Appearance-Based Statistical Object Recognition Including Color and Context Modeling
Published in May 2007
Book
Paperback/Softback
230 pages
978-3-8325-1588-1 (ISBN)
Description
Within the scope of this dissertation a system
for appearance-based statistical classification
and localization of 3-D objects in 2-D digital
images is presented. The first three chapters
define the object recognition task, present the
mathematical background of the system, and discuss
the known approaches for object recognition. Then
the learning phase of the system is described.
The training begins with the image acquisition,
which can be done with a hand-held camera. The
object poses required for the further object
modeling are computed from the training image
sequence with the structure-from-motion algorithm.
In contrast to shape-based approaches, appearance-based methods do not use any segmentation steps to extract object features. The objects are described by 2-D local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and color images can be used for feature extraction. Finally, the object features are statistically modeled with the normal distribution and stored in the object models as density functions.
Additionally, context modeling is also performed
in the training phase. In the recognition phase
the system classifies and localizes objects in
scenes with real heterogeneous background, whereas
the number of objects in a scene is unknown. First, feature vectors are calculated in the scene with the same method as in the training. Second, a maximization algorithm evaluates the learned density functions with the extracted feature vectors and yields classes and poses of objects found in the scene. Experiments made on a real data set with more than 40000 images compare the classification and localization rates for all
algorithms discussed in the dissertation and
show a very good performance of the system in
a real world environment.
for appearance-based statistical classification
and localization of 3-D objects in 2-D digital
images is presented. The first three chapters
define the object recognition task, present the
mathematical background of the system, and discuss
the known approaches for object recognition. Then
the learning phase of the system is described.
The training begins with the image acquisition,
which can be done with a hand-held camera. The
object poses required for the further object
modeling are computed from the training image
sequence with the structure-from-motion algorithm.
In contrast to shape-based approaches, appearance-based methods do not use any segmentation steps to extract object features. The objects are described by 2-D local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and color images can be used for feature extraction. Finally, the object features are statistically modeled with the normal distribution and stored in the object models as density functions.
Additionally, context modeling is also performed
in the training phase. In the recognition phase
the system classifies and localizes objects in
scenes with real heterogeneous background, whereas
the number of objects in a scene is unknown. First, feature vectors are calculated in the scene with the same method as in the training. Second, a maximization algorithm evaluates the learned density functions with the extracted feature vectors and yields classes and poses of objects found in the scene. Experiments made on a real data set with more than 40000 images compare the classification and localization rates for all
algorithms discussed in the dissertation and
show a very good performance of the system in
a real world environment.