Surface Science for Vision and Graphics - CiteSeerX

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Surface Science for Vision and Graphics Kristin J. Dana Rutgers University Electrical and Computer Engineering Department [email protected] www.ece.rutgers.edu/ kdana I. N OVEL I MAGING

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C APTURING S URFACE G EOMETRY

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A PPEARANCE

Capturing surface appearance is a challenging task because reflectance varies as a function of viewing and illumination direction. In addition, most real world surfaces have a textured appearance, so reflectance also varies spatially. We present a texture camera which can conveniently capture spatially varying reflectance on a surface. Unlike other bidirectional imaging devices, the design eliminates the need for complex mechanical apparatus to move the light source and camera over a hemisphere of possible directions. To facilitate fast and convenient measurement, the device uses a curved mirror so that multiple views of the same surface point are captured simultaneously. Simple planar motions of the imaging components also enable change of illumination direction and region imaging. The original concept is described in [6], and [7] fully describes the current prototype of this device, imaging results, and an analysis of the important optical imaging properties. One of the imaging properties that we investigate is the spatial resolution effects due to changes in the illumination aperture. The finite size of the illumination aperture leads to a ray-bundle as opposed to a single ray. The cone angle of this incident illumination will vary depending on the position this ray bundle strikes the parabolic mirror. As in microscopy, the cone angle of incident illumination, or the numerical aperture, determines spatial resolution. We analyze the resolution for the texture camera and its dependence on illumination direction in [7]. TOP VIEW

Illumination Source

y

Collimating Lens Assembly

Aperture

x

Illumination

x-z translation stage

Collimating Lens

Mirror

Illumination Aperture

x-y-z translation stage

Sample

Beam Splitter Camera Beam Splitter

Surface Point Camera

Off-axis Concave Parabolic Mirror

Fig. 1. Texture camera (texcam). The surface point is imaged by a CCD video camera observing an off-axis concave parabolic

mirror for simultaneous observation of a large range of viewing directions. Illumination direction is controlled by an aperture, i.e. translations of the aperture in the x-z plane cause variations in the illumination angle incident on the surface point. The device achieves illumination/viewing direction variations using a simple translations of the illumination aperture instead of complex gonioreflectometer equipment. Measurements of bidirectional texture are accomplished by translating the mirror in the x-y plane. The position of the specularity is detected in the observed camera images and used to estimate the surface normal.

II. C APTURING S URFACE G EOMETRY

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S PECULARITIES

The texture camera can also be used for shape recovery by detecting the position of the specularity in the observed camera for a given illumination aperture position. Details of this application for our texture camera is provided in [8]. Because multiple viewing directions are imaged simultaneously, the specular direction can be easily detected. Furthermore, this detected specular position indicates the surface normal at that point. The surface normal is computed by mapping the position of the detected specularity to the viewing angle and mapping the position of the illumination aperture to the illumination angle. This mapping is obtained using the known parabolic shape of the mirror.

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Fig. 2. The coin image reconstructed from the lambertian surface assumption applied to the estimated surface normals. From left column to right column, βL = 0◦ , 120◦ , 240◦ and for each γL = 45◦ .

Fig. 3. Texture images for the coin. Let the viewing direction be denoted by γv , βv . Let the illumination direction be denoted by γl , βl . Top left: γv = 15◦ , βv = 90◦ , γl = 3.5◦ , βl = −90◦ . Top right: γv = 12◦ , βv = 90◦ , γl = 6.38◦ , βl = 90◦ . Bottom left: γv = 0◦ , βv = −90◦ , γl = 15.3◦ , βl = −90◦ . Bottom Right: γv = 16◦ , βv = −90◦ , γl = 9.3◦ , βl = 90◦ .

