Time-Interval-Guided Event Representation for Scene Understanding
The recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras to reconstr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3186 |
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| author | Boxuan Wang Wenjun Yang Kunqi Wu Rui Yang Jiayue Xie Huixiang Liu |
| author_facet | Boxuan Wang Wenjun Yang Kunqi Wu Rui Yang Jiayue Xie Huixiang Liu |
| author_sort | Boxuan Wang |
| collection | DOAJ |
| description | The recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras to reconstruct static scenes, particularly those in low illumination. We introduce a new method to elucidate the phenomenon where event cameras continue to generate events even in the absence of brightness changes, highlighting the crucial role played by noise in this process. Furthermore, we substantiate that events predominantly occur in pairs and establish a correlation between the time interval of event pairs and the relative light intensity of the scene. A key contribution of our work is the proposal of an innovative method to convert sparse event streams into dense intensity frames without dependence on any active light source or motion, achieving the static imaging of event cameras. This method expands the application of event cameras in static vision fields such as HDR imaging and leads to a practical application. The feasibility of our method was demonstrated through multiple experiments. |
| format | Article |
| id | doaj-art-aa7341da1e8049d89e6d6d233cfe9a8a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-aa7341da1e8049d89e6d6d233cfe9a8a2025-08-20T03:47:58ZengMDPI AGSensors1424-82202025-05-012510318610.3390/s25103186Time-Interval-Guided Event Representation for Scene UnderstandingBoxuan Wang0Wenjun Yang1Kunqi Wu2Rui Yang3Jiayue Xie4Huixiang Liu5School of Automation, Beijing Information Science and Technology University, Beijing 102206, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 102206, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 102206, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 102206, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 102206, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 102206, ChinaThe recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras to reconstruct static scenes, particularly those in low illumination. We introduce a new method to elucidate the phenomenon where event cameras continue to generate events even in the absence of brightness changes, highlighting the crucial role played by noise in this process. Furthermore, we substantiate that events predominantly occur in pairs and establish a correlation between the time interval of event pairs and the relative light intensity of the scene. A key contribution of our work is the proposal of an innovative method to convert sparse event streams into dense intensity frames without dependence on any active light source or motion, achieving the static imaging of event cameras. This method expands the application of event cameras in static vision fields such as HDR imaging and leads to a practical application. The feasibility of our method was demonstrated through multiple experiments.https://www.mdpi.com/1424-8220/25/10/3186event camerastatic imagingtime intervalintensity frames |
| spellingShingle | Boxuan Wang Wenjun Yang Kunqi Wu Rui Yang Jiayue Xie Huixiang Liu Time-Interval-Guided Event Representation for Scene Understanding Sensors event camera static imaging time interval intensity frames |
| title | Time-Interval-Guided Event Representation for Scene Understanding |
| title_full | Time-Interval-Guided Event Representation for Scene Understanding |
| title_fullStr | Time-Interval-Guided Event Representation for Scene Understanding |
| title_full_unstemmed | Time-Interval-Guided Event Representation for Scene Understanding |
| title_short | Time-Interval-Guided Event Representation for Scene Understanding |
| title_sort | time interval guided event representation for scene understanding |
| topic | event camera static imaging time interval intensity frames |
| url | https://www.mdpi.com/1424-8220/25/10/3186 |
| work_keys_str_mv | AT boxuanwang timeintervalguidedeventrepresentationforsceneunderstanding AT wenjunyang timeintervalguidedeventrepresentationforsceneunderstanding AT kunqiwu timeintervalguidedeventrepresentationforsceneunderstanding AT ruiyang timeintervalguidedeventrepresentationforsceneunderstanding AT jiayuexie timeintervalguidedeventrepresentationforsceneunderstanding AT huixiangliu timeintervalguidedeventrepresentationforsceneunderstanding |