32806401
Use of multiple difference-of-Gaussian filters to verify geometric models
Abstract Experiments are reported with a program which recognizes London buses in busy street scenes. The program uses a 3D model of a bus. together with knowledge of camera and scene parameters, to compute the visual appearance of possible target events. In earlier work this took the form of 2D templates, which identify locations within the image where abrupt discontinuities of grey-value are expected. Each template corresponds to an hypothesis which is verified by measuring the agreement between the predicted features and the response of a Sobel filter to the image data. This paper reports an extension to the program which offers improved selectivity, and a more general-purpose method of linking predicted features to detected imageattributes. Multiple difference-of-Gaussian filters perform an initial object-independent analysis of the image to provide an intermediate domain in which object-dependent features, predicted by hypothesized instances of the model, are tested.
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.
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.
14213624
Achieving Efficient and Cognitively Plausible Learning in Backgammon
Traditionally, computer applications to game domains have taken a brute-force approach, relying on sheer computational power to overcome the complexity of the domain. Although many of these programs have been quite successful, it is interesting to note that humans can still perform extremely well against them. Thus we are compelled to ask, if no human could match the computational power of most of these programs, are there methods for learning and performance in game domains that more closely reflect human cognition? In response to this question, this paper attempts to model how humans learn and play games by developing a Backgammon-playing algorithm based on cognition. Analysis of this algorithm shows that it is efficient and commensurate with human abilities suggesting that it provides a cognitively plausible theory of learning in Backgammon.
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.
195901903
Integrating analogical mapping and general problem solving: the path-mapping theory
This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects' incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.
29133778
Toward a real-time model-based training system
This article describes the development of a real-time model-based training system that provides adaptive ''over-the-shoulder'' (OTS) instructions to trainees as they learn to perform an Anti-Air Warfare Coordinator (AAWC) task. The long-term goal is to develop a system that will provide real-time instructional materials based on learners' actions, so that eventually the initial set of instructions on a task can be strengthened, complemented, or overridden at different stages of training. The training system is based on the ACT-R architecture, which serves as the theoretical background for the cognitive model that monitors the learning process of the trainee. An experiment was designed to study the impact of OTS instructions on learning. Results showed that while OTS instructions facilitated short-term learning, (a) they took time away from the processing of current information, (b) their effects tended to decay rapidly in initial stages of training, and (c) their effects on training diminished when the OTS instructions were proceduralized in later stages of training. A cognitive model that learned from both the upfront and OTS instructions was created and provided good fits to the learning and performance data collected from human participants. Our results suggest that to fully capture the symbiotic performance between humans and intelligent training systems, it is important to closely monitor the learning process of the trainee so that instructional interventions can be delivered effectively at different stages of training. We proposed that such a flexible system can be developed based on an adaptive cognitive model that provides real-time predictions on learning and performance.
6937633
A Theory of Language Acquisition Based on General Learning Principles
A simulation model is described for the acquisition of the control of syntax in language generation. This model makes use of general learning principles and general principles of cognition. Language generation is modelled as a problem solving process involving principly the decomposition of a lobe-communicated semantic structure into a hierarchy of subunits for generation. The syntax of the language controls this decomposition. It is shown how a sentence and semantic structure can be compared to infer the decomposition that led to the sentence. The learning processes involve generalizing rules to classes of words, learning by discrimination the various contextual constraints on a rule application, and a strength process which monitors a rule's history of success and failure. This system is shown to apply to the learning of noun declensions in Latin, relative clause constructions in French, and verb auxiliary structures in English.