Abstract
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.
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Drew, S., Venema, S., Sheridan, P., Sun, C. (2003). Collaborative Supervision of Machine Vision Systems: Breaking a Sequential Bottleneck in the Supervised Learning Process. In: Zhou, X., Xu, M., Jähnichen, S., Cao, J. (eds) Advanced Parallel Processing Technologies. APPT 2003. Lecture Notes in Computer Science, vol 2834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39425-9_72
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DOI: https://doi.org/10.1007/978-3-540-39425-9_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20054-3
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