dr. David Van Hamme
- ORCID iD
- 0000-0003-2112-3475
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Segmentation of range-azimuth maps of FMCW radars with a deep convolutional neural network
(2023) ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2023. In Lecture Notes in Computer Science 14124. p.136-147 -
Joint probabilistic data fusion for pedestrian detection in multimodal images
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- Journal Article
- A1
- open access
Deep learning tone-mapping and demosaicing for automotive vision systems
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Bayesian optimisation of existing object detection methods for new contexts
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- Journal Article
- A1
- open access
High-dynamic-range tone mapping in intelligent automotive systems
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- Conference Paper
- P1
- open access
Low-complexity deep HDR fusion and tone mapping for urban traffic scenes
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- Journal Article
- A1
- open access
Efficient detection of crossing pedestrians from a moving vehicle with an array of cameras
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- Conference Paper
- C1
- open access
Ego-motion estimation with a low power millimeter wave radar on a UAV
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- Journal Article
- A1
- open access
Probabilistic fusion for pedestrian detection from thermal and colour images
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- Conference Paper
- P1
- open access
Perception system based on cooperative fusion of lidar and cameras
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- Journal Article
- A4
- open access
Haalbaarheid van veiligheidssystemen die gevaarlijke situaties vanop de fiets detecteren : onderzoek met behulp van LiDAR-technologie levert veelbelovende resultaten op
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- Conference Paper
- P1
- open access
Automatic labeling of vulnerable road users in multi-sensor data
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- Journal Article
- A1
- open access
Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections
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Detecting vehicles’ relative position on two-lane highways through a smartphone-based video overtaking aid application
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- Conference Paper
- P1
- open access
Weakly supervised deep learning method for vulnerable road user detection in FMCW radar