most citedBatVision with GCC-PHAT Features for Better Sound to Vision Predictions

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2020

Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission

Jesper Haahr Christensen, Lars Valdemar Mogensen, Ole Ravn

Low-bandwidth communication, such as underwater acoustic communication, is limited by best-case data rates of 30--50 kbit/s. This renders such channels unusable or inefficient at b…

cs.RO2020

SeaShark: Towards a Modular Multi-Purpose Man-Portable AUV

Jesper Haahr Christensen, Marco Jacobi, Martin Clemmensen Rotne +4

In this work, we present the SeaShark AUV: a modular, easily configurable, one-man portable micro-AUV. The SeaShark AUV is conceived as modular parts that fit around a central main…

cs.CV2020

Deep Learning based Segmentation of Fish in Noisy Forward Looking MBES Images

Jesper Haahr Christensen, Lars Valdemar Mogensen, Ole Ravn

In this work, we investigate a Deep Learning (DL) approach to fish segmentation in a small dataset of noisy low-resolution images generated by a forward-looking multibeam echosound…

cs.CV20205 cited

BatVision with GCC-PHAT Features for Better Sound to Vision Predictions

Jesper Haahr Christensen, Sascha Hornauer, Stella Yu

Inspired by sophisticated echolocation abilities found in nature, we train a generative adversarial network to predict plausible depth maps and grayscale layouts from sound. To ach…

cs.CV2019

BatVision: Learning to See 3D Spatial Layout with Two Ears

Jesper Haahr Christensen, Sascha Hornauer, Stella Yu

Many species have evolved advanced non-visual perception while artificial systems fall behind. Radar and ultrasound complement camera-based vision but they are often too costly and…