|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
XXII CIITI 2024, Congreso Internacional en Innovación Tecnológica Informática
July 16th, 2024 
Daniela Lopez de Luise 
Lyon-France : IEEE 6th International Conference on Image Processing Applications and Systems: Call for Submission
July 16th, 2024 Daniela Lopez de Luise
Call for Submission: IPAS’6
Deadline: 30 July 2024
6th IEEE International Image Processing, Applications and Systems Conference
January 9-11 2025, Lyon, France
Dear Colleagues & Researchers,
Image Processing Application in modern Systems makes them smart, interactive integrating features going sometimes beyond human natural intelligence. The international Image Processing Applications and Systems conference aims at grouping from all over the world challenging researchers, academicians, and practitioners in image processing theory, tools and applications, for presenting their research achievements and discussing their main recent issues. The conference will also focus on highlighting new applications issues of image processing in all our environment fields to make more research added values and win the different technological challenges.
The conference is devoted to image and signal processing, computer vision and all their fields of applications. High quality original papers are welcome in all these areas of research. Accepted papers will be submitted for publication in IEEE Xplore.
The Sixth IEEE international conference on Image Processing Applications and Systems is technically sponsored by:
IEEE Region 08 – Europe, Middle East, Africa
IEEE France Section
IEEE France Section SP Chapter
IEEE Tunisia Section
IEEE Tunisia Section SP Chapter
IMPORTANT DATES
Paper Submission: July 30, 2024
Paper Notification: August 30, 2024
Camera ready paper submission: September 10, 2024
Author registration: October 10 2024
MAIN SCOPE
Main topics contain, but not limited to:
· Image Processing Theory and Methods
· Image and Video Processing Theory
· Image and video analysis and interpretation
· Real Time Image Processing
· Categorization and Indexing
· Content Based Image Retrieval
· Low level image Processing & Image Segmentation
· Large Scale Methods Motion and Tracking
· Human Focused Analysis
· 3D Computer Vision
· Vision for Robotics
· Computer Vision for Virtual and Augmented reality
· Ultrasound, mammograms, Magnetic Resonance Imaging, and multimodal medical imaging
· Biologically Inspired Computer Vision and Image Processing
· GPU-based Image Processing and Computer Vision
· Computer Vision for tourism applications
· Computer Vision and Image Processing for cultural heritage applications
· Speech Processing
· Vision for Web Applications
· Underwater acoustic imaging
· Remote Sensing and Signal Processing
· Communication, Networking and Broadcast Technologies
· Computing and Processing applied to sensing the earth, oceans, atmosphere and space, and the processing, interpretation.
· Theory, concepts, and techniques of science and engineering applied to Geoscience.
· Medical Engineering and Healthcare applications
· Medical Image Processing and Computer Aided Diagnosis, Computer Aided Detection.
· Computer Vision and Image Processing for healthcare applications.
· Image processing and Big Data
· Data Selection
· Image Processing for Cyber Security
· Signal Processing for Smart Systems and Sensors
· Hardware Implementation & Co-design
· Possibility Theory and Decision Making Systems
· FPGA Reconfigurable Systems
· Ontology based Image Representation & Processing
· Image Processing and biometric systems
· Multimodal Biometric Systems
· Statistical learning
· Pattern Analysis and Machine Intelligence
· Computer Vision Theory and Deep Learning
· Artificial Intelligence
· Convolutional Neural Networks.
· Operating systems, software systems, and communication protocols;
· Real-time systems and embedded systems;
· Performance, fault tolerance, reliability, security, and testability;
· Case studies and experimental and theoretical evaluations;
· New and important applications and trends in computer vision.
· Affective computing: Sensing & analysis: Algorithms and features for the recognition of affective state from face and body gestures.
· Analysis of text and spoken language for emotion recognition.
· Analysis of prosody and voice quality of affective speech.
· Recognition of auditory and visual effect bursts;
· Innovative studies in Cloud Computing applications.
Kindest Regards,
IPAS’6 organizing Committee
Workshop on Data Analytics in Biomedicine
July 16th, 2024 Daniela Lopez de Luise CALL FOR PAPERS
First Workshop on “Data Analytics in Biomedicine”
(held in conjunction with IEEE DDP2024)
Fourth International Conference on Digital Data Processing
Yeshiva University.
New York, US
30 September – 01 October 2024
https://socio.org.uk/ddp/workshop/
CALL FOR PAPERS
The exponential growth of data generated from various textual sources presents both a challenge and a huge opportunity. A key challenge lies in effectively managing and extracting valuable insights from this vast amount of unstructured and heterogeneous data. To address this issue, advanced data analytics techniques, ranging from data and text mining to semantic network analysis and recent advancements in large language models (LLMs), have become indispensable tools for researchers and practitioners.
This is particularly relevant in the realm of biomedicine, where text mining has shown the ability to enable researchers to uncover hidden patterns, trends, and associations that would otherwise remain buried in the vast amount of health-related textual data, for instance, research articles, clinical reports, and electronic health records (EHRs).
