期刊信息

Image and Vision Computing (IVC)

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影响因子:
5.0
出版商:
Elsevier
ISSN:
0262-8856
浏览:
50321
关注:
40
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征稿

Image and Vision Computing (IVC) is an academic journal published by Elsevier. (ISSN 0262-8856, impact factor 5.0, CCF C).

Aims & Scope Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases. In addition to regular manuscripts, Image and Vision Computing Journal solicits manuscripts for the Opinions Column, aimed at initiating a free forum for vision researchers to express their opinions on past, current, or future successes and challenges in research and the community. An opinion paper should be succinct and focused on a particular topic. Addressing multiple related topics is also possible if this helps making the point. While posing questions helps raising awareness about certain issues, ideally, an opinion paper should also suggest a concrete direction how to address the issues. Topics of interest include, but are not limited to: Comments on success and challenges in a (sub-) field of computer vision, Remarks on new frontiers in computer vision Observations on current practices and trends in research, and suggestions for overcoming unsatisfying aspects Observations on current practices and trends in the community regarding, e.g., reviewing process, organizing conferences, how journals are run, and suggestions for overcoming unsatisfying aspects Reviews of early seminal work that may have fallen out of fashion Summaries of the evolution of one's line of research Recommendations for educating new generations of vision researchers. The format of an opinion paper should comply with the existing formatting guidelines for the Image and Vision Computing Journal submissions, and should not exceed 2 pages. Months of publication: January/February, March, April, May, June, July/August, September, October, November and December.
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特刊

