6 citations · 10 across the 4 of their papers we have counts for
6 papers
Combining geolocation and height estimation of objects from street level imagery
Matej Ulicny, Vladimir A. Krylov, Julie Connelly +1
We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality…
Context Aware Object Geotagging
Chao-Jung Liu, Matej Ulicny, Michael Manzke +1
Localization of street objects from images has gained a lot of attention in recent years. We propose an approach to improve asset geolocation from street view imagery by enhancing…
Tensor Reordering for CNN Compression
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
We show how parameter redundancy in Convolutional Neural Network (CNN) filters can be effectively reduced by pruning in spectral domain. Specifically, the representation extracted…
Harmonic Convolutional Networks based on Discrete Cosine Transform
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spec…
Harmonic Networks with Limited Training Samples
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. Howe…
Harmonic Networks: Integrating Spectral Information into CNNs
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produ…