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

自然景物统计驱动的主动立体视觉系统。作者：Lawrence K.该提案的主要目的是开发基于立体图像的自然统计的方法，这些方法将使下一代foveated，fixating机器视觉系统的设计和实现成为可能，这些系统能够通过利用和应用有关人类固定和搜索机制的知识进行有效和智能的视觉搜索。我们通过以下关键目标总结了我们的建议的意图：目标1：在各种三维视觉搜索和学习任务中，开发人类主动立体视觉作为自然场景统计的函数的定量描述。我们的重点将是发展的统计模型，吸引低层次的视觉注意力的基础上，一个独特的和深入的统计分析的立体原语。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：训练一个最先进的有中心凹的、固定的主动计算机视觉系统（FOVEA）来搜索并学习搜索4-D（时空）场景。 要做到这一点，后端人工神经网络训练的远程人类搜索模式将被使用。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视素将用作先验知识，以改进网络的配置和学习。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.