特刊：Visual Perception enabling Autonomous Navigation (VP-NAV) 截稿日期: 2026-12-31 Autonomous navigation is rapidly transforming a wide spectrum of sectors, including intelligent transportation systems, environmental monitoring, smart agriculture, service robotics, and industrial automation. A foundational enabler of this transformation is visual perception, which equips machines with the ability to sense, interpret, and adapt to their environments using visual data. Whether mounted on vehicles, robots, or embedded within the infrastructure, visual perception systems are critical for achieving situational awareness, safe interaction, and autonomous decision-making. This Special Issue aims to spotlight the latest advances in visual perception technologies that empower autonomous navigation across diverse application domains. It invites contributions that explore how onboard visual systems (e.g., cameras and vision sensors on connected autonomous vehicles, mobile robots, drones) and infrastructure-based perception systems (e.g., smart intersections, agricultural field monitoring stations, factory floor cameras) collaboratively contribute to the autonomy and intelligence of machines operating in dynamic, complex and often unstructured environments. Key challenges addressed in this issue include robust perception under real-world variability, sensor fusion for enhanced scene understanding, edge processing for real-time applications, and coordination between distributed perception sources. By unifying research across traditionally separate fields, this Special Issue seeks to foster interdisciplinary dialogue and innovation at the intersection of computer vision, robotics and autonomous systems. Guest editors: Antonio Greco (Executive Guest Editor), Tenure-Track Assistant Professor Dept. of Information and Electrical Engineering and Applied Mathematics (DIEM) Laboratory of Intelligent Machines for the Analysis of Videos, Images and Audio (MIVIA) University of Salerno, Via Giovanni Paolo II, 132 84084 - Fisciano (SA), Italy E-mail: [email protected] Modesto Castrillón-Santana, Full Professor Edificio Departamental de Informática y Matemáticas Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain E-mail: [email protected] Giovanna Castellano, Full Professor Department of Computer Science, Campus "Ernesto Quagliariello" Computational Intelligence Lab (CILab) University of Bari Aldo Moro, Via E. Orabona, 4, 70125 Bari, Italy E-mail: [email protected] Bruno Vento, PhD Student Dept. of Electrical Engineering and Information Technology (DIETI) Pattern Analysis and Intelligent Computation for mUltimedia Systems LAB University of Naples, Via Claudio, 21 - 80135 Naples, Italy E-mail: [email protected] Special issue information: We invite contributions that address key challenges in visual perception for autonomous navigation, including robust scene understanding, sensor fusion, real-time processing, learning-based approaches, and collaboration between distributed sensing systems. We expect submissions that present novel methodologies, strong experimental validation, or insightful system-level contributions with relevance to real-world applications in transportation, service robotics, smart agriculture, and industrial automation. Topics include, but are not limited to: ● Visual perception systems for autonomous vehicles, mobile robots, drones, and automated machinery ● Object detection, tracking, classification, and scene understanding for navigation ● Multi-modal sensor fusion (e.g., combining vision with LiDAR, radar, IMU) for autonomous navigation ● Learning-based perception systems (e.g., deep learning, foundation models) for autonomous navigation ● Distributed visual sensing in smart cities, factories, and agricultural fields ● Vision-based SLAM, mapping, and localization in dynamic or unstructured environments ● Real-time perception and edge computing for onboard and distributed systems ● Collaborative and V2X-enabled perception between vehicles, robots, and infrastructure ● Visual perception for service and assistive robotics in indoor and semi-structured environments ● Robust visual perception under challenging conditions (e.g., poor lighting, adverse weather, occlusions) ● Data generation, simulation environments, and benchmark datasets for training and evaluation Manuscript submission information: Open for Submission: from 01-Sep-2026 to 31-Dec-2026 Submission Site: https://submit.elsevier.com/IMAVIS Article Type Name: "VSI: VP-NAV" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact the Guest Editor team. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Image and Vision Computing - ISSN 0262-8856 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: IMAVIS | Image and Vision Computing | Journal | ScienceDirect.com by Elsevier Keywords: Visual perception; autonomous navigation https://www.sciencedirect.com/special-issue/335697/visual-perception-enabling-autonomous-navigation-vp-nav
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特刊：Complex Environment Vision 截稿日期: 2027-02-15 Real-world computer vision systems increasingly operate in environments that are far from the controlled conditions of traditional benchmarks. Dynamic illumination, adverse weather, environmental scattering, non-Lambertian materials, large-scale geometry, underwater scenarios, endoscopic imaging, and incomplete or degraded observations pose significant challenges to robust scene understanding. These factors are amplified in safety-critical and mission-driven applications such as autonomous navigation, industrial inspection, environmental monitoring, medical imaging, and cultural heritage preservation. This Special Issue will highlight recent advances in computer vision methods explicitly developed for operation in complex and challenging environments. The focus will be on approaches that integrate physical modeling with data-driven learning, enabling systems to remain robust under unpredictable conditions such as extreme lighting variation, strong scattering, non-uniform reflectance, or severe sensory degradation. By combining the interpretability and generalization potential of physical principles with the adaptability of learning-based models, these methods can better handle the violations of ideal assumptions common in real-world scenarios. Beyond technical innovations, this issue seeks to spark community discussion on evaluation and benchmarking practices. Key questions include: How can we design datasets and metrics that reflect operational challenges rather than idealized laboratory setups? What strategies ensure that vision models maintain robustness when exposed to environmental variations and sensor imperfections? By bringing together perspectives from both theory and application, this Special Issue aims to advance the development of vision systems capable of reliable and scalable performance in the complex, unstructured environments of the real world. Guest editors: Dr. Yakun Ju (Executive Guest Editor), University of Leicester, Leicester, UK; Email: [email protected], [email protected] Dr. Long Ma, The Chinese University of Hong Kong, Hong Kong SAR, China; Email: [email protected] Dr. Giuseppe Valenzise, Université Paris-Saclay, Gif-sur-Yvette, France; Email: [email protected] Special issue information: The list of possible topics includes, but is not limited to: • Vision algorithms for complex and challenging environments • Domain adaptation, generalization, and robustness to environmental variation • Multi-modal and cross-spectral fusion in real-world perception • Simulation-to-real and data-efficient learning for rare scenarios • Evaluation protocols and benchmarks reflecting real-world complexity • Autonomous navigation and robotics in unstructured environments • Medical imaging in challenging acquisition settings • Adverse underwater vision Manuscript submission information: Open for Submission: from 15-Aug-2026 to 15-Feb-2027 Submission Site: https://submit.elsevier.com/IMAVIS Article Type Name: "VSI: Complex Env Vision" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact the Guest Editor team. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Image and Vision Computing - ISSN 0262-8856 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: IMAVIS | Image and Vision Computing | Journal | ScienceDirect.com by Elsevier Keywords: Complex Environment Vision; Challenging Scene Understanding; Robust Image Analysis; Real-world Computer Vision https://www.sciencedirect.com/special-issue/335145/complex-environment-vision
