Generalized Image Acquisition and Analysis

Intrinsic Shape Matching by Planned Landmark Sampling

Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have been particularly successful due to their generality and efficiency. We introduce a new sampling-based shape matching algorithm that uses a planning step to find optimized "landmark" points. These points are matched first in order to maximize the information gained and thus minimize the sampling costs. Our approach makes three main contributions: First, the new technique leads to a significant improvement in performance, which we demonstrate on a number of benchmark scenarios. Second, our technique does not require any keypoint detection. This is often a significant limitation for models that do not show sufficient surface features. Third, we examine the actual numerical degrees of freedom of the matching problem for a given piece of geometry. In contrast to previous results, our estimates take into account unprecise geodesics and potentially numerically unfavorable geometry of general topology, giving a more realistic complexity estimate.

Teaching

Parallel Visual Computing

Seminar in winter term 2012/13

Lecturers: Ivo Ihrke, Tobias Ritschel, Mario Fritz

General Information

Course webpage

When: 2012, Oct. 18th to 2013, Jan. 31st
Where: E1.7 room 0.01
Registration for mailing list: send email to Ivo Ihrke (lastname@mmci.uni-saarland.de)

Overview:

This seminar covers the hands-on use of parallel hardware (CPUs and GPUs) for visual computing, i.e.,

  • Computer vision (e.g., from simple image operations to classification)
  • Computer graphics (e.g., advanced shading)
  • Scientific computing (e.g., equation solving)

The target audience are students in computer science or related fields. Good C++ programming skills, basic knowledge about 3D geometry, image processing, and computer graphics are required. This seminar will be based on hands-on parallel programming:

  • Every one week, a tutor will present a problem with an interesting parallel solution.
  • On the same day there will be a programming assignment on the topic.
  • Teams of two people will work on this assignment
  • Every team demos their solution and we discuss

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