Sharp discontinuities in a depth map, ordepth edges, are extremely useful 2.5D

entities. The ability to localize and highlight depth dis-continuities makes it possible to produce stylized pho-

tography and videos, which areuseful in technical applications suchas medical imaging1 and nonphoto-realistic rendering (NPR).2 In addi-tion, depth edges are importantlow-level features for many com-puter vision tasks, such as visualrecognition.3

Figure 1 shows an example ofautomatic depth-edge-based styl-ization, where a car engine isimaged under diffused lighting andshown in a stylized form, accentu-ating the shape-conveying depthedges even when they are low-con-trast edges, while deemphasizingvisual clutter, like those introduced

by rust. The resulting image resembles drawings fromcar repair manuals with an added degree of realism andauthenticity as it was captured from a real engine.

When a rich 3D model of a scene is available, identi-fying and localizing depth discontinuities is a relativelywell-understood task. Extending this approach to realscenes by first capturing 3D models, however, remainsdifficult. Our multiflash imaging method bypasses 3Dgeometry acquisition and directly acquires depth edgesfrom images. In the place of expensive, elaborate equip-ment for geometry acquisition,4 we use a camera withmultiple strategically positioned flashes. Instead of hav-ing to estimate the full 3D coordinates of points in thescene (using, for example, 3D cameras) and then lookfor depth discontinuities, our technique reduces the gen-eral 3D problem of depth edge recovery to one of 2Dintensity edge detection. Our method could, in fact, helpimprove current 3D cameras, which tend to produceincorrect results near depth discontinuities.

Exploiting the imaging geometry for rendering pro-vides a simple and inexpensive solution for creating styl-ized images from real scenes. We believe that ourcamera will be a useful tool for professional artists andphotographers, and we expect that it will also let theaverage user easily create stylized imagery.

Creating stylized imagery from photographs, ratherthan 3D geometric models, has recently received a greatdeal of attention. The majority of the available techniquesfor image stylization involves processing a single image asthe input, using image processing and computer visiontechniques like morphological operations, image seg-mentation, and edge detection. Some methods aim forstylized depiction, while others enhance legibility. Ani-mators have used interactive techniques for stylized ren-dering—such as rotoscoping with real video footage—tocreate animation like the groundbreaking Waking Life(http://www.wakinglifemovie.com/) and Avenue Amy(http://www.curiouspictures.com/shows/clips/ave_amy.html. Multiflash imaging has the potential to automatetasks where meticulous manual operation was previous-ly required and to make it easier for filmmakers to blurthe line between photographic and stylized material.

Emerging Technologies

A method for capturing

geometric features of real-

world scenes relies on a

simple capture setup

modification. The system

might conceivably be

packaged into a portable,

self-contained device.

Ramesh RaskarMitsubishi Electric Research Labs

Kar-Han TanEpson Palo Alto Lab

Rogerio S. Feris and Matthew TurkUniversity of California, Santa Barbara

James KoblerHarvard Medical School

Jingyi YuMassachusetts Institute of Technology

Harnessing Real-World Depth Edgeswith MultiflashImaging

32 January/February 2005 Published by the IEEE Computer Society 0272-1716/05/$20.00 © 2005 IEEE

(a) (b)

(c) (d)

1 Car engine(a) imagedunder diffusedlighting, (b) stylizedusing depthedges comput-ed with ourtechnique, (c) withincreasedbrightness, (d) and withhistogramequalized.

BasicsOur multiflash imaging method

is motivated by the observationthat when a flashbulb (close to thecenter of projection of the camera)illuminates a scene during imagecapture, thin slivers of cast shadoware created at depth discontinu-ities. Moreover, the shadows’ posi-tions are determined by the relativeposition of the camera and theflashbulb: When the flashbulb is onthe right, shadows are created onthe left, and so on. Thus, if we canshoot a sequence of images inwhich different light sources illu-minate the subject from variouspositions, we can use the shadowsin each image to assemble a depthedge map.

