1995_Viola_thesis_registrationMI

As a result this is a test of emmas ability to handle

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Unformatted text preview: that covers the entire chin area. The nal alignment is very close to the correct one despite the occlusion. Figure 5.10 shows an initial and nal pose for a more complex occlusion. In this image we have replaced a rectangular window with another randomly chosen window of the image. The source of the rectangle is near the bottom of the image. In a number of experiments, we have found that alignment to occluded images can require more time for convergence. 117 Paul A. Viola CHAPTER 5. ALIGNMENT EXPERIMENTS Figure 5.8: Final pose of the skull model after alignment. Figure 5.9: An image including an arti cial occlusion. White spots denote the pose of the model. On the left is the initial pose, on the right is the nal pose. Figure 5.10: An image including an arti cial occlusion. White spots denote the pose of the model. On the left is the initial pose, on the right is the nal pose. 118 5.1. ALIGNMENT OF 3D OBJECTS TO VIDEO AI-TR 1548 5.1.2 Alignment of Head Model We have repeated many of the skull experiments with a three dimensional model of a human head. This model was obtained from a Cyberware scan of the subject that was taken approximately two years before the video images3. A Cyberware scan is a complete three dimensional representation of the shape of the subject's head in cylindrical coordinates. The surface normals were computed from the surface by smoothing and di erencing neighboring surface points. The experiments in this section are designed to answer two questions: 1 Will the same techniques and parameters work with two di erent types of models and images? 2 Is it possible to use the pose re nement procedure to track a moving object in a video sequence? Figure 5.11 shows an image of the head and a rendering of the model. How are the face experiments di erent from the skull experiments? Firstly, the face model is much smoother than the skull model. There really aren't any creases or points of high curvature. As a result it is much less likely that an edge-based system could construct a representation either of the image or the model that would be stable under changes in illumination. Secondly, the albedo of the actual object is not exactly constant. The face contains eyebrows, lips and other regions where the albedo is not the same. As a result this is a test of EMMA's ability to handle objects where the assumption of constant albedo is violated. Thirdly, not all of the occluding contours of the object are present in the model. The model is truncated both at the chin and the forehead. As a result experiments with this model demonstrate that EMMA can work even when the occluding contours of the image and model are not in agreement. In the previous experiment projecting points from the model into the image was su cient to describe the model pose. Since the head model is very smooth and some occluding contours are missing simply projecting the model points into the image is not su cient to determine the quality of an alignment. For our experiments with the head model we will display the original image, augmented with model points, alongside a rendered image of the model. Figures 5.11 and 5.12 show the model before and after alignment. In this experiment the model has been rotated 30 degrees around the vertical and translated 40 millimeters to the 3 Thanks to Ron Kikinis for providing the Cyberware scan and for allowing me to take the images of him. 119 Paul A. Viola CHAPTER 5. ALIGNMENT EXPERIMENTS Figure 5.11: An initial incorrect pose. The model has been rotated 30 degrees about the vertical and translated 40 millimeters to the right. On the left is an image of the head along with a collection of points projected from the model. On the right is a rendering of the model in the same pose. Figure 5.12: The nal aligned poses. On the left is an image of the head along with a collection of points projected from the model. On the right is a rendering of the model in the same pose. right. Figures 5.13 and 5.14 show another experiment where EMMA alignment corrects for a 150 millimeter translation in depth. We have also tested EMMA alignment on a video sequence digitized from a video tape. The sequence was taken at the same time as the other images, though the camera and the lens were di erent. Ten frames were acquired from a video tape at 3 frames per second. The quality of the resulting images is very low. The images were degraded both by their storage on video tape and by the frame grabber that was used. It was somewhat surprising that these images worked nearly as well as the higher quality still frames. Motion in the video sequence was tracked by sequentially aligning the model to each of the frames. The starting pose for each frame was obtained by using the nal estimated pose from the previous frame. The starting pose for the rst frame was hand selected so that EMMA alignment could acquire a good initial alignment. The sequence and pose estimates are displayed in Figure 5.15. 120 5.1. ALIGNMENT OF 3D OBJECTS TO VIDEO AI-TR 1548 Fig...
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