This paper describes a computer vision system in the context of exploiting parallelism. The key contribution is a description of a network design that breaks a long-standing bottleneck in the supervision phase of the vision process. The proposed solution draws from and contributes to the disciplines of machine learning, computer vision and collaborative editing. The significance of the solution is that it provides the means by which complex visual tasks such as mammography can be learned by an artificial vision system. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Drew, S., Venema, S., Sheridan, P., & Sun, C. (2003). Collaborative supervision of machine vision systems: Breaking a sequential bottleneck in the supervised learning process. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2834, 619–628. https://doi.org/10.1007/978-3-540-39425-9_72
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