Call for Paper for the International Workshop on “Sustainable Pattern Recognition and Computer Vision Developments – Balancing Innovation and Environmental Responsibility (SPRCV) in conjunction with ICPR 2024, Kolkata, India, December 1, 2024.

Description of Topic

We have experienced an unparalleled wave of technological advancements, with Artificial Intelligence (AI) taking the forefront in revolutionizing industries and reshaping the fabric of society. This rapid growth has ushered us into an era where machines simulate human intelligence, performing complex tasks and decision-making with remarkable efficiency. While the potential benefits of AI are immense, these technological advancements have encountered issues related to environmental well-being while developing and deploying. The intersection of such techniques and their environmental implications has emerged as a significant focal point in recent times as we wrestle with the challenges associated with climate change and environmental degradation. The energy consumption, carbon footprint, and resource utilization associated with such technologies are significant, raising questions about their long-term impacts on the environment. Therefore, standing at the junction of innovation and environmental well-being, it is crucial to address the concerns raised and outline solutions to nurture AI development in harmony with environmental welfare.

This workshop aims to provide a comprehensive platform for stakeholders to engage in profound discussions and gain practical insights into addressing the intricate relationship between AI-aided Pattern Recognition and Computer Vision technologies and sustainability. By exploring the multifaceted dimensions of this relationship, the workshop seeks not only to raise awareness of the environmental implications to the Pattern Recognition and Computer Vision community but also to equip participants with the knowledge and tools necessary to contribute to the development and deployment of such technologies in an environmentally responsible manner. Further, it is to be noted that the workshop will include general topics from the Pattern Recognition and Computer Vision domains that are designed in an environment-friendly way. In summary, the topics, but not limited to, that will be covered during this workshop are as follows.

• Energy-efficient algorithms and architectures
• Resource-efficient deep learning model training
• Environmental impact assessments of computer vision system
• Sustainable data collection and annotation methods
• Green computing in pattern recognition and computer vision
• Renewable energy applications for computer vision and pattern recognition
• Ethical considerations in sustainable AI
• Environment-friendly computer vision and pattern recognition applications
• Case studies and best practices in sustainable pattern recognition and computer vision

Main Organizers

– Dilip K. Prasad ,  UiT The Arctic University of Norway, Tromsø, Norway
– Balasubramanian Raman, Indian Institute of Technology Roorkee, India
– Krishan Berwal, National Institute of technology, Kurukshetra, India
– Debi Prosad Dogra, Indian Institute of Technology Bhubaneswar, India
– Ajit Bopardikar, University of Houston, USA
– Balvinder Singh, Samsung Electronics, India
– Ayan Kumar Bhunia, University of Surrey, UK & Sony, UK
– Samir Malakar, UiT The Arctic University of Norway, Tromsø, Norway
– Arif Ahmed, XIM University, India
– Iti Chaturvedi, James Cook University, Australia
– Ayush Somani, UiT The Arctic University of Norway, Tromsø, Norway.

Important Dates 

Full Paper Submission Deadline: August 09, 2024

Decisions to Authors:                  September 16, 2024

Camera-ready Deadline:             September 23, 2024

Publication: The accepted papers can be included in the ICPR 2024 workshop proceedings, which will be published in Lecture Notes in Computer Science (LNCS), Springer (https://www.springer.com/gp/computer-science/lncs)

Selected papers, after extensions and further revisions, will be published in a special issue of an international journal.