This project brings together an interdisciplinary collaboration between Engineering and Statistics for developing the science for automated object recognition. Object recognition technology is pervasive in a broad range of applications, such as safety and law enforcement, defense and security, autonomous navigation, industrial manufacturing, business process and e-commerce. The proposed transformative research provides a foundational framework for predicting the performance of object recognition algorithms based on probabilities and structural characteristics of the models and data. In contrast to previous work, the proposed research considers not only the data distortion factors but also the model similarity. The performance is explicitly modeled as a function of data distortion factors (feature uncertainty, occlusion and clutter) and model factors (similarity) so that one can ultimately characterize the probability distribution of performance rather than the empirical results on a specific dataset. The research will focus in three areas: 1) Developing the Bayesian formulation of performance prediction and bounds on performance. 2) Analyzing the effects of all of the assumptions made in the performance prediction approach; evaluating the effects of mathematical tractability, complexity and the gain in performance and validating different assumptions based on real data. 3) Generating results of the approach for practical applications. The development of scientific theory, prediction and computational models for recognition will result in the development of systematic approaches to the design of recognition systems that can reliably achieve predictable results in complex real-world, and contribute towards the science of computer-based object recognition.
