The purpose of the Applied Imagery Pattern Recognition (AIPR) annual workshops is to bring together researchers from government, industry, and academia in an elegant setting conducive to technical interchange across a broad range of disciplines. The papers span a range of topics, from research to fielded systems and provide to scientists, developers, and managers alike, a broad vision of the applicability of image analysis and machine learning technologies.

AIPR continues the half-century of success and tradition in pioneering new topics in applied image and visual understanding. Artificial intelligence (AI) & machine learning (ML) have proven to be extremely useful in analyzing and classifying images and are now prolific in image analysis, geoscience, biomedical image understanding, robotics, face recognition, and beyond. Forbes estimates that artificial intelligence (AI) will become a $150 trillion dollar industry. AI is impacting nearly every facet of life and with the advent of large language models and transformers for dialog (i.e. Generative Pre-trained Transformer (GPT), Bard) and novel image or video generation (i.e. Dall-E) it will likely redefine our world faster than previous advances in computational learning. While machine learning (ML) and deep learning (DL) is heavily anchored in supervised learning, recent algorithms (e.g., ChatGPT, DALL-E, etc.) are using self-supervised, transfer, and reinforcement learning. However, all these approaches are data intensive. Where does the data and its associated truth/metadata come from? While simulation has been around for decades, what’s new is a convergence in the maturity, realism, and availability of relatively simple-to-use tools and content/assets for individuals who are not computer graphics, physics, nor gaming experts. Companies like NVIDIA, Google, Microsoft, Meta, OpenAI, Apple, Tesla, IBM, Epic Games, Unity Technologies, Scale AI, and others have taken this a step further and developed billion-dollar in-house solutions based on synthetic data-driven AI. The 2023 IEEE AIPR Workshop will explore AI/ML/DL in synthetic, augmented, and natural datasets. In addition to papers on regular AIPR topics in applied imagery, as they pertain to computer vision, imaging, and pattern recognition, the Workshop Committee invites papers focused on the theme of Deep Learning Using Synthetic, Augmented, and Natural Datasets. 

Call for Abstracts webpage 

Online Final Paper submission line and link

AIPR is organized by the AIPR Workshop Committee, with generous support from: