Understanding the Geometry of Urban Scenes – We study the problem of estimating the spatial dependencies between scenes in urban environments. Given a few images, we propose a simple yet effective method to infer an appropriate coordinate system of two images. This method is based on two properties. First, if the spatial dependencies of images are not well-formed from a purely visual perspective, they contain a false representation of the spatial coordinate system. Second, an image obtained from a distance map is not a natural image to be estimated. We study the relationship between spatial dependencies and the geometrical properties of the two images. We show that the inferred coordinates are correct to the spatial position of images taken with the same camera angle, but this is different than the distance-invariant point of the two images. Our experimental results on the MNIST dataset show that our method is effective for capturing the spatial information of two images. Furthermore, we show that our method produces accurate and accurate estimates of spatial dependencies. Finally, we explore the performance of an alternative method based upon the Euclidean metric of the coordinate system.
An automatic font recognition (BSR) system is presented in this paper. A novel architecture is designed to recognize the characters in a large font of high quality. The system includes two features: character similarity maps (CSMs) for the recognition, based on a novel convolutional neural network approach. Each CSM encodes the character at the same level as the corresponding font with the information needed to train the CSM. The system is presented in this report.
A Multi-Agent Multi-Agent Learning Model with Latent Variable
Machine Learning Methods for Multi-Step Traffic Acquisition
Understanding the Geometry of Urban Scenes
Recurrent Neural Sequence-to-Sequence Models for Prediction of Adjective Outliers
Semantic Font Attribution Using Deep LearningAn automatic font recognition (BSR) system is presented in this paper. A novel architecture is designed to recognize the characters in a large font of high quality. The system includes two features: character similarity maps (CSMs) for the recognition, based on a novel convolutional neural network approach. Each CSM encodes the character at the same level as the corresponding font with the information needed to train the CSM. The system is presented in this report.