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

本研究将发展一个快速侦测、定位与辨识无限制真实的世界域中视觉目标的架构。 这个框架将导致算法，可以在便携式计算机上实现视频输入的目标，例如，使盲人/视障人士在真实的世界场景中导航。 这些要求意味着算法必须非常有效地从输入图像中提取信息。 该办法将 使用目标和背景的统计分析，考虑由于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。 这些测试将根据错误率的统计测量设计为最大限度地提供信息 例如，信息，并导致 在便携式PC上快速实现。 搜索策略是基于直觉的挑选测试，最大限度地提高预期收益的信息有关的目标假设。 然而，在实际问题中，并不总是能够在真实的时间中计算这些期望的信息增益。 因此，搜索策略将使用更一般的公式，在A算法中，搜索由泰勒应用领域的算法指导。 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到最优解。

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.