Imaging geometryTo capture the intuitive notion of

how the positions of the cast shad-ows are dependent on the relativeposition of the camera and lightsource, we examine the imaginggeometry, illustrated in Figure 2.Adopting a pinhole camera model,the projection of the point lightsource at Pk is at pixel ek on the imaging sensor. We callthis image of the light source the light epipole. Theimages of (the infinite set of) light rays originating at Pk

are in turn called the epipolar rays originating at ek. Wethen define the term depth edges as the 2D images ofdepth discontinuities.

Removing and detecting shadowsOur approach to reliably remove and detect shadows

in the images is to strategically position lights so thatevery point in the scene that is shadowed in some imageis also imaged without being shadowed in at least oneother image. We can achieve this by placing lights sothat for every light there is another light on the camera’sopposite side so that all depth edges are illuminatedfrom two sides. Also, by placing the lights close to thecamera, we minimize changes across images due toeffects other than shadows.

To detect shadows in each image, we first computea shadow-free image, which can be approximated withthe maximum composite image (MAX image), whichis an image assembled by choosing at each pixel themaximum intensity value from among the image set.We then compare the shadow-free image with theindividual shadowed images. In particular, for eachshadowed image we compute the ratio image by per-forming a pixel-wise division of the intensity by that ofthe MAX image. The ratio image is close to 1 at pixelsthat are not shadowed, and close to 0 at pixels that areshadowed. This accentuates the shadows and alsoremoves intensity transitions due to surface materialchanges.

Algorithm Codifying these ideas, we arrive at the following

algorithm: Given n light sources positioned at P1, P2 … Pn,

■ Capture n pictures Ik, k = 1…n with a light source at Pk

■ For all pixels x, Imax(x) = maxk(Ik(x)), k = 1 … n■ For each image k, create ratio image Rk, where Rk (x)

= Ik(x)/Imax(x)■ For each image Rk, traverse each epipolar ray from

epipole ek, find pixels y with step edges with negativetransition, and mark pixels y as a depth edge

Building multiflash camerasWe built a multiflash camera using a 4-megapixel

Canon Powershot G2, as shown in Figure 2. A micro-controller board triggers sequentially the four flashesmounted around the camera. The board synchronizesthe flashes to the image capture process by sensing theflash trigger signal from the camera hot shoe.

We have published elsewhere descriptions of moreadvanced prototypes and discussions on the finer pointsof multiflash imaging.2 In this article we shall illustratethe usefulness of multiflash imaging in a number of dif-ferent applications: NPR, medical imaging, biologicalillustrations, and visual recognition.

NPR with depth edgesMultiflash imaging can address two important issues

in NPR: detecting shape contours that should beenhanced and identifying features that should be sup-

IEEE Computer Graphics and Applications 33

Camera

Object

Shadow

Image

Pk

Ik

eke1 e2

e3

Camerahot shoe

Multiplexingmicrocontroller

Flashes

(a)

(b) (c)

Shadowedby 1 butnot by2 and 3

2 Building a multiflash camera. (a) Imaging geometry. (b) Hardware schematic. (c) Prototypebased on a Canon G2 camera.

pressed.5-7 Depth edges correspond to physical objectshape contours and silhouettes. In addition to provid-ing the 2D location of such contours, depth edges arealso signed in the sense that at a depth edge we knowwhich side is the foreground (positive sign) and back-ground (negative sign), since we know where the shad-ow appeared. This 2.5D nature of depth edges allowsthe design of rendering styles not possible or difficult inthe absence of high-quality 3D models. Figure 3 showsexamples of rendering styles.

