23 papers · ranked by Valyu relevance
Yasunari Matsuzaka, Masayuki Iyoda
This review summarizes the current advances, applications, and research prospects of computer vision in advancing medical imaging. Computer vision in healthcare has revolutionized medical practice by increasing diagnostic accuracy, improving patient care, and increasing operational efficiency. Likewise, deep learning…
Jarosław Panasiuk, Tamás Haidegger
Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process…
Wendy Flores-Fuentes, Oleg Sergiyenko, Julio C. Rodríguez-Quiñonez, Jesús E. Miranda-Vega
Information theory is the foundation for computer vision and image processing across diverse applications. It constitutes the application of sophisticated mathematical principles to electrical engineering development for advanced research in vision systems and machine learning. Within this field of study, 3D visual…
Valentyna Loboichenko, Grzegorz Wilk-Jakubowski, Lukasz Pawlik, Jacek Lukasz Wilk-Jakubowski + 8 more
This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from the…
Md Nafiul Islam, J. Lannett Edwards, Robert Burns, Hairong Qi + 2 more
Highlights What are the main findings?1. Computer vision technologies (e.g., YOLO, CNNs, transformers, and 3D imaging) have achieved high accuracy (>90% in many cases) for key cattle health monitoring tasks, including BCS, lameness, weight, estrus, breathing, and behavior detection. 2. A clear evolution is evident from…
Matteo Dunnhofer, Jean de dieu Uwisengeyimana, Kohitij Kar
How does motion contribute to robust object perception when appearance cues are unreliable? In natural scenes, camouflage, clutter, and occlusion can obscure object boundaries in static images, yet humans often resolve these ambiguities once objects move. Here we ask whether modern artificial vision systems capture…
Junchao Zhu, Ruining Deng, Junlin Guo, Tianyuan Yao + 13 more
Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation…
Jonathan Adams, Luca Serrière, Maximilian Jonathan Kothen, Maria Barbara Smorczewska + 2 more
In amodal completion observers perceive complete objects despite partial occlusion. When two object parts are divided by an occluder, completion can result in perceiving one or two objects. This phenomenon involves both lower-level cues (e.g., symmetry, contour continuity) and higher-level cues (e.g., prior knowledge).…
Władysław Skarbek, Michał Salomonowicz, Michał Król
Estimating the position and orientation of a camera with respect to an observed scene is one of the central problems in computer vision, particularly in the context of camera calibration and multi-sensor systems. This paper addresses the planar Perspective–n–Point problem, with special emphasis on the initial…
Kundai Farai Sachikonye
This work presents a novel thermodynamic computer vision framework for biological image analysis that reformulates traditional image processing problems within the context of statistical mechanics and molecular dynamics. The approach treats image pixels as information-carrying entities analogous to gas molecules, where…
Yifan Luo, Niklas Müller, H. Steven Scholte
Deep convolutional neural networks match human accuracy on standard object recognition tasks but fail to recognize familiar objects from novel view-points. Humans, however, develop viewpoint-invariant recognition at an early age through diverse visual experience. This gap in visual experience may explain why models…
Kasjan Śmigielski, Natalia Piórkowska
Automated behavioral tracking is increasingly used in biological and biomedical research; however, robustness across heterogeneous imaging conditions remains a major challenge. Domain shifts caused by changes in illumination, contrast, or acquisition setup can substantially degrade the performance of computer vision…
Rakhi Issrani, Hafiz Muhammad Zeeshan, Abid Iqbal, Shahzad Ahmad + 3 more
Computer vision, enabled by machine learning and image processing techniques, plays a crucial role in healthcare by facilitating the analysis of medical images such as magnetic resonance imaging, computed tomography and X-rays. It supports disease diagnosis, treatment planning, telemedicine and remote patient…
Alexa R. Tartaglini, Michael A. Lepori
Object binding is a foundational process in visual cognition, during which low-level perceptual features are joined into object representations. Binding has been considered a fundamental challenge for neural networks, and a major milestone on the way to artificial models with flexible visual intelligence. Recently…
Peilin Tao, Hainan Cui, Mengqi Rong, Shuhan Shen
Global Structure-from-Motion (SfM) offers advantages over incremental methods in terms of efficiency and error distribution. However, the task of translation averaging remains challenging. Many existing methods rely solely on relative translations or feature tracks, which either degrade under collinear camera motion or…
Palma-Amestoy, Rodrigo, Provenzi, Edoardo + 3 more
Basic phenomenology of human color vision has been widely taken as an inspiration to devise explicit color correction algorithms. The behavior of these models in terms of significative image features (such as contrast and dispersion) can be difficult to characterize. To cope with this, we propose to use a variational…
Authors not listed
Infrared (IR) spectroscopy provides rich structural information but interpreting spectra at scale remains challenging. Here we introduce j-IR-vis, a vision-based neural model that learns chemically interpretable representations directly from IR spectra for functional-group prediction and downstream molecular…
Shunkun Liang, Banglei Guan, Bin Li, Qifeng Yu + 1 more
Multi-camera systems offer rich observation capabilities for visual navigation and 3D scene reconstruction; however, the resulting feature redundancy often compromises computational efficiency. This challenge is particularly pronounced during bundle adjustment, where the non-linear optimization of both system poses and…
Pierpaolo Serio, Giulio Pisaneschi, Andrea Dan Ryals, Vincenzo Infantino + 3 more
—This work presents a systematic investigation into how alternative LiDAR–to–image projections affect metric place recognition when coupled with a state-of-the-art vision foundation model. We introduce a modular retrieval pipeline that controls for backbone, aggregation, and evaluation protocol, thereby isolating the…
Authors not listed
Large Language Models have demonstrated impressive capabilities in natural language understanding and processing. However, as AI and LLMs continue to evolve, their ability to accurately and efficiently interpret data from scientific figures and plots remains obscure. In this study, we test and evaluate the ability of…
Xinrui Li, Cai, Qi, Yuanxin Wu
—Structure from Motion (SfM) is a critical task in computer vision, aiming to recover the 3D scene structure and camera motion from a sequence of 2D images. The recent pose-only imaging geometry decouples 3D coordinates from camera poses and demonstrates significantly better SfM performance through pose adjustment.…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
For decades, molecular visualization software has been fundamental to education and research in chemistry, structural biology, and materials science. These tools have enabled the inspection of structures, dynamics, and interactions, yet their reliance on two-dimensional (2D) interfaces imposes persistent limitations.…