On the other hand, semantic network analysis, which focuses on understanding the structure and dynamics of networks formed by entities and their interconnections derived from text mining processes, can facilitate a deeper understanding of the complex interrelationships within biomedical data. By analyzing properties like centrality, modularity, and community structures, researchers can identify key nodes and critical pathways in biological networks, predict disease associations, and explore the functional organization of biological systems.
The integration of text mining, semantic network analysis, and large language models offers a powerful approach to enhancing the ability to generate new hypotheses and insights and supporting the development of more effective diagnostics, treatments, and interventions.
The workshop represents an opportunity to explore the latest advancements in data analytics and text mining in biomedicine. Attendees will gain insights into developing more interpretable models, handling large-scale biomedical datasets, and implementing scalable solutions for real-world healthcare applications.
Moreover, the workshop is highly relevant because it has the potential to significantly improve the safety, effectiveness, and efficiency of biomedical interventions through advanced data analytics.
TOPIC OF INTEREST
We invite submissions on a wide range of topics, including but not limited to:
Novel techniques and measures for assessing textual data quality and handling data integration.
Advanced text mining techniques for biomedical data
Construction and analysis of semantic networks in biomedicine
Case studies on integrated text mining and semantic network analysis
Applications of LLMs in biomedicine
Data analytics in precision medicine
Text-driven approaches to drug discovery
Interpretable or scalable data analytics approaches
Application of Data Analytics and network science in Narrative Medicine
Computational methods for disease modeling and prediction
Ethical considerations in biomedical data analytics
Multimodal biomedical data analytics
Future trends and challenges in biomedical data analytics
PROGRAM
The workshop will take place on (To Be Announced). The program has yet to be made available. The Venue is Yeshiva University, New York.
PAPER SUBMISSION, REGISTRATION AND PUBLICATION
The submissions should follow the IEEE template.
Please refer to socio.org.uk/ddp/paper-submission/
IMPORTANT DATES
Submission of Papers: August 05, 2024
Review and Notification: August 31, 2024
Camera-ready: Sep. 25, 2024
Workshop Date: Oct. 01, 2024
Post-conference proceedings: Nov. 30, 2024
WORKSHOP ORGANIZERS
Chiara Zucco, University Magna Graecia of Catanzaro, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Marianna Milano, University Magna Graecia of Catanzaro, Italy
PROGRAM COMMITTEE (TO BE CONFIRMED)
Marzia Settino, University of Calabria, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Maria Chiara Martinis, University Magna Graecia of Catanzaro, Italy
Giuseppe Agapito, University Magna Graecia of Catanzaro, Italy
Pietro Cinaglia, University Magna Graecia of Catanzaro, Italy
Ilaria Lazzaro, University Magna Graecia of Catanzaro, Italy
ECCV Workshop: Towards Multimodal Foundational Models for Modelling Visual Cortex
July 16th, 2024 Daniela Lopez de Luise Towards Multimodal Foundational Models for Modelling Visual Cortex
Held in conjunction with the 18th European Conference on Computer Vision ECCV 2024 – Sep 29th – Oct 4th, 2024, Milan (Italy)
IMPORTANT DATES
Paper submission deadline: July 15, 2024 extended to July 22, 2024
Notification to authors: August 10, 2024
Camera-ready deadline: August 20, 2024
CALL FOR PAPERS
Recent advancements in artificial intelligence and computational modeling have sparked a new era in understanding complex systems, particularly in neuroscience. Foundation models, distinguished by their capability to integrate diverse data modalities, have emerged as powerful tools in decoding the complexities of the visual cortex in both human and animal subjects. This workshop aims to explore state-of-the-art techniques and methodologies for developing multimodal foundational models that comprehensively represent the visual cortex.
Topics (but not limited to):
-
Theoretical Frameworks and Computational Approaches: Novel theoretical constructs and computational strategies for modeling the visual cortex using multimodal data.
-
Integration of Diverse Data Sources: Techniques and challenges in integrating and harmonizing heterogeneous data modalities such as fMRI, EEG, in vivo two-photon calcium imaging, fNIRS, and others.
-
Learning Paradigms for Noisy Data: Innovations in learning algorithms and paradigms to effectively handle noisy and incomplete data in modeling brain functions.
-
Applications in Neuroscientific Research: Practical applications of multimodal foundational models in elucidating perception, cognition, and neurodevelopmental or neurodegenerative disorders.
-
Contrastive Learning for Multimodal Brain Data Fusion: Techniques and advancements in leveraging contrastive learning methods to fuse multimodal brain data for enhanced representation and analysis.
-
Self-Supervised Learning for Temporal Brain Dynamics: Approaches utilizing self-supervised learning to capture and model temporal dynamics in brain imaging and physiological data.
-
Unsupervised Learning for Structural and Functional Brain Network Construction: Methods employing unsupervised learning to construct and analyze structural and functional brain networks from multimodal data.
-
Weakly Supervised Learning for Brain Connectivity Analysis: Innovations in weakly supervised learning techniques for analyzing brain connectivity patterns and networks.
