Special Track
Computer vision studies how to extract useful information from perceptual signals, including images and video, and from complementary modalities when available. Recent progress in image processing, machine learning, and deep learning—particularly with modern neural architectures—has significantly expanded the capabilities and applications of visual perception systems across domains including healthcare, industry, agriculture, remote sensing, and smart cities. Despite these advances, challenges related to data quality, robustness, evaluation, and real-world deployment remain central to the field.
This track aims to provide an open forum for researchers, practitioners, and educators—particularly from the Latin American research community—to present and discuss recent theoretical and applied contributions in computer vision.
The track welcomes work spanning classical image processing techniques, machine learning methods, and modern deep learning approaches, as well as studies addressing shared challenges across the computer vision pipeline Topics of interest include, but are not limited to:
All research submissions must be in English.
Submissions must be in PDF, formatted with the Springer Publications format. For details on the Springer style, see here.
Submissions for RACV-IMDL 2026 here.
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
