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.
Big data analytics and advanced computer hardware have pushed the limits of computational imaging and machine vision, yielding extraordinary results in domains such as:
- Robotics and autonomous systems - unmanned aerial vehicles and self-driving cars
- Big imaging datasets for medical, atmospheric, neurological, and more – in the commercial sector and across the disciplines like connectomics, deep learning, and biologically-inspired AI methods
- Cloud computing - Scalable and inexpensive parallel computational resources both local and in the cloud
The 2017 IEEE AIPR Workshop will explore these reemerging intersections and synergies between imaging, Big Data and computer hardware, continuing the workshop’s long tradition of bringing together researchers and developers who span the disciplines and work in labs across academia, industry, and government. The Workshop Committee invites papers that present new techniques, algorithms, analysis, applications, visualizations, systems, theoretical insights and hardware developments to the interplay between imaging and artificial intelligence. Topics include, but are not limited to, the following:
- Image-based autonomous capabilities in transportation, medicine, robotics, manufacturing, defense, space
- Image understanding: captioning, inference, retrieval, summarization, scene modeling
- Situational awareness: biometrics, surveillance, anomaly detection, environmental monitoring, video analytics, mixed and augmented reality
- Remote sensing: space-time processing, change detection, natural resource monitoring, enabling cloud applications
- Medical and biological applications: diagnosis, intervention, prognosis, telediagnostics, global health, scientific discovery
- AI tools for assisted image analysis, deep learning, visualization, performance metrics and evaluation methodologies
- Data Parallel architectures
- Advanced AI and machine hardware developments
- Historical accounts of machine vision development
AIPR 2017 Program Chairs:
- Robert Pless, George Washington University
- John Irvine, Draper Laboratory
- Pete Doucette, USGS
Please use our Automated Abstract and Paper Submission Page to submit your abstract (150 - 300 words).
The Workshop will include oral and poster presentations, several keynote talks that provide in-depth overviews of the fields, and a debate between two experts on a fundamental question. Written papers will be required (due after the workshop) and will be indexed in IEEE Xplore.