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	Analysis of a Biologically-Inspired System for Real-time Object Recognition Erik Murphy-Chutorian, Sarah Aboutalib and Jochen Triesch Department of Electrical and Computer Engineering, UCSD Department of Cognitive Science, UCSD Frankfurt Institute for Advanced Studies We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system. Download this article This article should be cited as: Murphy-Chutorian, E., Aboutalib, S., Triesch, J.(2005) Analysis of a Biologically-Inspired System for Real-time Object Recognition Cognitive Science Online, 3.2, pp. 1-14. http://cogsci-online.ucsd.edu/3/3-3.pdf

	Theory Pictures as Trails: Diagrams and the Navigation of Theoretical Narratives J. R. Osborn Department of Communication, UCSD This paper examines diagrams as academic and theoretical tools. Drawing upon the work of Gilles Deleuze and Felix Guattari (1987), a diagram is defined as an abstract machine for constructing arguments. The theoretical diagram provides neither a direct representation of the natural world nor a representation of a natural data set, but a suggested theoretical walk through a landscape of data. It is a tool for learning how to see, how to reason, and how to narrate. The paper begins with a closer examination of diagrammatic thought and the ways in which diagrams differ from other visual representations. It then introduces Vannevar Bush (1945) and follows his idea of associative trails through more recent attempts at modeling semantic associations (Semantica Inc., 2005) and the use of "trails" as narrative markers in the sequential art of comics (McCloud, 1993). These trails, in turn, lead to a discussion of academic work practices, trajectory (Strauss, 1993), and the means of navigating information ecologies (Hutchins, 1996; Bowker and Star, 1999). Finally, the path returns to visualization practices, where it uncovers diagrams as a distinct strategy which scholars may employ as a method of analysis. Along the way, diagrams are offered as both examples and theoretical models. For, among their other benefits, diagrammatic models construct a visual language and represent what is difficult to express in prose. Download this article This article should be cited as: Osborn, J.R.(2005) Theory Pictures as Trails: Diagrams and the Navigation of Theoretical Narratives Cognitive Science Online, 3.2, pp. 15-44. http://cogsci-online.ucsd.edu/3/3-4.pdf
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