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The anthropology of neoliberalism has become polarised between a hegemonic economic model anchored by variants of market rule and an insurgent approach fuelled by derivations of the Foucaultian notion of governmentality. Both conceptions obscure what is ‘neo’ about neoliberalism: the reengineering and redeployment of the state as the core agency that sets the rules and fabricates the subjectivities, social relations and collective representations suited to realising markets. I propose a via media between these two approaches that construes neoliberalism as an articulation of state, market and citizenship that harnesses the first to impose the stamp of the second onto the third.
In this paper we explore object recognition in clutter. We test our object recognition techniques on Gimpy and EZ-Gimpy, examples of visual CAPTCHAs. A CAPTCHA ("Completely Automated Public Turing test to Tell Computers and Humans Apart") is a program that can generate and grade tests that most humans can pass, yet current computer programs can't pass. EZ-Gimpy, currently used by Yahoo, and Gimpy are CAPTCHAs based on word recognition in the presence of clutter. These CAPTCHAs provide excellent test sets since the clutter they contain is adversarial; it is designed to confuse computer programs. We have developed efficient methods based on shape context matching that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time. The problem of identifying words in such severe clutter provides valuable insight into the more general problem of object recognition in scenes. The methods that we present are instances of a framework designed to tackle this general problem.