Computational Complexity of Vision and Cognition
Much has been written about how the biological brain might represent and process visual information, and how this might inspire and inform machine vision systems. Indeed, tremendous progress has been made, and especially during the last decade in the latter area. However, a key question seems too often, if not mostly, be ignored. This question is simply: do proposed solutions scale with the reality of the brain’s resources? This scaling question applies equally to brain and to machine solutions. A number of papers have examined the inherent computational difficulty of visual information processing using theoretical and empirical methods. The main goal of this activity had three components: to understand the deep nature of the computational problem of visual information processing; to discover how well the computational difficulty of vision matches to the fixed resources of biological seeing systems; and, to abstract from the matching exercise the key principles that lead to the observed characteristics of biological visual performance. This set of components was termed complexity level analysis in Tsotsos (1987) and was proposed as an important complement to Marr’s three levels of analysis. This project represents the continuing effort.
Tsotsos, J.K., Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us About How the Brain Might Represent Visual Information?,Frontiers in Psychology – Cognition, doi: 10.3389/fpsyg.2017.01216 (July 3, 2017)
Tsotsos, J.K., Computational Perspective on Visual Attention, MIT Press, 2011.
Ye, Y., Tsotsos, J.K., A Complexity Level Analysis of the Sensor Planning Task for Object Search, Computational Intelligence, 17(4), p605-620, Nov. 2001.
Parodi, P., Lanciwicki, R., Vijh, A., Tsotsos J.K., Empirically-Derived Estimates of the Complexity of Labeling Line Drawings of Polyhedral Scenes, Artificial Intelligence 105, p47-75, 1998.
Tsotsos, J.K., On Behaviorist Intelligence and the Scaling Problem, Artificial Intelligence, 75, p135-160, 1995.
Tsotsos, J.K., On the Relative Complexity of Passive vs. Active Visual Search, International Journal of Computer Vision, 7(2), p127-141, 1992.
Tsotsos, J.K., Analyzing Vision at the Complexity Level, Behavioral and Brain Sciences, 13(3), p423-445, 1990.
Tsotsos, J., The Complexity of Perceptual Search Tasks, Proc. International Joint Conference on Artificial Intelligence, Detroit, August, 1989, pp1571 – 1577.
Tsotsos, J.K, A `Complexity Level’ Analysis of Immediate Vision, International Journal of Computer Vision, Marr Prize Special Issue, 2(1), [303-320, Sept. 1988.
Tsotsos, J.K., How Does Human Vision beat the Computational Complexity of Visual Perception?, in Computational Processes in Human Vision: An Interdisciplinary Perspective, ed. by Z. Pylyshyn, Ablex Press, Norwood, NJ, pp. 286 – 338, 1988.
Tsotsos, J.K., A `Complexity Level’ Analysis of Vision, Proceedings of International Conference on Computer Vision: Human and Machine Vision Workshop, London, England, June 1987. (received an Honourable Mention in the Marr Prize competition for best paper).
Random Polyhedral Scenes: An Image Generator for Active Vision System Experiments
This project is specifically designed to support research in active perception by generating images of scenes with known complexity characteristics and with verifiable properties with respect to the distribution of features across a population. Thus, it is well-suited for research in active perception without the requirement of a live 3D environment and mobile sensing agent, including comparative performance evaluations.
Creates a random scene based on a few user parameters, renders the scene from random viewpoints, then creates a data set containing the renderings and corresponding annotation files. The system provides a web-interface as well as a RESTful API.
Solbach, Markus D., et al. “Random Polyhedral Scenes: An Image Generator for Active Vision System Experiments.” arXiv preprint arXiv:1803.10100 (2018).