SGER: Stochastic Algorithms for Visual Search and Recognition

SGER建议0240148用于快速搜索和检测的DDMCMC算法PI：A.L.Yuille加州大学，车床建议的研究是设计并实现一种称为DDMCMC的快速视觉搜索算法，该算法使用数据驱动的前馈视觉线索来驱动蒙特卡罗算法。该方法结合了前馈算法的速度和自顶向下模型实例化算法的高质量性能。DDMCMC是由Co-PI为图像分割问题而开发的，并被证明在大数据集(具有GROUTALUAL)上给出了快速且非常准确的分割结果。通过使用AdaBoost学习等技术确定有效的前馈算法来驱动MCMC算法，将DDMCMC推广到搜索和检测任务中。更广泛的影响是开发计算机视觉算法来帮助盲人和视障人士(尽管我们也将使用这笔赠款来培训研究生)。为了实现这一点，将针对搜索然后阅读信息符号的特定任务开发DDMCMC算法的试点研究(结果可以通过语音合成器传达给视障用户)。这项研究将得到史密斯-凯特尔韦尔眼科研究所(SKERI)的研究人员的帮助，其中包括两名盲人工程师。所提出的算法将在盲人志愿者使用头部或身体安装式摄像头拍摄的图像数据集上进行设计和测试(以确保我们方法的真实性)。SKERI的研究人员还将就该方法的实用性以及与这项任务的替代技术(都不使用计算机视觉)进行比较的情况提供反馈。只要开发出快速有效的算法，计算机视觉在帮助视觉残障人士方面具有巨大的潜力。

SGER Proposal 0240148DDMCMC Algorithms for Rapid Search and DetectionPI: A.L. YuilleUniversity of California, LAThe proposed study is to design and implement a rapid visual search algorithm called DDMCMC, which uses data-driven feedforward visual cues to drive a Monte Carlo algorithm. This approach combines the speed of feedforward algorithms with the high quality performance of top-down model instantiation algorithms. DDMCMC was developed for the image segmentation problem by the Co-PI and was demonstrated to give fast and very accurate segmentation results on large datasets (with groundtruth). DDMCMC is generalized to the search and detection task by using techniques such as AdaBoost learning to determine effective feedforward algorithms to drive the MCMC algorithm. The broader impact is to develop computer vision algorithms to help the blind and visually impaired (though we will also use this grant to train graduate students). To achieve this, a pilot study of the DDMCMC algorithm will be developed for the specific task of searching for, and then reading, informational signs (the results can be communicated to a visually impaired user by a speech synthesizer). This study will be aided by researchers at the Smith-Kettlewell Eye Research Institute (SKERI) who include two blind engineers. The proposed algorithms will be designed and tested on image datasets taken by blind volunteers using head or body mounted cameras (to ensure the realism of our approach). Researchers at SKERI will also give feedback on the practicality of the approach and how it compares with alternative technologies for this task (none of which use computer vision). Computer vision has enormous potential to help the visually disabled provided fast and effective algorithms are developed.