Shape and Refractive Index from Single-View Spectro-Polarimetric Images

Huynh, Cong Phuoc; Robles-Kelly, Antonio; Hancock, Edwin R.

doi:10.1007/s11263-012-0546-3

Cited by 46 publications

(26 citation statements)

References 49 publications

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“…In particular, out-of-plane rotations drastically change the 2D appearance of a face, as they cause portions of the face to be occluded. A common approach to mitigate the effects of pose variations is to build a 3D face model from a 2D image(s) so that synthetic 2D face images can then be rendered at designated poses (e.g., [23]- [25]). …”

Section: B 3d Face Modelsmentioning

confidence: 99%

Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

Best-Rowden

Han

Otto

et al. 2014

IEEE Trans.Inform.Forensic Secur.

191

127

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Abstract-As face recognition applications progress from constrained sensing and cooperative subjects scenarios (e.g., driver's license and passport photos) to unconstrained scenarios with uncooperative subjects (e.g., video surveillance), new challenges are encountered. These challenges are due to variations in ambient illumination, image resolution, background clutter, facial pose, expression, and occlusion. In forensic investigations where the goal is to identify a "person of interest," often based on low quality face images and videos, we need to utilize whatever source of information is available about the person. This could include one or more video tracks, multiple still images captured by bystanders (using, for example, their mobile phones), 3D face models constructed from image(s) and video(s), and verbal descriptions of the subject provided by witnesses. These verbal descriptions can be used to generate a face sketch and provide ancillary information about the person of interest (e.g., gender, race, and age). While traditional face matching methods generally take a single media (i.e., a still face image, video track, or face sketch) as input, our work considers using the entire gamut of media as a probe to generate a single candidate list for the person of interest. We show that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3D face models, and incorporation of quality measures for fusion and video frame selection.

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Section: B 3d Face Modelsmentioning

confidence: 99%

Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

Best-Rowden

Han

Otto

et al. 2014

IEEE Trans.Inform.Forensic Secur.

191

127

View full text Add to dashboard Cite

show abstract

“…A variety of work seeks to augment polarisation with an additional shape-from-X cue. Huynh et al [14] extended their earlier work to use multispectral measurements to estimate both shape and refractive index. Drbohlav and Sara [10] showed how the Bas-relief ambiguity [6] in uncalibrated photometric stereo could be resolved using polarisation.…”

Section: Related Workmentioning

confidence: 99%

Depth From a Polarisation + RGB Stereo Pair

Zhu

Smith

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Estimated specular mask Corrected normal Depth from stereo (a) Setup and Input (b) Local disambiguation Estimated Albedo Estimated Shape (c) Output Section 4 Section 5 Section 6 Figure 1: Overview: From a stereo pair of one polarisation image and one RGB image (a) we merge stereo depth with polarisation normals using a higher order graphical model (b) before estimating an albedo map and the final geometry (c). AbstractIn this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shapefrom-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to estimate diffuse albedo. By extending a previous shape-from-polarisation method to the perspective case, we show how to compute dense, detailed maps of absolute depth, while retaining a linear formulation. We show that our hybrid method is able to recover dense 3D geometry that is superior to state-of-the-art shape-from-polarisation or two view stereo alone.

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“…Similarly, Atkinson and Hancock [12] used polarisation normals to segment an object into patches, simplifying stereo matching. Huynh et al [13] extended their earlier work to use multispectral measurements to estimate both shape and refractive index. There have been a number of attempts to augment polarisation cues with calibrated, Lambertian photometric stereo, e.g.…”

Section: Related Workmentioning

confidence: 99%

Linear Depth Estimation from an Uncalibrated, Monocular Polarisation Image

Smith

Ramamoorthi

Tozza

2016

Computer Vision – ECCV 2016

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Abstract. We present a method for estimating surface height directly from a single polarisation image simply by solving a large, sparse system of linear equations. To do so, we show how to express polarisation constraints as equations that are linear in the unknown depth. The ambiguity in the surface normal azimuth angle is resolved globally when the optimal surface height is reconstructed. Our method is applicable to objects with uniform albedo exhibiting diffuse and specular reflectance. We extend it to an uncalibrated scenario by demonstrating that the illumination (point source or first/second order spherical harmonics) can be estimated from the polarisation image, up to a binary convex/concave ambiguity. We believe that our method is the first monocular, passive shape-from-x technique that enables well-posed depth estimation with only a single, uncalibrated illumination condition. We present results on glossy objects, including in uncontrolled, outdoor illumination.

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Shape and Refractive Index from Single-View Spectro-Polarimetric Images

Cited by 46 publications

References 49 publications

Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

Depth From a Polarisation + RGB Stereo Pair

Linear Depth Estimation from an Uncalibrated, Monocular Polarisation Image

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