Capturing high-quality digital models is a central issue for research in computer vision and graphics. With ubiquitous use of the internet, there are an increasing number of applications for such models such as e-commerce, historical archiving, and virtual reality. Acquiring depth maps with three-dimensional scanning is a main component of systems that capture digital object models. There are several methods for range scanning or 3D scanning which are based on time of flight, depth from defocus, and project-light triangulations. Typical methods of 3D scanning cannot fully represent the appearance of an object. There are three main problems with capturing surface detail with typical scanning methods: 1) Depth information alone is not sufficient. Our device simultaneously captures fine scale geometry and bidirectional reflectance and texture. 2) High spatial resolution is needed for fine scale geometry. Our device captures detail on the order of 0.1 mm. 3) Typical scanning methods don’t work well for specular objects. A distinguishing aspect of our method is that it is designed for specular surfaces, unlike many methods (e.g. laser scanning) which cannot handle highly specular objects. For an example result, the bidirectional texture and the fine-scale surface shape is measured for a coin. Figure 2 shows the recovered geometry of the coin surface rendered using a simple lambertian shading model. The bidirectional texture function measured for the coin sample is illustrated in Figure 3 for a sampling of imaging parameters. Notice that this measured texture reveals a very realistic metallic reflectance that has some spatial variation due to the coin imperfections. III. C OMPUTATIONAL S URFACE R EPRESENTATIONS : A PPLICATION

TO

S KIN T EXTURE

Modeling of human skin is an important task for both computer vision and graphics. For computer vision, accurate models of skin texture can greatly assist algorithms for human face recognition or facial feature tracking. In computer graphics, facial animation is an important problem which necessitates reliable skin texture models. In addition to computer vision and graphics, accurate skin models are useful in dermatology and several industrial fields. In dermatology, these skin models can be used to develop methods for computer-assisted diagnosis of skin disorders, while in the pharmaceutical industry, quantification is useful when applied to measuring healing progress.

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Fig. 4. Skin texture in the lip region of the face, as the illumination source is repositioned. The appearance of the skin surface varies significantly, yet only the light direction is changed.

(a) Shiny appearance

Translucency

Slightly elevated border

(b) Fig. 5. Basal cell carcinoma. (a) As the illumination is repositioned, the appearance captures better features specific to this disorder, i.e. slightly elevated rolled border, with a shiny and translucent appearance. (b) Two detailed basal cell carcinoma images, as the illumination is repositioned. The need for bidirectional imaging is evident from these two images, when one considers that while the left image shows that the skin surface is translucent, the right image captures the shiny aspect of the affected skin, as well as the slightly elevated border of the lesion.

In [5], we present a novel skin texture database called Rutgers Skin Texture Database [3], which contains more than 3500 skin texture images. Each skin surface in the database is represented by a set of texture images, captured under different combinations of imaging parameters. The database has two components: (1) a normal skin component for recognition and rendering in computer vision and graphics; (2) a skin disorder component for quantitative imaging in dermatology. Figure 4 and Figure 5 show example images from this database. In addition to the measurements and the bidirectional imaging methods employed to construct the database, we develop two texture modeling methods which we employ for texture recognition in two contexts: classification of skin disorders, and classification of facial regions (e.g. forehead vs. chin). Both models are image based, and support recognition methods that have several desirable properties. Specifically, a single image can be used for fast non-iterative recognition, the illumination and viewing directions of the images need not be known, and no image alignment is needed. Details of our modeling methods can be found in [1], [2], [4], [5]. R EFERENCES [1] O. G. Cula and K. J. Dana. Compact representation of bidirectional texture functions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, I:1041–1047, December 2001. [2] O. G. Cula and K. J. Dana. 3D texture recognition using bidirectional feature histograms. to appear in International Journal of Computer Vision, 2003. [3] O. G. Cula, K. J. Dana, F. P. Murphy, and B. K. Rao. http://www.caip.rutgers.edu/rutgers texture/. 2003. [4] O. G. Cula, K. J. Dana, F.P. Murphy, and R.K. Rao. Bidirectional imaging and modeling of skin texture. Texture 2003, held in conjunction with International Conference on Computer Vision, October 2003. [5] O. G. Cula, K. J. Dana, F.P. Murphy, and R.K. Rao. Skin texture modeling. to appear in International Journal of Computer Vision, 2003. [6] K. J. Dana. Brdf/btf measurement device. International Conference on Computer Vision, 2:460–6, July 2001. [7] K. J. Dana and J. Wang. Device for convenient measurement of spatially varying bidirectional reflectance. Journal of the Optical Society of America A, pages 1–15, December 2003. [8] J. Wang and K. J. Dana. A novel approach for texture shape recovery. International Conference on Computer Vision, pages 1374–1380, October 2003.