Integrated Sensing: Active Stereoscopic Visual Search Driven by Natural Scene Statistics

《由自然场景统计学驱动的主动立体视觉搜索》Alan C.Bovik，Lawrence K.Cormack，J.Ghosh本提案的主要目的是开发基于立体图像的自然统计的方法，该方法将通过开发和应用关于人的注视和搜索机制的知识来设计和实现能够高效和智能地进行视觉搜索的下一代凹陷、注视机器视觉系统。我们通过以下几个主要目标总结了我们建议的意图：目标1：在各种三维视觉搜索和学习任务中，开发作为自然场景统计函数的人类主动立体视觉的定量描述。我们的重点将是基于独特和深入的统计分析，开发吸引低水平视觉注意的立体声基元的统计模型。我们认为，基于自然场景统计的统计模型在确定性模型失败的地方有很好的成功机会。目标2：训练最先进的中心凹、注视的主动计算机视觉系统(称为中心凹)来搜索和学习搜索4维(空间-时间)场景。为了做到这一点，将使用针对远程呈现人类搜索模式训练的后端人工神经网络。作为目标1研究的一部分，发现的统计模型和提取的统计立体词将用作先验知识，以改进网络的结构和学习。我们设想，这些实验将产生智能主动机器视觉协议，用于探索、搜索和与4-D环境交互，同时为视觉认知过程提供新的见解。

ABSTRACT"ACTIVE STEREOSCOPIC VISUAL SEARCH DRIVEN BY NATURAL SCENE STATISTICS"Alan C. Bovik, Lawrence K. Cormack, J. Ghosh The primary thrust of this proposal is to develop methods based on the natural statistics of stereoscopic images that will enable the design and implementation of the next generation of foveated, fixating machine vision systems that are capable of efficient and intelligent visual search, by exploiting and applying knowledge about human fixation and search mechanisms. We summarize the intention of our proposal via the following key goals:Goal 1: To develop a quantitative description of human active stereo vision as a function of natural scene statistics in a variety of three-dimensional visual search and learning tasks. Our emphasis will be on developing statistical models of stereo primitives that attract low-level visual attention based on a unique and in-depth statistical analysis. We feel that statistical models based on natural scene statistics have a very good chance of succeeding where deterministic models have failed.Goal 2: To train a state-of-the-art foveated, fixating active computer vision system (named FOVEA) to search and to learn to search 4-D (space-time) scenes. To do this, back-end artificial neural networks trained on telepresent human search patterns will be used. The statistical models and extracted statistical stereoprimitives discovered as part of the research in Goal 1 will be used as a priori knowledge to improve the configuration and learning of the networks. We envision that these experiments will result in smart active machine vision protocols for exploring, searching, and interacting with 4-D environments, while giving new insights into visual cognitive processes.