-
Foundational Models for Classification and Predictive Modeling: Development and application of foundational models for classification and predictive modeling tasks in neuroscience.
-
Multimodal Brain Image Visualization with Advanced Learning Techniques: Techniques for visualizing multimodal brain images using advanced learning and visualization methods to aid in data interpretation.
-
Ethical Implications: Ethical considerations in the creation, use, and implications of foundational models in neuroscience research and applications.
SUBMISSION GUIDELINES:
We invite original contributions in the form of full research papers, review articles, or extended abstracts of ongoing works aligned with the workshop's themes. Full papers should not exceed 8 pages (excluding references), while extended abstracts should be limited to 4 pages. All submissions will undergo a rigorous peer-review process conducted by experts in the field.
Submission follows the ECCV format (see the ECCV website for format instruction (https://eccv.ecva.net/Conferences/2024/AuthorGuide)).
WORKSHOP PROCEEDINGS
All accepted contributed papers will appear in the ECCV2024 workshop proceedings.
WORKSHOP ORGANIZERS
Nouria Lakhdar Ghazal – Mohammed V University of Rabat, Morocco
Simone Palazzo – University of Catania, Italy
Matteo Pennisi – Campus Bio Medico Rome, Italy
Federica Proietto Salanitri – University of Catania, Italy
Shanmuganathan Raman – Indian Institute of Technology, India
Concetto Spampinato – University of Catania, Italy
Andreas Tolias – University of Houston, Texas
Jonathan Xu – University of Waterloo, Canada
First International Workshop on “AI-based All-Weather Surveillance System”, AWSS 2024 in conjunction with ACCV 2024
July 16th, 2024 Daniela Lopez de Luise Main Organizers
Thierry Bouwmans, Associate Professor (HDR), Laboratoire MIA, La Rochelle Université, France, Email : tbouwman@univ-lr
Santosh Kumar Vipparthi, Associate Professor, Dept. of Computer Science & Engineering, MNIT, Jaipur, India, Email : kvipparthi@iitrpr.ac.in
Subrahmanyam Murala, Trinity College Dublin, Ireland, Email: muralas@tcd.ie
Sajid Javed, Khalifa University of Science and Technology, UAE, Email : sajid.javed@ku.ac.ae
Description (AWSS 2024 (google.com))
Advances in computer vision and the falling costs of camera hardware have allowed the massive deployment of cameras for monitoring physical premises. The extensive deployment of fixed and movable cameras for control and safety has resulted in visual data collection for online and post-event analysis. However, different environmental conditions such as haze or fog, snow, dust, raindrops, and rain streaks degrade the perceptual quality of the data, eventually affecting the architecture performance on high-level computer vision tasks such as change detection, object detection, traffic monitoring, border surveillance, behavior analysis, video synopsis, action recognition, anomaly detection, and object tracking, motion magnification, etc. In literature, different modeling methods based on deep learning (CNNs, GNNs) and graph signal processing concepts have been employed to address the challenges of weather-specific applications (either removal of rain, fog, snow, or haze) only. Nevertheless, only few algorithms allow to handle these multi-weather conditions with a unified network. Moreover, these algorithms require high computational complexity, which leads to poor inference performance in real-world scenarios, and also are most-of-the time not suitable in unseen scenarios. In addition, very few algorithms are available for simultaneous image/video restoration and static/moving object detection in these challenging multi-weather scenarios.
Most of the time, these algorithms employ two-stage architectures to address these challenges. In the first stage, an application-specific image/video degrading algorithm is applied, and in the second stage, high-level video processing tasks such as static/moving objects are detected. Thus, there is an immense need to design and develop end-to-end unified learning architectures which restore the image/videos and detect the static/moving objects under sparse to extreme multi-weather conditions.
Goal of This Workshop
The goals of this workshop are three-fold:
-
Designing unified framework that handles low- and high-level computer vision applications such as intelligent transportation, intelligent surveillance systems, conventional/aerial image or video enhancements.
-
Proposing new algorithms that can fulfil the requirements of real-time applications,
-
Proposing robust and interpretable deep learning to handle the key challenges in pattern in these applications.
Broad Subject Areas for Submitting Papers
Papers are solicited to address deep learning methods to be applied in based all-weather surveillance system,including but not limited to the following:
-
Graph Machine Learning for Computer Vision
-
Transductive/Inductive Graph Neural Networks (GNNs)
-
GNNs Architectures
-
Zero-shot Learning
-
Graph Signal Processing for Computer Vision
-
Graph Spectral Clustering for Computer Vision
-
Ensemble learning-based methods
-
Meta-knowledge Learning methods
-
RGB-D cameras, Event based cameras
Important Dates
Full Paper Submission Deadline: August 30, 2024
Decisions to Authors: September 20, 2024
Camera-ready Deadline: Same than ACCV 2024.
Selected papers, after extensions and further revisions, will be published in a special issue of an international journal.
Design by 2b Consult