Some static illustrations demonstrate action—forexample, changing oil in a car—by making moving partsin the foreground brighter. Foreground detection via

intensity-based schemes, however, is difficult when thecolors are similar and texture is lacking—for example,detecting hand gesture in front of other skin-coloredparts (see Figure 4). We take two separate sets of mul-tiflash shots, without and with the hand in front of theface, to capture the reference and changed scene. Wenote that any change in a scene is bounded by new depthedges introduced. Without explicitly detecting fore-ground, we highlight interiors of regions that contributeto new depth edges. We create a gradient field wherepixels marked as depth edges in the changed scene, butnot in reference, are assigned a unit magnitude gradi-ent. The orientation matches the image space normalto the depth edge. The gradient at other pixels is zero.The reconstructed image from 2D integration is a pseu-do depth map. We threshold this map at 1.0 to get theforeground mask, which is brightened.

Medical applications In many medical applications like minimally invasive

surgery with endoscopes, it’s often difficult to captureimages that convey the 3D shape of the organs and tis-sues under examination. Perhaps for the same reason,medical textbooks and articles frequently resort to hand-drawn illustrations when depicting organs and tissues.In the sections that follow we show the use of multiflashimaging to address this problem.

A multiflash imaging system captures additional shapeinformation compared to traditional cameras and there-fore has the potential to enhance visualization and doc-umentation in surgery and pathology. The raw shadowedimages can be processed to create finely detailed imagescomparable to medical illustrations or to enhance edgefeatures for quantitative measurement. Alternatively, theshadowed images can be combined to generate shadow-free images, which are often desirable for documentationof specimens in the field of pathology.

SurgeryMost endoscopic procedures are now performed with

the surgeon observing monitor displays rather than theactual tissue. This affords the possibility of interposingimage manipulation steps, which, if they can run closeto real time, can enhance the surgeon’s understanding.Depth perception is an obvious deficit when usingmonocular endoscopes. Researchers have explored 3Dimaging using stereoscopic methods explored withmixed results. A 1999 study found that stereoendoscopicviewing was actually more taxing on the surgeons thanmonocular viewing.8 Structured lighting is also underinvestigation as a means for calibrating endoscopicimages, but this technique does not enhance 3D struc-tures in real time.9

Application of enhanced shadow information to aug-ment surgical perception has not been exploited previ-ously. Shadows normally provide clues about shape, butthe circumferential (ring light) illumination provided bymost laparoscopes diminishes this information. Simi-larly, the intense multisource lighting used for open pro-cedures tends to reduce strong shadow effects. Loss ofshadow information might make it difficult to appreciatethe shapes and boundaries of structures and thus more

Emerging Technologies

34 January/February 2005

3 NPR with depth edges. (a) Bone scene, renderedwith signed contour style. (b) Flower scene, renderedby removing textures and overlaying depth edges,(bottom right) with contour colors assigned with fore-ground object colors, (bottom left) with signed edgeshighlighted by modulating image intensity arounddepth edges.

(a)

(b)

4 Changedetection(counterclock-wise from topleft): referenceimage, changedimage, detectedchanged region,and stylizedscene changedepiction.

difficult to estimate their extent and size. It could alsomake it more difficult to spot a small protrusion, such asan intestinal polyp, if no clear color differences exist. Theability to enhance the borders of lesions so that they canbe measured will become more useful as endoscopesbegin to incorporate calibrated sizing features.

Multiflash imaging with endoscopesThe simplest way to implement multiflash imaging in

endoscopes is to use multiple instruments, where insteadof inserting one endoscope, three are inserted. The mid-dle instrument acts as the camera while the two on theside act as light sources. By synchronizing the lightsources with the image-capture process for the middleendoscope, the entire setup would act as a multiflashcamera. While this approach might involve insertingmore imaging instruments, it’s a way to systematicallyilluminate the subject and potentially reduce the amountof adjustments required during an operation to produceimages that convey the required 3D information.

