80,714 research outputs found
Cross-View Image Matching for Geo-localization in Urban Environments
In this paper, we address the problem of cross-view image geo-localization.
Specifically, we aim to estimate the GPS location of a query street view image
by finding the matching images in a reference database of geo-tagged bird's eye
view images, or vice versa. To this end, we present a new framework for
cross-view image geo-localization by taking advantage of the tremendous success
of deep convolutional neural networks (CNNs) in image classification and object
detection. First, we employ the Faster R-CNN to detect buildings in the query
and reference images. Next, for each building in the query image, we retrieve
the nearest neighbors from the reference buildings using a Siamese network
trained on both positive matching image pairs and negative pairs. To find the
correct NN for each query building, we develop an efficient multiple nearest
neighbors matching method based on dominant sets. We evaluate the proposed
framework on a new dataset that consists of pairs of street view and bird's eye
view images. Experimental results show that the proposed method achieves better
geo-localization accuracy than other approaches and is able to generalize to
images at unseen locations
A data-based approach for multivariate model predictive control performance monitoring
An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system
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