5060275
A CAD-based computer vision system
A vision system for recognizing rigid and articulated three-dimensional objects in two-dimensional images is described. Geometrical models are extracted from a commercial computer aided design package. The models are then augmented with appearance and functional information which improves the system's hypothesis generation, hypothesis verification, and pose refinement. Significant advantages over existing CAD-based vision systems, which utilize only information available in the CAD system, are realized. Examples show the system recognizing, locating, and tracking a variety of objects in a robot work-cell and in natural scenes.
41957373
'Model': a POPLOG package to support model-based vision
Abstract A software package to support model-based vision in the poplog environment is described. The package contains a constructive solid geometry instantiated in an extended-depth buffer to yield a representation of the spatial properties of the viewed scene. The geometry constrains its geometrical transformations to make visual search more efficient.
19285984
Surface Based Hypothesis Verification in Intensity Images Using Geometric and Appearance Data
In this paper we discuss current work concerning Appearance-based and CAD-based vision; two opposing vision strategies. CAD-based vision is geometry based, reliant on having complete object centred models. Appearance-based vision builds view dependent models from training images. Existing CAD-based vision systems that work with intensity images have all used one and zero dimensional features, for example lines, arcs, points and corners. We describe a system we have developed for combining these two strategies. Geometric models are extracted from a commercial CAD library of industry standard parts. Surface appearance characteristics are then learnt automatically by observing actual object instances. This information is combined with geometric information and is used in hypothesis evaluation. This augmented description improves the systems robustness to texture, specularities and other artifacts which are hard to model with geometry alone, whilst maintaining the advantages of a geometric description.
22373766
Explorations of an incremental, Bayesian algorithm for categorization
An incremental categorization algorithm is described which, at each step, assigns the next instance to the most probable category. Probabilities are estimated by a Bayesian inference scheme which assumes that instances are partitioned into categories and that within categories features are displayed independently and probabilistically. This algorithm can be shown to be an optimization of an ideal Bayesian algorithm in which predictive accuracy is traded for computational efficiency. The algorithm can deliver predictions about any dimension of a category and does not treat specially the prediction of category labels. The algorithm has successfully modeled much of the empirical literature on human categorization. This paper describes its application to a number of data sets from the machine learning literature. The algorithm performs reasonably well, having its only serious difficulty because the assumption of independent features is not always satisfied. Bayesian extensions to deal with nonindependent features are described and evaluated.
17909219
Robot Free Will
We introduce the perspex machine which unifies projective geometry and Turing computation and results in a supra-Turing machine. We show two ways in which the perspex machine unifies symbolic and non-symbolic AI. Firstly, we describe concrete geometrical models that map perspexes onto neural networks, some of which perform only symbolic operations. Secondly, we describe an abstract continuum of perspex logics that includes both symbolic logics and a new class of continuous logics. We argue that an axiom in symbolic logic can be the conclusion of a perspex theorem. That is, the atoms of symbolic logic can be the conclusions of sub-atomic theorems. We argue that perspex space can be mapped onto the spacetime of the universe we inhabit. This allows us to discuss how a robot might be conscious, feel, and have free will in a deterministic, or semi-deterministic, universe. We ground the reality of our universe in existence. On a theistic point, we argue that preordination and free will are compatible. On a theological point, we argue that it is not heretical for us to give robots free will. Finally, we give a pragmatic warning as to the double-edged risks of creating robots that do, or alternatively do not, have free will.
15021995
Maintaining High-Bandwidth Under Dynamic Network Conditions
The need to distribute large files across multiple wide-area sites is becoming increasingly common, for instance, in support of scientific computing, configuring distributed systems, distributing software updates such as open source ISOs or Windows patches, or disseminating multimedia content. Recently a number of techniques have been proposed for simultaneously retrieving portions of a file from multiple remote sites with the twin goals of filling the client's pipe and overcoming any performance bottlenecks between the client and any individual server. While there are a number of interesting tradeoffs in locating appropriate download sites in the face of dynamically changing network conditions, to date there has been no systematic evaluation of the merits of different protocols. This paper explores the design space of file distribution protocols and conducts a detailed performance evaluation of a number of competing systems running in both controlled emulation environments and live across the Internet. Based on our experience with these systems under a variety of conditions, we propose, implement and evaluate Bullet' (Bullet prime), a mesh based high bandwidth data dissemination system that outperforms previous techniques under both static and dynamic conditions.
8794180
The generality/specificity of expertise in scientific reasoning
Previous research on scientific reasoning has shown that it involves a diverse set of skills. Yet, little is known about generality or domain specificity of those skills, an important issue in theories of expertise and in attempts to automate scientific reasoning skills. We present a study designed to test what kinds of skills psychologists actually use in designing and interpreting experiments and contrast expertise within a particular research area with general expertise at designing and interpreting experiments. The results suggest that psychologists use many domain-general skills in their experimentation and that bright and motivated Carnegie Mellon undergraduates are missing many of these skills. We believe that these results have important implications for psychological and artificial intelligence models of expertise, as well as educational implications in the domain of science instruction.
12350992
A Rational Analysis of Categorization
Abstract A rational analysis tries to predict the behavior of a cognitive system from the assumption it is optimized to the environment. An iterative categorization algorithm has been developed which attempts to get optimal Bayesian estimates of the probabilities that objects will display various features. A prior probability is estimated that an object comes from a category and combined with conditional probabilities of displaying features if the object comes from the category. Separate Bayesian treatments are offered for the cases of discrete and continuous dimensions. The resulting algorithm is efficient, works well in the case of large data bases, and replicates the full range of empirical literature in human categorization.