Search Results for author: Clint Sebastian

Found 9 papers, 0 papers with code

Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery

no code implementations15 Apr 2020 Clint Sebastian, Raffaele Imbriaco, Egor Bondarev, Peter H. N. de With

Building extraction from aerial images has several applications in problems such as urban planning, change detection, and disaster management.

Change Detection Management +3

Aggregated Deep Local Features for Remote Sensing Image Retrieval

no code implementations22 Mar 2019 Raffaele Imbriaco, Clint Sebastian, Egor Bondarev, Peter H. N. de With

In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor.

Dimensionality Reduction Image Retrieval +1

LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas

no code implementations13 Mar 2019 Clint Sebastian, Bas Boom, Egor Bondarev, Peter H. N. de With

We propose a system that is cost-effective even after increasing the resolution by a factor of 2. 5.

Towards Accurate Camera Geopositioning by Image Matching

no code implementations13 Mar 2019 Raffaele Imbriaco, Clint Sebastian, Egor Bondarev, Peter de With

The matching of the query image is obtained with a recall@5 larger than 90% for panorama-to-panorama matching.

Clustering Position

Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery

no code implementations8 Oct 2018 Clint Sebastian, Bas Boom, Thijs van Lankveld, Egor Bondarev, Peter H. N. de With

Detection of buildings and other objects from aerial images has various applications in urban planning and map making.

Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery

no code implementations5 Sep 2018 Clint Sebastian, Ries Uittenbogaard, Julien Vijverberg, Bas Boom, Peter H. N. de With

We have performed detection and classification tests across a large number of traffic sign classes, by training the detector using the combination of real and generated data.

Asset Management Autonomous Driving +4

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