Sonar sensor interpretation for ectogeneous robots

We have developed four generations of sonar scanning systems to automatically interpret surrounding environment. The first two are stationary 3D air-coupled ultrasound scanning systems and the last two are packaged as sensor heads for mobile robots. Template matching analysis is applied to distinguish simple indoor objects. It is conducted by comparing the tested echo with the reference echoes. Important features are then extracted and drawn in the phase plane. The computer then analyzes them and gives the best choices of the tested echoes automatically. For cylindrical objects outside, an algorithm has been presented to distinguish trees from smooth circular poles based on analysis of backscattered sonar echoes. The echo data is acquired by a mobile robot which has a 3D air-coupled ultrasound scanning system packaged as the sensor head. Four major steps are conducted. The final Average Asymmetry-Average Squared Euclidean Distance phase plane is segmented to tell a tree from a pole by the location of the data points for the objects interested. For extended objects outside, we successfully distinguished seven objects in the campus by taking a sequence scans along each object, obtaining the corresponding backscatter vs. scan angle plots, forming deformable template matching, extracting interesting feature vectors and then categorizing them in a hyper-plane. We have also successfully taught the robot to distinguish three pairs of objects outside. Multiple scans are conducted at different distances. A two-step feature extraction is conducted based on the amplitude vs. scan angle plots. The final Slope1 vs. Slope2 phase plane not only separates the rectangular objects from the corresponding cylindrical

Study of face detection and tracking

Since the development of face detection in the 1990’s, the research on its potential applications such as automated face recognition, surveillance and security system, human-computer interaction, etc. has been very active. Together with the development of the instant messenging and teleconferencing, there is a huge increase the usage of these technologies. Integration between these two technologies has been sought after. During the last decades, there had been numerous proposition and research on methods for face detection and face tracking. In this project, it is to study in details a few of the more popular and common existing methods available. The background and introduction of these methods will be shown, together with how the algorithms worked and how they performed. The methods that will be discussed are namely, automatic human face detection and recognition under non-uniform illumination face detection using discriminating feature analysis and Support Vector Machine (SVM), face tracking in Model-based Coding (MBC), multi-expert approach for face detection and multi-view face and eye detection using discriminant features. Lastly, a more enhanced algorithm, Kalman filter algorithm, is displayed and discussed on its functions and effectiveness on multiple face tracking. The various algorithms and detection methods are studied in details such as how they are derived from and how they detect or track faces. Experimental results showing their performance and efficiency are also displayed to justify why they are the more common and popular existing methods being used in many applications nowadays. This project also provides detailed information and insights that can be used as a foundation, to fulfill the possibility of integrating the more enhanced algorithm with other applications such as real-time video streaming for future developments.Bachelor of Engineerin