Computational Imaging for Scene Understanding : Transient, Spectral, and Polarimetric Analysis

• Autore: Funatomi Takuya
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (341 pages)
• Disciplina: 006.6
• Soggetto topico: Image processing - Digital techniques - Mathematics
• Altri autori: OkabeTakahiro
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Introduction -- Part 1. Transient Imaging and Processing -- Chapter 1. Transient Imaging -- 1.1. Introduction -- 1.2. Mathematical formulation -- 1.2.1. Analysis of transient light transport propagation -- 1.2.2. Sparsity of the impulse response function T (x, t) -- 1.3. Capturing light in flight -- 1.3.1. Single-photon avalanche diodes (SPAD) -- 1.4. Applications -- 1.4.1. Range imaging -- 1.4.2. Material estimationand classification -- 1.4.3. Light transport decomposition -- 1.5. Non-line-of-sight imaging -- 1.5.1. Backprojection -- 1.5.2. Confocal NLOS and the light-cone transform -- 1.5.3. Surface-based methods -- 1.5.4. Virtualwaves and phasorfields -- 1.5.5. Discussion -- 1.6. Conclusion -- 1.7. References -- Chapter 2. Transient Convolutional Imaging -- 2.1. Introduction -- 2.2. Time-of-flight imaging -- 2.2.1. Correlationimage sensors -- 2.2.2. Convolutional ToF depth imaging -- 2.2.3. Multi-path interference -- 2.3. Transient convolutional imaging -- 2.3.1. Global convolutional transport -- 2.3.2. Transient imaging using correlation image sensors -- 2.3.3. Spatio-temporal modulation -- 2.4. Transient imagingin scatteringmedia -- 2.5. Present andfuturedirections -- 2.6. References -- Chapter 3. Time-of-Flight and Transient Rendering -- 3.1. Introduction -- 3.2. Mathematical modeling -- 3.2.1. Mathematical modeling for time-of-flight cameras -- 3.3. How to render time-of-flight cameras? -- 3.3.1. Challenges and solutions in time-of-flight rendering -- 3.4. Open-sourceimplementations -- 3.5. Applicationsof transient rendering -- 3.6. Future directions -- 3.7. References -- Part 2. Spectral Imaging and Processing -- Chapter 4. Hyperspectral Imaging -- 4.1. Introduction -- 4.2. 2D (raster scanning) architectures -- 4.2.1. Czerny-Turner grating spectrometers. ; 4.2.2. Transmission grating/prism spectrometers -- 4.2.3. Coded aperture spectrometers -- 4.2.4. Echelle spectrometers -- 4.3. 1D scanning architectures -- 4.3.1. Dispersive spectrometers -- 4.3.2. Interferometric methods -- 4.3.3. Interferometric filter methods -- 4.3.4. Polarization-based filter methods -- 4.3.5. Active illumination methods -- 4.4. Snapshot architectures -- 4.4.1. Bowen-Walravenimage slicer -- 4.4.2. Image slicing and imagemapping -- 4.4.3. Integral field spectrometry with coherent fiber bundles (IFS-F) -- 4.4.4. Integral field spectroscopy with lenslet arrays (IFS-L) -- 4.4.5. Filter array camera (FAC) -- 4.4.6. Computed tomography imaging spectrometry (CTIS) -- 4.4.7. Coded aperture snapshot spectral imager (CASSI) -- 4.5. Comparisonof snapshot techniques -- 4.5.1. The disadvantagesof snapshot -- 4.6. Conclusion -- 4.7. References -- Chapter 5. Spectral Modeling and Separation of Reflective-Fluorescent Scenes -- 5.1. Introduction -- 5.2. RelatedWork -- 5.3. Separation of reflection and fluorescence -- 5.3.1. Reflection and fluorescence models -- 5.3.2. Separation using high-frequency illumination -- 5.3.3. Discussion on the illumination frequency -- 5.3.4. Error analysis -- 5.4. Estimating the absorption spectra -- 5.5. Experiment results and analysis -- 5.5.1. Experimental setup -- 5.5.2. Quantitative evaluation of recovered spectra -- 5.5.3. Visual separation and relighting results -- 5.5.4. Separation by using high-frequency filters -- 5.5.5. Ambient illumination -- 5.6. Limitations and conclusion -- 5.7. References -- Chapter 6. Shape from Water -- 6.1. Introduction -- 6.2. Related works -- 6.3. Light absorption in water -- 6.4. Bispectral light absorption for depth recovery -- 6.4.1. Bispectral depth imaging -- 6.4.2. Depth accuracy and surface reflectance -- 6.5. Practical shape from water. ; 6.5.1. Non-collinear/perpendicular light-camera configuration -- 6.5.2. Perspective camera with a point source -- 6.5.3. Non-ideal narrow-band filters -- 6.6. Co-axial bispectral imaging system and experiment results -- 6.6.1. System configuration and calibration -- 6.6.2. Depth and shape accuracy -- 6.6.3. Complex static and dynamic objects -- 6.7. Trispectral light absorption for depth recovery -- 6.7.1. Trispectral depth imaging -- 6.7.2. Evaluation on the reflectance spectra database -- 6.8. Discussions -- 6.9. Conclusion -- 6.10. References -- Chapter 7. Far