1 / 9

Method: Elastic Graph Matching (EGM)

Method: Elastic Graph Matching (EGM). A labeled graph G representing Hand consists of : Nodes with a local image description, positions Edges with a distance vector General Hand Knowledge Two constraints Similarity functions for the nodes and a cost function for the edges.

audra
Download Presentation

Method: Elastic Graph Matching (EGM)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Method: Elastic Graph Matching (EGM) • A labeled graph G representing Hand consists of : Nodes with a local image description, positions Edges with a distance vector General Hand Knowledge • Two constraints • Similarity functions for the nodes and a cost function for the edges • Node positions are chosen to lie on the rim of the hand and on highly textured positions within the hand.

  2. Complex Gabor-based kernels

  3. Jets and Similarity function • Jet is the result of convolutions with several filters of different wave-vector k form a local image description. • Sabs only uses the magnitudes of the complex filter responses, Spha also considers their phases. Both functions yield similarity values between zero and one.

  4. Bunch-graphs of hand postures • The idea behind the bunch-graph concept is to express the variability in the jets of corresponding points in several images of an object by labeling a node with a collection of jets rather than only a single jet. • All graphs have 35 nodes and 70 edges. • The node information attached to each node of the bunch-graph is a bunch-jet B defined as the set of the six jets of that node n: • B = {J (1),…, J (6)}

  5. Matching of a Bunch-graph • The key role in EGM is played by a function evaluating the graph similarity between an image graph and the GHK of identical view.

  6. Compensate Variations • During the matching process the model graph may be distorted. • Big distortions shall be punished by a topological cost term. • Cost for a single edge • The average for all individual edges:

  7. Matching Process • The matching process operates in three steps: • Coarse positioning of the graph: • Rescaling of the graph: • Local diffusion of single nodes:

  8. Tests and Results • Our gallery consists of 10 hand postures performed by 24 persons against three types of backgrounds: uniform light, uniform dark, and complex.

  9. References [1] L. Wiskott, J.-M. Fellous, N. Kru¨ger, C. von der Malsburg, Face recognition and gender determination, Proc. Int. Workshop Autom. Face Gesture Recogn., Zu¨rich, Switzerland June (1995) 92–97. [2] L. Wiskott, Labeled Graphs and Dynamic Link Matching for Face Recognition and Scene Analysis, PhD Thesis, vol. 53 of Reihe Physik, Verlag Harri Deutsch, Thun, Frankfurt a. Main, Germany, 1995.

More Related