Depth-adaptive methodologies for 3D image caregorization.

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Image classification is an active topic of computer vision research. This topic deals with the learning of patterns in order to allow efficient classification of visual information. However, most research efforts have focused on 2D image classification. In recent years, advances of 3D imaging enabled the development of applications and provided new research directions. In this thesis, we present methodologies and techniques for image classification using 3D image data. We conducted our research focusing on the attributes and limitations of depth information regarding possible uses. This research led us to the development of depth feature extraction methodologies that contribute to the representation of images thus enhancing the recognition efficiency. We proposed a new classification algorithm that adapts to the need of image representations by implementing a scale-based decision that exploits discriminant parts of representations. Learning from the design of image representation methods, we introduced our own which describes each image by its depicting content providing more discriminative image representation. We also propose a dictionary learning method that exploits the relation of training features by assessing the similarity of features originating from similar context regions. Finally, we present our research on deep learning algorithms combined with data and techniques used in 3D imaging. Our novel methods provide state-of-the-art results, thus contributing to the research of 3D image classificatio

Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our tasks. In our system we employ effective depth map processing techniques, along with edge detection, connected components detection and filtering approaches, in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects. Besides, we apply the Linear Spatial Pyramid Matching (LSPM) [1] method proposed by Jianchao Yang et al for the efficient classification of the detected objects. Experimental results are presented for both detection and classification, showing the efficiency of the proposed design