Infrared Light Transport Decomposition and Its Application for Thermal Photometric Stereo -- 7.1. Introduction -- 7.1.1. Contributions -- 7.2. Related work -- 7.2.1. Light transport decomposition -- 7.2.2. Computational thermal imaging -- 7.2.3. Photometric stereo -- 7.3. Far infrared light transport -- 7.4. Decomposition and application -- 7.4.1. Far infrared light transport decomposition -- 7.4.2. Separating the ambient component -- 7.4.3. Separating reflection and radiation -- 7.4.4. Separating diffuse and global radiations -- 7.4.5. Other options -- 7.4.6. Thermal photometric stereo -- 7.5. Experiments -- 7.5.1. Decomposition result -- 7.5.2. Surface normal estimation -- 7.6. Conclusion -- 7.7. References -- Chapter 8. Synthetic Wavelength Imaging: Utilizing Spectral Correlations for High-Precision Time-of-Flight Sensing -- 8.1. Introduction -- 8.2. Synthetic wavelength imaging -- 8.3. Synthetic wavelength interferometry -- 8.4. Synthetic wavelength holography -- 8.4.1. Imaging around corners with synthetic wavelength holography -- 8.4.2. Imaging through scattering media with synthetic wavelength holography -- 8.4.3. Discussion and comparison with the state of the art -- 8.5. Fundamental performance limits of synthetic wavelength imaging -- 8.6. Conclusion and future directions. ; 8.7. Acknowledgment -- 8.8. References -- Part 3. Polarimetric Imaging and Processing -- Chapter 9. Polarization-Based Shape Estimation -- 9.1. Fundamental theory of polarization -- 9.2. Reflection component separation -- 9.3. Phase angle of polarization -- 9.4. Surface normal estimation from the phase angle -- 9.5. Degree of polarization -- 9.6. Surface normal estimation from the degree of polarization -- 9.7. Stokes vector -- 9.8. Surface normal estimation from the Stokes vector -- 9.9. References -- Chapter 10. Shape from Polarization and Shading -- 10.1. Introduction -- 10.2. Related works -- 10.2.1. Shading and polarization fusion -- 10.2.2. Shape estimation under uncalibrated light sources -- 10.3. Problem setting and assumptions -- 10.4. Shading stereoscopic constraint -- 10.5. Polarization stereoscopic constraint -- 10.6. Normal estimation with two constraints -- 10.6.1. Algorithm 1: Recovering individual surface points -- 10.6.2. Algorithm 2: Recovering shape and light directions -- 10.7. Experiments -- 10.7.1. Simulation experiments with weights for two constraints -- 10.7.2. Real-world experiments -- 10.8. Conclusion and future works -- 10.9. References -- Chapter 11. Polarization Imaging in the Wild Beyond the Unpolarized World Assumption -- 11.1. Introduction -- 11.2. Mueller calculus -- 11.3. Polarizing filters -- 11.3.1. Linear polarizers -- 11.3.2. Reflectors -- 11.4. Polarization imaging -- 11.5. Image formation model -- 11.5.1. Partially linearly polarized incident illumination -- 11.5.2. Unpolarized incident illumination -- 11.5.3. Discussion -- 11.6. Polarization imaging reflectometry in the wild -- 11.7. Digital Single-Lens Reflex (DSLR) setup -- 11.7.1. Data acquisition -- 11.7.2. Calibration -- 11.7.3. Polarization processing pipeline -- 11.8. Reflectance recovery -- 11.8.1. Surface normal estimation. ; 11.8.2. Diffuse albedo estimation -- 11.8.3. Specular component estimation -- 11.9. Results and analysis -- 11.9.1. Results -- 11.9.2. Discussion and error analysis -- 11.10. References -- Chapter 12. Multispectral Polarization Filter Array -- 12.1. Introduction -- 12.2. Multispectral polarization filter array with a photonic crystal -- 12.3. Generalization of imaging and demosaicking with multispectral -- 12.4. Demonstration -- 12.5. Conclusion -- 12.6. References -- List of Authors -- Index -- EULA.
• Sommario/riassunto: Most cameras are inherently designed to mimic what is seen by the human eye: they have three channels of RGB and can achieve up to around 30 frames per second (FPS). However, some cameras are designed to capture other modalities: some may have the ability to capture spectra from near UV to near IR rather than RGB, polarimetry, different times of light travel, etc. Such modalities are as yet unknown, but they can also collect robust data of the scene they are capturing. This book will focus on the emerging computer vision techniques known as computational imaging. These include capturing, processing and analyzing such modalities for various applications of scene understanding.
• Titolo autorizzato: Computational Imaging for Scene Understanding
• ISBN: 1-394-28443-8; 1-394-28441-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910877776003321
• Lo trovi qui: Univ. Federico II