Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  have developed a stereo vision based obstacle detection (OD) system that can be used to detect obstacles in off-road terrain during both day and night conditions. In order to acquire enough depth estimates for reliable OD during low visibility conditions,  propose a stereo disparity (depth) estimation approach that uses fine-to-coarse selection in a stereo image pyramid. This fine-to-coarse selection is based on a novel disparity validity metric that reflects the estimation reliability. Dense three-dimensional terrain data is reconstructed from the estimated stereo disparities. In our OD methods, several geometric properties, such as the terrain slope, are inspected to distinguish beten obstacles and drivable terrain. This is achieved in a robust and efficient manner by considering the inherent uncertainty in stereo depth and using a hysteresis threshold. A large and varied collection of day- and nighttime images has been used to evaluate the performance of our system. The results show that our methods can reliably detect different types of obstacles in all tested conditions.
['Gijs Dubbelman', 'W. van der Mark', 'J.C. van den Heuvel', 'Frans C. A. Groen']
I am writing a research paper on a idea Obstacle detection during day and night conditions using stereo vision
[{"from": "human", "value": "I am writing a research paper on a idea Obstacle detection during day and night conditions using stereo vision"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  have developed a stereo vision based obstacle detection (OD) system that can be used to detect obstacles in off-road terrain during both day and night conditions. In order to acquire enough depth estimates for reliable OD during low visibility conditions,  propose a stereo disparity (depth) estimation approach that uses fine-to-coarse selection in a stereo image pyramid. This fine-to-coarse selection is based on a novel disparity validity metric that reflects the estimation reliability. Dense three-dimensional terrain data is reconstructed from the estimated stereo disparities. In our OD methods, several geometric properties, such as the terrain slope, are inspected to distinguish beten obstacles and drivable terrain. This is achieved in a robust and efficient manner by considering the inherent uncertainty in stereo depth and using a hysteresis threshold. A large and varied collection of day- and nighttime images has been used to evaluate the performance of our system. The results show that our methods can reliably detect different types of obstacles in all tested conditions."}]