| Conference |
| Type of Publication |
| Recognition of User-Defined Video Object Models using
Weighted Graph Homomorphisms |
| Title |
|
Peter H. N. de With
|
| Authors |
| SPIE Image and Video Communications and
Processing, pp.
542-553, Santa Clara, CA, January 2003 |
| Published in |
| In this paper, we propose a new system for
video object detection based on user-defined models. Object
models are described by model graphs in which nodes represent
image regions and edges denote spatial proximity. Each node is
attributed with color and shape information about the
corresponding image region. Model graphs are specified manually
based on a sample image of the object. Object recognition starts
with automatic color segmentation of the input image. For each
region, the same features are extracted as specified in the model
graph. Recognition is based on finding a subgraph in the image
graph that matches the model graph. Evidently, it is not possible
to find an isomorph subgraph, since node and edge attributes will
not match exactly. Furthermore, the automatic segmentation step
leads to an oversegmented image. For this reason, we employ
\emph{inexact} graph matching, where several nodes of the image
graph may be mapped onto a single node in the model graph. We
have applied our object recognition algorithm to cartoon
sequences. This class of sequences is difficult to handle with
current automatic segmentation algorithms because the motion
estimation has difficulties arising from large homogeneous
regions and because the object appearance is typically highly
variable. Experiments show that our algorithm can robustly detect
the specified objects and also accurately locates the object
boundary. |
| Abstract |
|
object recognition
inexact graph matching
image segmentation
dynamic programmingsegmentation
|
| Keywords |
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