# CVPR 1983

CVPR 1983 was the first IEEE Conference on Computer Vision and Pattern Recognition, held in Washington, DC, and organized by Takeo Kanade and Dana H. Ballard. It established an annual venue for research in computer vision and pattern recognition, later co-sponsored by the IEEE Computer Society and the Computer Vision Foundation.

The Conference on Computer Vision and Pattern Recognition (CVPR) is an annual academic conference focused on computer vision and pattern recognition. The first edition, CVPR 1983, was held in Washington, DC, and organized by Takeo Kanade and Dana H. Ballard. It marked the beginning of a series that has become a primary venue for presenting advances in extracting structures and answers from images, video, and data patterns.

## Origins and Sponsorship

CVPR 1983 was the inaugural event, taking place in Washington, DC. From 1985 to 2010, the conference was sponsored by the IEEE Computer Society. In 2011, it was co-sponsored by the University of Colorado Colorado Springs. Since 2012, the conference has been co-sponsored by the IEEE Computer Society and the Computer Vision Foundation, which provides open access to conference papers. The conference is typically held in June in North America.

## Scope and Review Process

The conference covers a wide range of topics in computer vision and pattern recognition, including object recognition, image segmentation, motion estimation, 3D reconstruction, and deep learning. The scope broadly encompasses any work that extracts structures or answers from images or video, or applies mathematical methods to data for pattern recognition. The conference generally has less than 30% acceptance rates for all papers and less than 5% for oral presentations. It is managed by a rotating group of volunteers chosen in a public election at the Pattern Analysis and Machine Intelligence Technical Community (PAMI-TC) meeting four years before the conference. The review process is multi-tier and double-blind, with program chairs (who cannot submit papers) selecting area chairs who manage reviewers for their subsets of submissions.

## Awards

### Best Paper Award

The Best Paper Award is selected by committees delegated by the program chairs of the conference.

### Longuet-Higgins Prize

The Longuet-Higgins Prize recognizes papers from ten years prior that have made a significant impact on computer vision research.

### PAMI Young Researcher Award

The Pattern Analysis and Machine Intelligence Young Researcher Award is given by the Technical Committee on Pattern Analysis and Machine Intelligence of the IEEE Computer Society to a researcher within 7 years of completing their Ph.D. for outstanding early career research contributions. Candidates are nominated by the computer vision community, with winners selected by a committee of senior researchers. The award was originally instituted in 2012 by the journal Image and Vision Computing, which continues to sponsor it.

### PAMI Thomas S. Huang Memorial Prize

The Thomas Huang Memorial Prize was established at the 2020 conference and is awarded annually starting from 2021 to honor researchers recognized as examples in research, teaching and mentoring, and service to the computer vision community.

## Related Conferences

Other major conferences in the field include the International Conference on Computer Vision and the European Conference on Computer Vision. The field of computer vision has deep connections to [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) and [machine-learning](https://www.wikiprompt.org/wiki/machine-learning), with modern advances often relying on [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) techniques such as [neural-network](https://www.wikiprompt.org/wiki/neural-network) architectures. Early work in pattern recognition also drew on foundational research from institutions like [xerox-parc](https://www.wikiprompt.org/wiki/xerox-parc) and [mit-csail](https://www.wikiprompt.org/wiki/mit-csail), and the field continues to evolve with contributions from academic labs such as [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab), [berkeley-ai-research](https://www.wikiprompt.org/wiki/berkeley-ai-research), and [carnegie-mellon-university](https://www.wikiprompt.org/wiki/carnegie-mellon-university).

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Source: https://www.wikiprompt.org/wiki/cvpr-1983
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Last updated: 2026-09-09T02:00:42.168246+00:00