Appearance based statistical object recognition including color and context modeling

Autori

• Marcin Grzegorzek

Parametri

• Computer, IT, programmazione

Maggiori informazioni sul libro

This dissertation presents a system for appearance-based statistical classification and localization of 3-D objects in 2-D digital images. The initial chapters define the object recognition task, outline the mathematical foundations of the system, and review existing object recognition methods. The learning phase begins with image acquisition using a hand-held camera, where object poses for modeling are computed from the training image sequence via a structure-from-motion algorithm. Unlike shape-based methods, this approach avoids segmentation steps to extract object features. Instead, objects are represented by 2-D local feature vectors derived directly from image pixel values using the wavelet transform, applicable to both gray level and color images. These features are statistically modeled with normal distribution and stored as density functions in the object models, alongside context modeling during training. In the recognition phase, the system classifies and localizes objects within scenes featuring real heterogeneous backgrounds, where the number of objects is unknown. Feature vectors are computed similarly to the training phase, and a maximization algorithm evaluates the learned density functions against the extracted feature vectors, identifying classes and poses of objects in the scene. Experiments conducted on a dataset of over 40,000 images demonstrate the system's strong performance in classification and localiza

Lingua: Inglese

Pubblicato: 2007

(In brossura)