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- W2024357166 abstract "Computer vision has evolved dramatically in the last 20 years or so, especially in the domains of classification and recognition. With the advent of a usable internet, algorithms dealing with hundreds or thousands of images had to be transformed or replaced by ones dealing with millions or tens of millions. The internet has made crowd sourcing possible, reducing dramatically the time and cost needed for some semi-automatic tasks such as annotating large numbers of images. However, annotation remains a bottleneck for many applications, a topic addressed by a paper in this issue using active learning [2]. Simultaneously, higher computer power has become available at lower and lower prices via hardware solutions (GPUs, for example) and access to low-cost or even free high-throughput computing facilities (cloud computing, for example). There has never been such a convergence of huge opportunities and steep challenges for vision algorithm development. It is safe to say that the computer vision community has risen to these challenges and embraced the opportunities. The eight papers in this issue offer an intriguing cross-section of the state-of-the-art. Each of them has been carefully selected after multiple review cycles. Between them they deal with important issues in learning and feature representation, propose novel methods for recognition of objects, actions and places, and cast new light on time-honoured algorithms such as the Hough transform [8]. In Object and Action Classification with Latent Window Parameters, Bilen" @default.
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- W2024357166 date "2014-01-23" @default.
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- W2024357166 title "Objects, Actions, Places" @default.
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- W2024357166 doi "https://doi.org/10.1007/s11263-014-0699-3" @default.
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