Learning Low-Rank Images for Robust All-Day Feature Matching
| Autor: | Marcelo H. Ang, Mengdan Feng, Gim Hee Lee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Matching (statistics) business.industry Computer science media_common.quotation_subject Rank (computer programming) Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Image (mathematics) 020901 industrial engineering & automation Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Contrast (vision) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business media_common Feature detection (computer vision) |
| Zdroj: | 2019 IEEE Intelligent Vehicles Symposium (IV). |
| DOI: | 10.1109/ivs.2019.8813799 |
| Popis: | Image-based localization plays an important role in today's autonomous driving technologies. However, in large scale outdoor environments, challenging conditions, e.g., lighting changes or different weather, heavily affect image appearance and quality. As a key component of feature-based visual localization, image feature detection and matching deteriorate severely and cause worse localization performance. In this paper, we propose a novel method for robust image feature matching under drastically changing outdoor environments. In contrast to existing approaches which try to learn robust feature descriptors, we train a deep network that outputs the low-rank representations of the images where the undesired variations on the images are removed, and perform feature extraction and matching on the learned low-rank space. We demonstrate that our learned low-rank images largely improve the performance of image feature matching under varying conditions over a long period of time. |
| Databáze: | OpenAIRE |
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