Analysis and Classification of Object Poses - Using Visual/Infrared Images and Feature Fusion - Yun, Yixiao (My University, 2011)
This master thesis addresses issues in computer vision and pattern classiﬁcation. More speciﬁcally, we are interested in classiﬁcation of various object poses from images, for examples, poses of human faces or cars. Analysis and classiﬁcation of visual object poses are important steps towards diﬀerent applications, e.g., surveillance and traﬃc safety. In this thesis work, several feature extraction methods are implemented, including HOG (histogram of oriented gradients) and Gabor features. A multi-class object classiﬁer based on multi-class AdaBoost is implemented. Experiments have been conducted on large numbers of face images and car images with diﬀerent poses. For human faces, the classiﬁer contains 5 classes of poses (frontal, left, right, upward and downward), while for car images the classiﬁer contains 4 classes of poses (frontal, rear, left and right). Two types of images are tested: one is from the visual band and another is from the thermal infrared band. Due to diﬀerent properties and characteristics in these two types of images, diﬀerent types of features are extracted. For visual band images, HOG is used as the main feature descriptor. For thermal IR images, Gabor features are used. For classiﬁcation of object poses, classiﬁers are tested separately by using visual band images only, and thermal IR images only. Performance is then evaluated for these two types of classiﬁers. Attempts are also made on classiﬁers through fusing these two types of features in visual and IR images. Results will be presented and future work will be discussed.
Keywords: Elektroteknik och elektronik; Informations- och kommunikationsteknik; Electrical Engineering, Electronic Engineering, Information Engineering; Information & Communication Technology