In many scenarios, it’s more useful to have a singleinstrument capable of multiflash imaging. For example,in situations that require flexible endoscopes, it couldbe difficult or impossible to insert and align multipleflexible light sources with the endoscope. Fortunately,it’s possible to implement multiflash imaging on a singleinstrument with our method because the light sourcescan be placed near the camera.1 This allows for compactdesigns suited for use in tightly constrained spaces,unlike many traditional 3D shape recovery methodswhere the imaging apparatuses must be placed at largedistances apart.

For proof-of-concept endoscopic imaging, we tookadvantage of the illumination system in the Wolf Lumi-na, a standard rod lens laryngeal endoscope. The illu-mination bundle in this endoscope bifurcates at thedistal tip into two illumination ports, which are locatedon each side of the imaging lens. Because the illumina-tion fibers travel largely in separate bundles, weachieved independent illumination of the two ports byselectively illuminating different halves of the bundleat their proximal end. Thus, we converted the endo-scope to a multiflash system consisting of two flashsources 180 degrees apart.

We bench-tested the multiflash endoscope with bio-logical specimens that simulated the examination of thehuman larynx. We were particularly interested to see howwell the system performed in detecting small surfacelesions, which commonly have depth discontinuities. Theprocessed images demonstrated the system’s capabilityto find edges of some simulated lesions (see Figure 5).We also found that the rounded shapes and the translu-cency of internal surfaces can make it difficult to cast use-ful shadows, problems that we will address in the futureby experimenting with illumination parameters.

Pathology Pathology departments document surgical and autop-

sy specimens. Systems for photographing such speci-mens involve special methods for eliminating unwantedshadows, usually by placing the specimens on glassplates suspended over black cavities. Using the multi-

flash system and processing to obtain the MAX com-posite image produces a view in which almost all shad-ows are eliminated.

Medical and biological illustration Often, it’s desirable to generate black-and-white illus-

trations of medical and natural history specimens inwhich salient details are emphasized and unnecessaryclutter is omitted.10 The most important details toemphasize are those that convey the object’s shape. Typ-ically shape is conveyed by emphasizing edges and usingstippling for shading. This type of illustration is seen lessfrequently nowadays because of the expense involvedin having artists create these graphics.

In our experiments with multiflash imaging, we haveobserved that the depth edge confidence maps frequent-ly resemble hand-drawn sketches. At the same time, sincethey are created from photographs, the maps retain a highdegree of realism. We felt that multiflash photographycould make it faster, easier, and less expensive for artistsand researchers to create medical illustrations.

Figure 6 shows some results generated with this cam-era. We showed the results to medical professionals atthe Massachusetts General Hospital and received posi-tive feedback on the usefulness of these images. In addi-tion, they also found the MAX composite image to be

IEEE Computer Graphics and Applications 35

5 (a) Enhanced endoscope. (b) Input image and image with depth edgessuperimposed.

(a) (b)

6 Biologicalillustrationswith multiflashimaging. (a) Sphenoidbone. (b) Rawoutput fromdepth edgedetection.

(a)

(b)

more useful, as it’s difficult to take shadow-free imageswith ordinary cameras. Anatomists and medical illus-trators who have seen our system are mostly interestedin the depth-edge confidence image, which can be eas-ily converted into a detailed black-and-white drawing.

Visual object recognitionThe accurate detection of depth edges is also useful

in many object recognition tasks and human–computerinteraction applications. In this section, we show thatour method can be reliably applied in automatic signlanguage analysis, particularly considering the fingerspelling recognition problem.

Sign language is the primary communication modeused by most deaf people. It consists of two major com-ponents: word level sign vocabulary, where gesturescommunicate the most common words, and fingerspelling, where the fingers on a single hand spell outmore obscure words and proper nouns, letter by letter.

We address the finger spelling recognition component,showing the usefulness of depth edges in this task.

