm from histograms of images’ fragments; and a Clustering and Processing block to group and evaluate the extracted anomalous regions. …”
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Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach by Reshma Ahmed Swarna, Muhammad Minoar Hossain, Mst. Rokeya Khatun, Mohammad Motiur Rahman, Arslan Munir

“…Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. …”
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Unsupervised Class Generation to Expand Semantic Segmentation Datasets by Javier Montalvo, Álvaro García-Martín, Pablo Carballeira, Juan C. SanMiguel

“…Semantic segmentation is a computer vision task where classification is performed at the pixel level. Due to this, the process of labeling images for semantic segmentation is time-consuming and expensive. …”
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A Novel Method to Compute the Contact Surface Area Between an Organ and Cancer Tissue by Alessandra Bulanti, Alessandro Carfì, Paolo Traverso, Carlo Terrone, Fulvio Mastrogiovanni

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Pumping up your predictive power for cognitive state detection with the proper GAINS by Victoria Ribeiro Rodrigues, Jeremy R. Prieto, Szilard L. Beres, Chad Stephens, Christopher Myers, Nicholas J. Napoli

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Benchmarking of Multispectral Pansharpening: Reproducibility, Assessment, and Meta-Analysis by Luciano Alparone, Andrea Garzelli

“…The term pansharpening denotes the process by which the geometric resolution of a multiband image is increased by means of a co-registered broadband panchromatic observation of the same scene having greater spatial resolution. …”
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Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals by Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas

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Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces by Kacoutchy Jean Ayikpa, Abou Bakary Ballo, Diarra Mamadou, Pierre Gouton

“…With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. …”
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Approaching maximum resolution in structured illumination microscopy via accurate noise modeling by Ayush Saurabh, Peter T. Brown, J. Shepard Bryan IV, Zachary R. Fox, Rory Kruithoff, Cristopher Thompson, Comert Kural, Douglas P. Shepherd, Steve Pressé

“…Abstract Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. …”
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer by Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong

“…Radiomics features were extracted from venous-phase CT images. After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). …”
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High-Precision Chip Detection Using YOLO-Based Methods by Ruofei Liu, Junjiang Zhu

“…The proposed framework achieves precision, recall, and mAP@0.5 values of 97.04%, 96.38%, and 95.56%, respectively, in image-based detection tasks. In video-based experiments, the proposed video-level post-processing algorithm combined with GM-YOLOv11-DNMS achieves crack–debris counting accuracy of 90.14%. …”
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Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery by Hsing-Hao Ho, Huai-Che Yang, Wen-Xiang Yang, Cheng-Chia Lee, Hsiu-Mei Wu, I-Chun Lai, Ching-Jen Chen, Syu-Jyun Peng

“…Methods We trained a Mask Region-based Convolutional Neural Network (Mask R-CNN) as an alternative to manual pre-processing in designating regions of interest. We also applied transfer learning to the DeepMedic deep learning model to facilitate the automatic segmentation and quantification of AVM edema regions in T2w images. …”
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