We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image. A handheld camera pointed at the display can receive not only the display image, but also the underlying message. These active scenes are fundamentally different from traditional passive scenes like QR codes because image formation is based on display emittance, not surface reflectance. Detecting and decoding the message requires careful photometric modeling for computational message recovery. Unlike standard watermarking and steganography methods that lie outside the domain of computer vision, our message recovery algorithm uses illumination to optically communicate hidden messages in real world scenes. The key innovation of our approach is an algorithm that performs simultaneous radiometric calibration and message recovery in one convex optimization problem. By modeling the photometry of the system using a camera-display transfer function (CDTF), we derive a physics-based kernel function for support vector machine classification. We demonstrate that our method of optimal online radiometric calibration (OORC) leads to an efficient and robust algorithm for computational messaging between nine commercial cameras and displays.
From a computer vision point of view, the imaging process can be divided into two main components: photometry and geometry. The geometric aspects of image formation have been addressed to some extent in the watermarking community, and many techniques have been developed for robustness to geometric changes during the imaging process such as scaling, rotations, translations and general homography transformations @cite_9 @cite_33 @cite_30 @cite_13 @cite_14 @cite_27 @cite_15 . However, the photometry of imaging has largely been ignored. The rare mention of photometric effects @cite_16 @cite_39 in the watermarking literature doesn't define photometry with respect to illumination; instead photometric effects are defined as lossy compression, denoising, noise addition and lowpass filtering''. In fact, photometric attacks are sometimes defined as jpeg compression @cite_30 .
@cite_30 @cite_14 @cite_33 @cite_9 @cite_39 @cite_27 @cite_15 @cite_16 @cite_13
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This paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques. We begin with taxonomy of active and passive shape acquisition techniques. Then we describe the methodical background of photometric 3D reconstruction, define the canonical setting of photometric stereo (Lambertian surface reflectance, parallel incident light, known illumination direction, known surface albedo, absence of cast shadows), discuss the 3D reconstruction of surfaces from local gradients, summarize the concept of the bidirectional reflectance distribution function (BRDF), and outline several important empirically and physically motivated reflectance models. We provide a detailed treatment of several generalizations of the canonical setting of photometric stereo, namely non-distant light sources, unknown illumination directions, and, in some detail, non-Lambertian surface reflectance functions. An important special case is purely specular reflections, where an extended light source allows capturing a surface that consists of perfectly specular surface patches. Linear combinations of purely Lambertian and purely specular reflectance components are favorably used for reconstructing smooth surfaces and also human skin. Nonuniform surface reflectance properties are estimated based on a simultaneous 3D reconstruction and determination of the locally variable parameters of the reflectance function based on a multitude of images. Assuming faceted surfaces, the effective resolution of the 3D reconstruction result can be increased to some extent beyond that of the underlying images. Other approaches separate specular and diffuse reflectance components based on polarization data or color information. The specular reflections can be used additionally to estimate the direction from which the surface is illuminated. Finally, we describe methods to combine photometric 3D reconstruction techniques with active and passive triangulation-based approaches. The paper addresses the problem of camera tracking, which denotes the continuous image-based computation of a camera’s position and orientation with respect to a reference frame. The method aims at regular cameras, which means that 3D-3D registration methods applicable to RGB-D cameras are not an option. The tracked frame contains only 2D information, thus requiring a solution to the absolute pose or 2D-3D registration problem. While traditional solutions to camera tracking [3] rely on sparse feature correspondences, the community has recently seen a number of direct photometric registration methods such as [8] and [1]. [1] is conceptually similar to [8], however gains computational efficiency by reducing the computation from dense to semi-dense regions that correspond to a thresholded edge-map of the image. Photometric methods have the more general advantage of compensating for appearance variations caused by perspective view-point changes, whereas classical sparse methods often rely on static feature descriptors only (providing at most rotation and scale invariant properties [5, 6]). However, photometric registration techniques inherently suffer from the disability to overcome large disparities, where large sometimes means even just a couple of pixels [7]. Many photometric registration techniques therefore depend on pyramidal subsampling schemes in order to alleviate this problem. The goal of the present paper is a novel 2D-3D registration paradigm for semi-dense depth maps that relies on the Iterative Closest Point (ICP) technique, and thus a reintroduction of geometric error minimization as a valid alternative for real-time monocular camera tracking in the case of semi-dense features. An example semi-dense depth map is indicated in Figure 1. In comparison to photometric registration techniques, our ICP technique has the conceptual advantage of requiring neither isotropic enlargement of the employed semi-dense regions, nor pyramidal subsampling. The work is in line with [2], Tomono [9], and Klein and Murray [4], which already attempt curve or edge registration in 2D using ICP. Based on a hypothesized relative pose, the basic idea consists of warping a reference curve into the tracked image based on a prior 3D model or depth (in our case semi-dense) inside a reference frame. From a mathematical point of view, our idea may be formulated as follows. Let An algorithm for image registration and mosaicing on underwater sonar image sequences characterised by a high noise level, inhomogeneous illumination and low frame rate is presented. Imaging geometry of acoustic cameras is significantly different from that of pinhole cameras. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration can be obtained via a projective transformation. For an acoustic camera, it is shown that, under the same conditions, an affine transformation is a good approximation. A novel image fusion method, which maximises the signal-to-noise ratio of the mosaic image is proposed. The full procedure includes illumination correction, feature based transformation estimation, and image fusion for mosaicing. We present an algorithm and the associated capture methodology to acquire and track the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured light, or silhouettes. Multispec- tral photometric stereo is an attractive alternative because it can recover a dense normal field from an un-textured surface. We show how to capture such data and register it over time to generate a single deforming surface. Experiments were performed on video sequences of un- textured cloth, filmed under spatially separated red, green, and blue light sources. Our first finding is that using zero- depth-silhouettes as the initial boundary condition already produces rather smoothly varying per-frame reconstructions with high detail. Second, when these 3D reconstructions are augmented with 2D optical flow, one can register the first frame's reconstruction to every subsequent frame.