Although researchers have spent great effort in thepast decade to develop automatic finger spelling recog-nition systems, most successful approaches are basedon instrumented gloves, which are considered cumber-some for the user and are often expensive. In general,nonintrusive vision-based methods, while useful for rec-ognizing a small subset of convenient hand configura-tions, are limited to discriminate configurations withhigh amounts of finger occlusions—a common scenarioin most finger spelling alphabets. In such cases, tradi-tional edge detectors or segmentation algorithms fail todetect important internal edges along the hand shape(due to the low intensity variation in skin color), whilekeeping edges due to nails and wrinkles, which may con-found scene structure and the recognition process. Also,some signs might look similar to each other, with smalldifferences on finger positions, thus posing a problemfor appearance-based approaches.

Finger spelling recognition We show that depth edges could be used as a signa-

ture to reliably discriminate among complex hand con-figurations in the American Sign Language (ASL)alphabet, which would not be possible with currentglove-free vision methods.

Figure 7 shows a comparison of the Canny method,which is a standard-intensity edge detector, and depthedges extracted with our method for the letter “R” of theASL alphabet. Important internal edges are missing inthe Canny method, while unwanted edges due to wrin-kles and nails are present.

We realized that depth edges are good features to dis-criminate among signs of finger spelling alphabets. Evenwhen the signs look similar (for example, letters “E”,“S,” and “O” in ASL alphabet), the depth edge signatureis quite discriminative (see Figure 8). This poses anadvantage over vision methods that rely on appearanceor edge-based representations. However, our methoddoes not detect edges in finger boundaries with no depthdiscontinuity. It turns out that this is helpful to providemore unique signatures for each letter.

To quantitatively evaluate the advantages of usingdepth edges as features for finger spelling recognition,we considered an experiment with the complete ASLalphabet—except for letters “J” and “Z,” which requiremotion analysis to be discriminated. We collected asmall set of 72 images using our multiflash camera(three 640 × 480-resolution images per letter, taken atdifferent times). The images showed variations in scale,translation, and rotation. The background was plain,with no clutter, since we wanted to show the importanceof obtaining clean edges in the interior of the hand. Tex-tured but flat and smooth backgrounds did not affectour method, but did make an edge detection approach(used for comparison) much more difficult.

For object classification, we used a depth edge shapedescriptor similar to shape context matching, which isinvariant to object translation and scaling.11 For com-parison, we also considered shape descriptors based onCanny edges. We obtained the recognition rate using a

Emerging Technologies

36 January/February 2005

7 (a) Letter “R” in ASL alphabet. (b) Canny edges. (c) Depth edgesobtained with our multiflash technique.

(a) (b) (c)

8 From left to right: input image, Canny edges, and depth edges.

leave-one-out scheme in the col-lected data set. Our approachachieved 96 percent correct match-es, compared with 88 percent whenusing Canny edges.

Rebollar mentioned in his workthat letters “R,” “U,” and “V” repre-sented the worst cases, as their classdistributions overlap significantly.12

Figure 9 shows these letters andtheir corresponding depth edge sig-natures. They are easily discrimi-nated with our technique. In theexperiment described previously,the method based on Canny edgesfails to discriminate them.

We collected all the images in ourexperiment from the same person.We plan to build a more completedatabase with different signers. Webelieve that our method will betterscale in this case, due to the fact thattexture edges (for example, wrin-kles, freckles, and veins) vary fromperson to person but are eliminatedin our approach. Also, shape contextdescriptors have proven useful forhandling hand shape variation from different people.11

For cluttered scenes, our method also eliminates alltexture edges, thus considerably reducing clutter (seeFigure 10).

For segmented hand images with resolution 96 × 180,the computational time required to detect depth edgesis 4 ms on a 3-GHz Pentium IV. The shape descriptorcomputation requires on average 16 ms. Thus, ourmethod is suitable for real-time processing. For improv-ing hand segmentation, depth edges could be comput-ed in the entire image. In this case, the processing timefor 640 × 480 images is 77 ms.

We are currently exploiting a frequency division mul-tiplexing scheme, where flashes with different colors(wavelength) are triggered simultaneously. We hopethis will allow for efficient online tracking of depth edgesin sign language analysis.

