Object-Image Metrics for Generalized Weak Perspective Projection

Statistics and Analysis of Shapes - Modeling and Simulation in Science, Engineering and Technology ◽

10.1007/0-8176-4481-4_10 ◽

2006 ◽

pp. 253-279 ◽

Cited By ~ 4

Author(s):

Gregory Arnold ◽

Peter F. Stiller ◽

Kirk Sturtz

Keyword(s):

Perspective Projection ◽

Object Image ◽

Image Metrics

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Robustness and statistical analysis of object/image metrics

10.1117/12.650700 ◽

2006 ◽

Author(s):

Peter F. Stiller

Keyword(s):

Statistical Analysis ◽

Object Image ◽

Image Metrics

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Real-time object tracking based on colour feature and perspective projection

2015 9th International Conference on Sensing Technology (ICST) ◽

10.1109/icsenst.2015.7438481 ◽

2015 ◽

Cited By ~ 2

Author(s):

Joseph K. P. Tsoi ◽

N. D. Patel ◽

A. K. Swain

Keyword(s):

Object Tracking ◽

Real Time ◽

Perspective Projection ◽

Colour Feature

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Computer Vision Tagging the Metropolitan Museum of Art's Collection

Journal on Computing and Cultural Heritage ◽

10.1145/3446621 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1-17

Author(s):

Elena Villaespesa ◽

Seth Crider

Keyword(s):

Computer Vision ◽

Selection Criteria ◽

Object Image ◽

Metropolitan Museum ◽

Museum Collections ◽

Vision Systems ◽

Computer Vision Systems ◽

Similarities And Differences ◽

Ibm Watson

Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.

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