Automated Detection of Informational Signs and Hazardous Objects: Visual Aids for the Blind

这项研究将开发一个框架，用于在不受约束的真实世界域中快速检测、定位和识别视觉目标。该框架将产生可在具有视频输入的便携式计算机上实现的算法，其目标是例如使盲人/视障人士能够在真实世界场景中导航。这些要求意味着算法必须非常高效地从输入图像中提取信息。该方法将使用目标和背景的统计分析，考虑到照明和视点变化引起的变化，以确定目标和背景的外观的概率模型。根据这些模型，将确定测试集和测试组。这些测试将被设计为最大限度地提供信息，基于对错误率的统计测量，如切尔诺夫信息，并导致在便携式PC上快速实施。搜索策略基于挑选测试的直觉，最大限度地增加关于目标假设的信息的预期收益。然而，在实际问题中，并不总是可以实时计算这些预期的信息收益。因此，搜索策略将使用A算法方面的更一般的公式，其中搜索是由启发式泰勒引导到应用领域的。期望信息是一种可能的启发式方法，但还有许多其他方法更容易计算，并且仍然可以证明收敛于最优解。

This research will develop a framework for the rapid detection, location, and identification of visual targets in unconstrained real world domains. This framework will lead to algorithms which can be implemented on portable computers with video input with the goal of being used, for example, to enable the blind/visually impaired to navigate in real world scenes. These requirements mean that the algorithms must be extremely efficient at extracting information from the input images. The approach will use statistical analysis of the targets and background, taking into account variations due to illumination and viewpoint variations, to determine probabilistic models for the appearance of the target and background. From these models, sets of tests and groups of tests will be determined. These tests will be designed to be maximally informative, based on statistical measures of errors rates such as Chernoff Information, and to lead to fast implementations on portable PC's. The search strategy is based on the intuition of picking tests which maximize the expected gain in information about the target hypothesis. In practical problems, however, it will not always be possible to compute these expected information gains in real time. Therefore the search strategy will make use of a more general formulation in terms of A algorithms where the search is guided by a heuristics taylored to the application domain. Expected information is one possible heuristic but there are many others which are more easily computable and which can still give provable convergence to the optimal solution.