Additional finger-spelling issuesWhat if there are no cast shadows due to lack of back-

ground? In these cases only the outermost depth edge,the edge shared by the foreground and distant back-ground, is missed in our method. This could be detectedwith a foreground and background estimation technique.Let I0 be the image of the scene taken just with ambientillumination. Then the ratio I0/Imax (image acquired withno flash over MAX composite of flash images), is near 1 inthe background and close to zero in the foreground inte-rior. This is because the faraway background is not affect-ed by the flash, but the object will be brighter.

Another solution is to use our method to detect inter-nal edges in the hand, while using traditional techniques(such as skin color segmentation or background sub-traction) to obtain the external hand silhouette.

We noticed that depth edges might appear or disap-

pear with small changes in viewpoint (rotations indepth). We believe this might be a valuable cue for handpose estimation.

A common thread in recent research on pose estima-tion involves using a 3D model to create a large set ofexemplars undergoing variation in pose, as trainingdata. Pose estimation is an image retrieval problem inthis data set. We could use a similar approach to handleout-of-plane hand rotations. In this case, a 3D handmodel would store a large set of depth edge signaturesof hand configurations under different views.

We have not seen any previous technique that can pre-cisely acquire depth discontinuities in complex handconfigurations. In fact, stereo methods for 3D recon-struction would fail in such scenarios, due to the tex-tureless skin color regions as well as low intensityvariation along occluding edges.

Word level sign language recognition could also ben-efit from our technique, due to the high amounts ofocclusions involved. Flashes in our setup could bereplaced by infrared lighting for user-interactive appli-cations. We are currently evaluating our method in alarge database with different signers. We also plan toaddress the problem of continuous signing in dynamicscenes, using colored flashes.

Future workMany hardware improvements are possible. The depth-

edge extraction scheme could be used for spectrums otherthan visible light that create shadows, for example, ininfrared, sonar, x-ray, and radar imaging. We describeda video-rate camera for detecting depth edges in dynam-ic scenes described elsewhere, and we plan to build pro-totypes with infrared light sources invisible to humans sothe resulting flashes are not distracting.2 We could use a

IEEE Computer Graphics and Applications 37

9 Letters “R,” “U,” and “V.” The use of a depth edge signature can easily discriminate them.

(a) (b)

10 Cluttered scene using (a) Canny edges and (b) depth edges.

frequency division multiplexing scheme to create a sin-gle shot multiflash photograph. ■

References1. K.-H. Tan et al., “Shape-Enhanced Surgical Visualizations

and Medical Illustrations with Multi-Flash Imaging,” Proc.Int’l Conf. Medical Image Computing and Computer-AssistedIntervention (MICCAI), Springer-Verlag, 2004, pp. 438-445.

2. R. Raskar et al., “Non-photorealistic Camera: AutomaticStylization with Multi-Flash Imaging,” Proc. Siggraph, ACMPress, 2004, pp. 679-688.

3. R. Feris et al., “Exploiting Depth Discontinuities for Vision-Based Fingerspelling Recognition,” Proc. IEEE WorkshopReal-Time Vision for Human−Computer Interaction, vol. 10,IEEE CS Press, 2004.

4. D. Scharstein and R. Szeliski, “High-Accuracy Stereo DepthMaps Using Structured Light,” Proc. IEEE Conf. ComputerVision and Pattern Recognition (CVPR), vol. 1, IEEE CS Press2003, pp. 195-202.

5. D. DeCarlo and A. Santella, “Stylization and Abstractionof Photographs,” Proc. Siggraph, ACM Press, 2002, pp. 769-776.

