Applying Deep Learning for cockpit segmentation in the context of mixed reality
Quick Answer
This study utilizes U-net and DeepLabV3+ convolutional neural networks for cockpit image segmentation, achieving approximately 90% accuracy.
Quick Take
This study utilizes U-net and DeepLabV3+ convolutional neural networks for cockpit image segmentation, achieving approximately 90% accuracy. The approach enhances mixed reality experiences by effectively distinguishing foreground from background in real-time images captured from a CAT793F truck simulator.
Key Points
- Utilized U-net and DeepLabV3+ for image segmentation in mixed reality.
- Achieved around 90% accuracy in distinguishing foreground and background.
- Real images were captured using a CAT793F off-highway truck simulator.
- Enhances user immersion in simulated environments through effective image processing.
- Focuses on integrating virtual objects with real-world imagery.
Article Excerpt
From source RSS / original summaryarXiv:2606. 06520v1 Announce Type: new Abstract: Computer vision is an area that has been growing continuously. With the advance of technologies with a first-person view, new development opportunities have emerged inside the area. Mixed reality promotes virtual environments with objects from the physical world shown in real time. For that, it's necessary to be concerned with the immersion of the user in this simulated environment, increasingly seeking to bring it closer to a possible desired reality.
This paper proposes the development of image processing in order to perform the segmentation of images to identify what is foreground and background in order to facilitate the union of virtual and real images. Thus, the present work obtain real images of the user using the off-highway truck simulator CAT793F, through a camera, to be able to perform the segmentation of such images with artificial intelligence techniques.
The convolutional neural network architectures "U-net" and "DeepLabV3+" are applied to perform image segmentation. As a result, metrics with around 90% accuracy were presented and and the best model was determined.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark. This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.
