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Although data-driven spatial template models provide a practical and cognitively motivated mechanism for characterizing spatial term meaning, the influence of perceptual rather than solely geometric and functional properties has yet to be systematically investigated. In the light of this, in this paper, we investigate the effects of the perceptual phenomenon of object occlusion on the semantics of projective terms. We did this by conducting a study to test whether object occlusion had a noticeable effect on the acceptance values assigned to projective terms with respect to a 2.5-dimensional visual stimulus. Based on the data collected, a regression model was constructed and presented. Subsequent analysis showed that the regression model that included the occlusion factor outperformed an adaptation of Regier & Carlson's well-regarded AVS model for that same spatial configuration.
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http://dx.doi.org/10.1007/s10339-010-0380-x | DOI Listing |
Front Plant Sci
September 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Accurate identification of cherry maturity and precise detection of harvestable cherry contours are essential for the development of cherry-picking robots. However, occlusion, lighting variation, and blurriness in natural orchard environments present significant challenges for real-time semantic segmentation.
Methods: To address these issues, we propose a machine vision approach based on the PIDNet real-time semantic segmentation framework.
F1000Res
September 2025
System Architecture Team (EAS), Engineering Research Laboratory (LRI),, National High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, Morocco, CASABLANCA, Morocco.
Background: UAV-based power line inspections offer a safer, more efficient alternative to traditional methods, but insulator detection presents key challenges: multiscale object detection and intra-class variance. Insulators vary in size due to UAV altitude and perspective changes, while their visual similarities across types (e.g.
View Article and Find Full Text PDFJ Imaging
August 2025
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Data Science, Minjiang University, Fuzhou 350108, China.
Accurate semantic segmentation of indoor architectural elements, such as staircases and elevators, is critical for safe and efficient robotic navigation, particularly in complex multi-floor environments. Traditional fusion methods struggle with occlusions, reflections, and low-contrast regions. In this paper, we propose a novel feature fusion module, Skip-Connection Fusion (SCF), that dynamically integrates RGB (Red, Green, Blue) and depth features through an adaptive weighting mechanism and skip-connection integration.
View Article and Find Full Text PDFFront Neurorobot
July 2025
Cognitive Systems Lab, University of Bremen, Bremen, Germany.
Introduction: Accurate vehicle analysis from aerial imagery has become increasingly vital for emerging technologies and public service applications such as intelligent traffic management, urban planning, autonomous navigation, and military surveillance. However, analyzing UAV-captured video poses several inherent challenges, such as the small size of target vehicles, occlusions, cluttered urban backgrounds, motion blur, and fluctuating lighting conditions which hinder the accuracy and consistency of conventional perception systems. To address these complexities, our research proposes a fully end-to-end deep learning-driven perception pipeline specifically optimized for UAV-based traffic monitoring.
View Article and Find Full Text PDFSensors (Basel)
July 2025
Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning.
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