6. J. Wang et al., “Video Tooning,” Proc. Siggraph, ACM Press,2004, pp. 574-583.

7. T. Saito and T. Takahashi, “Comprehensible Rendering of3D-Shapes,” Proc. Siggraph, ACM Press, 1990, pp. 197-206.

8. M. Mueller et al., “Three-Dimensional Laparoscopy. Gad-get or Progress? A Randomized Trial on the Efficacy ofThree-Dimensional Laparoscopy,” Surgical Endoscopy , vol.13, Springer-Verlag, 1999, pp. 1432-2218.

9. D. Rosen et al., “Calibrated Sizing System for FlexibleLaryngeal Endoscopy,” Proc. 6th Int’l Workshop: Advancesin Quantitative Laryngology Advances in Quantitative Laryn-gology, Voice and Speech Research, G. Schade et al., eds.,Verlag, 2003.

10. E.R.S. Hodges, The Guild Handbook of Natural History Illus-tration, John Wiley & Sons, 2003.

11. S. Belongie, J. Malik, and J. Puzicha, “Shape Matching andObject Recognition Using Shape Contexts,” IEEE Trans. Pat-tern Analysis and Machine Intelligence, vol. 24, no. 4, 2002,pp. 509-522.

12. J. Rebollar, R. Lindeman, and N. Kyriakopoulos, “A Multi-Class Pattern Recognition System for Practical Finger-spelling Translation,” Proc. Int’l Conf. Multimodal Interfaces,IEEE CS Press, 2002, p. 185.

Ramesh Raskar is a research sci-entist at Mitsubishi Electric ResearchLabs. His research interests includecomputer vision and graphics, pro-jective geometry, nonphotorealisticrendering, and intelligent user inter-faces. Raskar received a PhD from the

University of North Carolina at Chapel Hill in computerscience. He is a member of the IEEE, Society for Informa-tion Display, and ACM. Contact him at raskar@merl.com.

Kar-Han Tan is a research scientistat the Epson Palo Alto Lab. His re-search interests include human andcomputer vision, graphics, advancedsensors and displays, and intelligenthuman–machine interaction. Tan hasa PhD from the Univ. of Illinois at

Urbana-Champaign in computer science. He is a member ofthe IEEE and ACM. Email him at tankh@vision.ai.uiuc.edu.

Rogerio S. Feris is a PhD candi-date in computer science at the Uni-versity of California, Santa Barbara.His research interests include com-puter vision and graphics forhuman–computer interaction. Ferisreceived a BS in computer engineer-

ing from the Federal University of Rio Grande, Brazil, andan MS in computer science from the University of SaoPaulo, Brazil. Contact him at rferis@cs.ucsb.edu.

Matthew Turk is an associate pro-fessor in the Computer ScienceDepartment and the Media Arts andTechnology Program at the Universityof California, Santa Barbara. He codi-rects the Four Eyes Lab. Turk receiveda BS from Virginia Tech in electrical

engineering, an MS from Carnegie Mellon University in elec-trical and computer engineering, and a PhD from the MITMedia Lab. Contact him at mturk@cs.ucsb.edu.

James Kobler is director of the H.P.Mosher Laryngological Research Lab-oratory at the Massachusetts Eye andEar Infirmary, a faculty member ofthe Dept. of Otology and Laryngologyat Harvard Medical School, andadjunct faculty in the Division of

Health Sciences and Technology at MIT. His research inter-ests include endoscopic imaging, voice neural prostheses,and vocal fold regeneration. Kobler received a PhD in neu-robiology from the University of North Carolina at ChapelHill. Contact him at james_kobler@meei.harvard.edu.

Jingyi Yu is a PhD candidate in elec-trical engineering and computer sci-ence at MIT and is finishing hisdissertation at the University ofNorth Carolina at Chapel Hill. Hisresearch interests include image-based modeling and rendering,

image processing, and computational camera models. Yureceived a BS in electrical and applied science and appliedmathematics from the California Institute of Technologyand an MS in electric engineering and computer sciencefrom MIT. Contact him at Jingyi@graphics.csail.mit.edu.

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38 January/February 2005