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特刊：3D Mesh Processing and Generation in the Age of Deep Learning 截稿日期: 2027-03-15 Three-dimensional (3D) meshes continue to serve as a core representation in visual computing, supporting a wide range of applications in scientific visualization, computer graphics, computer vision, virtual and augmented reality, and digital twin technologies. The rapid progress of deep learning has transformed how 3D data is processed, analyzed, and generated. Modern neural networks can now capture complex geometric priors, generate high-fidelity shapes, repair and simplify meshes, and enable intelligent 3D content creation with unprecedented realism and scalability. This special issue of the Image and Vision Computing seeks to highlight the latest research at the intersection of 3D mesh processing and deep learning. It welcomes original, high-quality contributions that introduce new theories, propose innovative methods, or demonstrate practical applications revealing the transformative potential of deep learning for advancing 3D geometry and mesh understanding. Guest editors: Prof. Olivier Lézoray (Executive Guest Editor) University of Caen Normandy, Caen, France Email: [email protected] Prof. Yu-Kun Lai Cardiff University, Wales, UK Email: [email protected] Assist. Prof. Lei Li University of Virginia, Virginia, USA Email: [email protected] Assist. Prof. Yiyi Liao Zhejiang University, Hangzhou, China Email: [email protected] Special issue information: We are soliciting original contributions that address a wide range of theoretical and practical issues for 3D meshes, including, but not limited to: Neural representations and implicit functions for 3D geometry Deep 3D mesh generation, completion, and reconstruction Learning-based mesh simplification, denoising, and remeshing Mesh segmentation, classification, and correspondence learning Neural mesh editing, deformation, and animation Large-scale datasets and benchmarks for 3D mesh deep learning Generative models (GANs, diffusion models, auto-regressive models) for 3D mesh synthesis Differentiable rendering and inverse graphics for mesh learning Topology-aware neural network architectures Multi-modal 3D mesh learning from text, images, or video Mesh quality metrics, fairness, and bias in 3D data generation Applications in visualization, simulation, AR/VR, and industrial design Manuscript submission information: Open for Submission: from 31-Jul-2026 to 15-Mar-2027 Submission Site: https://submit.elsevier.com/IMAVIS Article Type Name: "VSI: 3D Mesh Processing & DL" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact the Guest Editor team. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Image and Vision Computing - ISSN 0262-8856 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: IMAVIS | Image and Vision Computing | Journal | ScienceDirect.com by Elsevier Keywords: 3D Mesh, Deep Learning, Neural representations https://www.sciencedirect.com/special-issue/335679/3d-mesh-processing-and-generation-in-the-age-of-deep-learning
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特刊：Multimodal Vision for Medical Imaging: Resilience, Robustness, Fairness, and Interpretability 截稿日期: 2027-03-15 Recent advances in deep learning and computer vision have significantly improved the analysis of medical images across a wide range of tasks, including classification, segmentation, detection, and prognosis. In clinical practice, however, decision-making often relies on multiple imaging modalities or multi-parametric acquisitions of the same modality, each providing complementary information about anatomical structures and pathological conditions. Multimodal learning has therefore emerged as a key paradigm in medical imaging, enabling the integration of heterogeneous sources of information. Despite these advances, most existing approaches focus primarily on architectural design and performance improvements under controlled experimental settings, often overlooking the challenges posed by real-world clinical environments. In practice, multimodal medical imaging data are frequently incomplete, heterogeneous, misaligned, or affected by acquisition variability, raising critical issues related to robustness, resilience, fairness, and interpretability. This Special Issue focuses on multimodal computer vision methods for medical imaging, adopting a vision-centric perspective in which medical images constitute the primary source of information, possibly complemented by additional data modalities. The aim is to promote the development of models that are not only accurate, but also robust to perturbations, resilient to missing or degraded inputs, fair across patient populations and acquisition settings, and interpretable in terms of cross-modal interactions. By bringing together contributions addressing these complementary challenges, this Special Issue seeks to advance the state of the art in multimodal vision for medical imaging and to foster the development of reliable systems suitable for real-world clinical deployment. Guest editors: Dr. Michela Gravina (Executive Guest Editor), University of Naples Federico II, Naples, Italy; Email: [email protected]​ Prof. Carlo Sansone, University of Naples Federico II, Naples, Italy; Email: [email protected] Prof. Angel Garcia-Pedrero, Universidad Politécnica de Madrid, Madrid, Spain; Email: [email protected] Special issue information: This Special Issue focuses on multimodal computer vision methods for medical imaging, adopting a vision-centric perspective in which medical images constitute the primary source of information, possibly complemented by additional data modalities. The goal is to promote the development of models that are not only accurate, but also robust, resilient, fair, and interpretable, enabling reliable deployment in real-world clinical environments. The Special Issue will particularly address four fundamental challenges: Robustness: ensuring stable performance under noise, artifacts, acquisition variability, and domain shifts, which are common in medical imaging due to differences in scanners, protocols, and clinical settings. Resilience: enabling models to operate under incomplete or degraded conditions, such as missing modalities, partially available data, or corrupted inputs, which frequently occur in real clinical workflows. Fairness: understanding and mitigating biases in multimodal systems arising from demographic imbalance, acquisition variability, modality availability, and representation learning, to ensure equitable model performance. Interpretability: developing methods to explain model predictions, particularly in terms of how different modalities contribute to the final decision, supporting clinical trust and adoption. Topics of interest include, but are not limited to: Multimodal fusion of medical imaging modalities Learning with missing or incomplete modalities Cross-modal alignment and shared representation learning Robustness to noise, artifacts, and domain shifts Bias analysis and fairness in multimodal imaging systems Interpretability and explainability of multimodal models Resilient architectures for real-world clinical deployment Evaluation protocols and benchmarks for multimodal vision Domain generalization and adaptation in multimodal settings Manuscript submission information: Open for Submission: from 15-Sep-2026 to 15-Mar-2027 Submission Site: https://submit.elsevier.com/IMAVIS Article Type Name: "VSI: Multimodal Medical Vision" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact the Guest Editor team. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Image and Vision Computing - ISSN 0262-8856 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: IMAVIS | Image and Vision Computing | Journal | ScienceDirect.com by Elsevier Keywords: Medical Imaging; Multimodal Vision; Resilience; Fairness; Interpretability https://www.sciencedirect.com/special-issue/335192/multimodal-vision-for-medical-imaging-resilience-robustness-fairness-and-interpretability
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