This thesis describes research on the use of Java to develop cross-platform computer vision applications for mobile phones with integrated cameras. The particular area of research that we are interested in is Mobile Augmented Reality (AR). Currently there is no computer vision library which can be used for mobile Augmented Reality using the J2ME platform. This thesis introduces the structure of our J2ME computer vision library and describes the implementation of algorithms in our library. We also present several sample applications on J2ME enabled mobile phones and report on experiments conducted to evaluate the compatibility, portability and efficiency of the implemented algorithms.

This thesis describes research on the use of Java to develop cross-platform computer vision applications for mobile phones with integrated cameras. The particular area of research that we are interested in is Mobile Augmented Reality (AR). Currently there is no computer vision library which can be used for mobile Augmented Reality using the J2ME platform. This thesis introduces the structure of our J2ME computer vision library and describes the implementation of algorithms in our library. We also present several sample applications on J2ME enabled mobile phones and report on experiments conducted to evaluate the compatibility, portability and efficiency of the implemented algorithms.

Robust, non-intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human-computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this thesis is not only a vision system with eye detection capability / Our aim is to design a real-time, robust, scale-invariant eye tracker system with human eye movement indication property using the movements of eye pupil. Our eye tracker algorithm is implemented using the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm proposed by Bradski and the EigenFace method proposed by Turk &amp / Pentland. Previous works for scale invariant object detection using Eigenface method are mostly dependent on limited number of user predefined scales which causes speed problems / so in order to avoid this problem an adaptive eigenface method using the information extracted from CAMSHIFT algorithm is implemented to have a fast and scale invariant eye tracking. First of all / human face in the input image captured by the camera is detected using the CAMSHIFT algorithm which tracks the outline of an irregular shaped object that may change size and shape during the tracking process based on the color of the object. Face area is passed through a number of preprocessing steps such as color space conversion and thresholding to obtain better results during the eye search process. After these preprocessing steps, search areas for left and right eyes are determined using the geometrical properties of the human face and in order to locate each eye indivually the training images are resized by the width information supplied by the CAMSHIFT algortihm. Search regions for left and right eyes are individually passed to the eye detection algortihm to determine the exact locations of each eye. After the detection of eyes, eye areas are individually passed to the pupil detection and eye area detection algorithms which are based on the Active Contours method to indicate the pupil and eye area. Finally, by comparing the geometrical locations of pupil with the eye area, human gaze information is extracted. As a result of this thesis a software named &ldquo / TrackEye&rdquo / with an user interface having indicators for the location of eye areas and pupils, various output screens for human computer interaction and controls for allowing to test the effects of color space conversions and thresholding types during object tracking has been built.