By Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Müller, Zhuowen Tu
This booklet constitutes the completely refereed post-workshop court cases of the 3rd foreign Workshop on clinical laptop imaginative and prescient, MCV 2013, held in Nagoya, Japan, in September 2013 at the side of the sixteenth overseas convention on clinical picture Computing and Computer-Assisted Intervention, MICCAI 2013. The 7 revised complete papers and 12 poster papers awarded have been chosen from 25 submissions. they've been equipped in topical sections on registration and visualization, segmentation, detection and localization, and lines and retrieval. additionally, the amount comprises invited papers describing segmentation activity and knowledge set of the VISCERAL benchmark challenge.
Read or Download Medical Computer Vision. Large Data in Medical Imaging: Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers PDF
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Additional info for Medical Computer Vision. Large Data in Medical Imaging: Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers
Sample text
The Oren-Nayar-Wolff model is applied for reflectance with a camera that obeys perspective projection and a light source is located at the cameras optical center. Intensity of the light is attenuated with squared distance. The idea here is to solve for the height map u(x) that minimizes the energy functional (I(x) − R(u(x))2 dx, ε= (8) κ where Ω ∈ ◦2 represents the image spatial domain, and I is the image intensity. A great advantage of the above optimization problem (8) is that the solution search space is shrunk into a finite number (N ) of weights.
4, and the paper is concluded in Sect. 5. 1 Method Data Preprocessing The triangular meshes of the training ensemble are obtained from a high resolution computer tomography scan of human invitro teeth. 2 mm is then used to scan the wax and teeth. where we use the Expectation-Maximization (EM) algorithm for segmentation [4]. The first surface of the training set is used as the reference to which the remaining surfaces are aligned. 3D surfaces for each tooth type are rigidly aligned to the reference using an ICP-based rigid registration algorithm [6] using the Hausdorff distance between corresponding points.
The Oren-Nayar-Wolff model is applied for reflectance with a camera that obeys perspective projection and a light source is located at the cameras optical center. Intensity of the light is attenuated with squared distance. The idea here is to solve for the height map u(x) that minimizes the energy functional (I(x) − R(u(x))2 dx, ε= (8) κ where Ω ∈ ◦2 represents the image spatial domain, and I is the image intensity. A great advantage of the above optimization problem (8) is that the solution search space is shrunk into a finite number (N ) of weights.