Predictive monocular odometry using propagation-based tracking

The technology of advanced driver assistance systems (ADAS) has rapidly developed in the last few decades. The current level of assistance provided by the ADAS technology significantly makes driving much safer by using the developed driver protection systems such as automatic obstacle avoidance and automatic emergency braking. With the use of ADAS, driving not only becomes safer but also easier as ADAS can take over some routine tasks from the driver, e.g. by using ADAS features of automatic lane keeping and automatic parking. With the continuous advancement of the ADAS technology, fully autonomous cars are predicted to be a reality in the near future. One of the most important tasks in autonomous driving is to accurately localize the egocar and continuously track its position. The module which performs this task, namely odometry, can be built using different kinds of sensors: camera, LIDAR, GPS, etc. This dissertation covers the topic of visual odometry using a camera. While stereo visual odometry frameworks are widely used and dominating the KITTI odometry benchmark (Geiger, Lenz and Urtasun 2012), the accuracy and performance of monocular visual odometry is much less explored. In this dissertation, a new monocular visual odometry framework is proposed, namely Predictive Monocular Odometry (PMO). PMO employs the prediction-and-correction mechanism in different steps of its implementation. PMO falls into the category of sparse methods. It detects and chooses keypoints from images and tracks them on the subsequence frames. The relative pose between two consecutive frames is first pre-estimated using the pitch-yaw-roll estimation based on the far-field view (Barnada, Conrad, Bradler, Ochs and Mester 2015) and the statistical motion prediction based on the vehicle motion model (Bradler, Wiegand and Mester 2015). The correction and optimization of the relative pose estimates are carried out by minimizing the photometric error of the keypoints matches using the joint epipolar tracking method (Bradler, Ochs, Fanani and Mester 2017). The monocular absolute scale is estimated by employing a new approach to ground plane estimation. The camera height over ground is assumed to be known. The scale is first estimated using the propagation-based scale estimation. Both of the sparse matching and the dense matching of the ground features between two consecutive frames are then employed to refine the scale estimates. Additionally, street masks from a convolutional neural network (CNN) are also utilized to reject non-ground objects in the region of interest. PMO also has a method to detect independently moving objects (IMO). This is important for visual odometry frameworks because the localization of the ego-car should be estimated only based on static objects. The IMO candidate masks are provided by a CNN. The case of crossing IMOs is handled by checking the epipolar consistency. The parallel-moving IMOs, which are epipolar conformant, are identified by checking the depth consistency against the depth maps from CNN. In order to evaluate the accuracy of PMO, a full simulation on the KITTI odometry dataset was performed. PMO achieved the best accuracy level among the published monocular frameworks when it was submitted to the KITTI odometry benchmark in July 2017. As of January 2018, it is still one of the leading monocular methods in the KITTI odometry benchmark. It is important to note that PMO was developed without employing random sampling consensus (RANSAC) which arguably has been long considered as one of the irreplaceable components in a visual odometry framework. In this sense, PMO introduces a new style of visual odometry framework. PMO was also developed without a multi-frame bundle adjustment step. This reflects the high potential of PMO when such multi-frame optimization scheme is also taken into account

A novel approach for face verification on mobile devices

Most people have cellular phones nowadays. In this technological age, much attention had been given to information security. Many approaches and methods had been developed. Some systems such as security codes, fingerprint or signature verification are used to secure these devices. Processors used in such devices have less computational power than PC processors. On the other hand, high resolution cameras, i.e. cameras with 8 mega pixel, have been attached to these handheld devices that enable face verification algorithms to have better accuracy. Despite of such higher resolution cameras, mobile devices processors are not able to process large face verification in real time. Therefore, face verification algorithms need to be modified to enable hand phones to verify the owner in acceptable time. This project focuses on face verification technique to provide a reliable identification method. Principle Component Analysis and Gabor-Adaboost methods were simulated. The strength and weakness of the methods were investigated further. The comparison was based on the accuracy of verifications and the time taken for each simulation. Furthermore, this project intends to seek a robust face verification system. The algorithms were implemented to ORL face database and Essex face databases (Face 94 & Face95). The results showed that Gabor-Adaboost method is relatively more robust than Principle Component Analysis with regards to pose variation and illumination changes, by having more than 95% of verification accuracy.Bachelor of Engineerin

Diffraction and Spectroscopy of Porous Solids

Porous solids are in the first place solids and thus all methods for the analysis of common solids can be also applied on porous ones. Structural information and bulk properties of the solid are accessible as for any other material. In addition, specific information on pore sizes, pore shapes, and properties of pore surfaces of such material can be obtained by different methods as well as information on guest species within the pores and on host–guest interactions. In the present chapter, diffraction and spectroscopic methods for the analysis of porous solids will be described and the type of information that can be achieved by the different methods will be illustrated. Diffraction and scattering of x-rays, neutrons, and electrons by a porous solid will be introduced as well as the application of infrared and nuclear magnetic resonance spectroscopy for the analysis of surface properties, host–guest interaction, and diffusion studies that are complemented by interference microscopy

Diffraction and Spectroscopy of Porous Solids

Porous solids are in the first place solids and thus all methods for the analysis of common solids can be also applied on porous ones. Structural information and bulk properties of the solid are accessible as for any other material. In addition, specific information on pore sizes, pore shapes, and properties of pore surfaces of such material can be obtained by different methods as well as information on guest species within the pores and on host–guest interactions. In the present chapter, diffraction and spectroscopic methods for the analysis of porous solids will be described and the type of information that can be achieved by the different methods will be illustrated. Diffraction and scattering of x-rays, neutrons, and electrons by a porous solid will be introduced as well as the application of infrared and nuclear magnetic resonance spectroscopy for the analysis of surface properties, host–guest interaction, and diffusion studies that are complemented by interference microscopy