Incorporating Near-Infrared Information into Semantic Image Segmentation
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Summary
It is shown that adding NIR leads to improved performance for classes that correspond to a specific type of material in both outdoor and indoor scenes, and the results with respect to the physical properties of the NIR response are discussed.
- Type
- preprint
- Published
- 2014-06-23
- Cited by
- 13
- References
- 53
- Access
- Open access
- OpenAlex
- https://openalex.org/W194919261
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14982859
Keywords
Computer science, Artificial intelligence, Segmentation, RGB color model, Computer vision
References
- Colouring the Near-Infrared
- Handbook of Near-Infrared Analysis
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- The devil is in the details: an evaluation of recent feature encoding methods
- On the use of regions for semantic image segmentation
- Combining Visible and Near-Infrared Cues for image Categorisation
- Object based image analysis for remote sensing
- Dark flash photography
- Multi-spectral SIFT for scene category recognition
- Invisible light: Using infrared for video conference relighting
- Pattern codification strategies in structured light systems
- Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study
- An Efficient Approach to Semantic Segmentation
- On-Line Classification of Synthetic Polymers Using near Infrared Spectral Imaging
- What is a good evaluation measure for semantic segmentation?
- Energy minimization for linear envelope MRFs
- Semantic texton forests for image categorization and segmentation
- What energy functions can be minimized via graph cuts?
- Hyperspectral remote sensing of plant pigments.
- Multi-class image segmentation using conditional random fields and global classification
Cited by
- Exponential family Fisher vector for image classification
- Did Evolution get it right?: An evaluation of Near-Infrared Imaging for Semantic Scene Segmentation
- Supervised classification of civil air patrol (CAP)
- Deeply Aggregated Alternating Minimization for Image Restoration
- Deep Semantic Segmentation Using Nir as Extra Physical Information
- Near-Infrared Image Guided Reflection Removal
- Learning Deeply Aggregated Alternating Minimization for General Inverse Problems
- Vegetation Detection Using Deep Learning and Conventional Methods
- Enhanced machine perception by a scalable fusion of RGB–NIR image pairs in diverse exposure environments
- Near-infrared fusion for deep lightness enhancement
- Semantic Image Segmentation: Two Decades of Research
- MMCAN: Multi-Modal Cross-Attention Network for Free-Space Detection with Uncalibrated Hyperspectral Sensors
- Face Reflection Removal Network Using Multispectral Fusion of RGB and NIR Images
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