Face Detection for Augmented Reality Application Using Boosting-based Techniques

Augmented reality has gained an increasing research interest over the few last years. Customers requirements have become more intense and more demanding, the need of the different industries to re-adapt their products and enhance them by recent advances in the computer vision and more intelligence has become a necessary. In this work we present a marker-less augmented reality application that can be used and expanded in the e-commerce industry. We take benefit of the well known boosting techniques to train and evaluate different face detectors using the multi-block local binary features. The work purpose is to select the more relevant training parameters in order to maximize the classification accuracy. Using the resulted face detector, the position of the face will serve as a marker in the proposed augmented reality

Enhanced Augmented Reality Framework for Sports Entertainment Applications

Augmented Reality (AR) superimposes virtual information on real-world data, such as displaying useful information on videos/images of a scene. This dissertation presents an Enhanced AR (EAR) framework for displaying useful information on images of a sports game. The challenge in such applications is robust object detection and recognition. This is even more challenging when there is strong sunlight. We address the phenomenon where a captured image is degraded by strong sunlight. The developed framework consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player detection, face detection, recognition of players, and display of personal information of players. First, an algorithm based on Multi-Scale Retinex (MSR) is proposed for image enhancement. For the tasks of player and face detection, we use adaptive boosting algorithm with Haar-like features for both feature selection and classification. The player face recognition algorithm uses adaptive boosting with the LDA for feature selection and nearest neighbor classifier for classification. The framework can be deployed in any sports where a viewer captures images. Display of players-specific information enhances the end-user experience. Detailed experiments are performed on 2096 diverse images captured using a digital camera and smartphone. The images contain players in different poses, expressions, and illuminations. Player face recognition module requires players faces to be frontal or up to ?350 of pose variation. The work demonstrates the great potential of computer vision based approaches for future development of AR applications.COMSATS Institute of Information Technolog

The Implementation of Augmented Reality Hairstyles at Beauty Salons Using the Viola-Jones Method (Case Study: Eka Salon)

Augmented reality is the technology that superimposes a computer-generated digital content on a user's view of the real world in a real time so users can experience the real virtual objects. The use of augmented reality has spread into various industries, for an example in the fashion industry. One of fashion industry type is hairstyle industry. Eka Salon is a beauty salon that provides beauty treatments for women's hair care. This salon has a problem that customer are not satisfacted with the results of their new haircut because that doesnt match with their expectations. This can be seen from the results of observations at Eka Salon is resulted that 8 out of 15 interviewed customers were not satisfied with their new haircuts because it did not match the appearance in the catalog. In this research, an augmented reality hairstyles will be made that can visualize how the shape of the selected hairstyle by the customer without having to cut their hair first. The Viola-Jones method was chosen as the method used in this study because it has a high accuracy of 90% in face detection. The result of this research is that the Viola-Jones method can detect facial surfaces and generate a 3D hairstyle model distance to 100cm properly. The test of the acceptance level of this application is carried out by Eka Salon customers with an average percentage of 84.3%

Using Haar-like feature classifiers for hand tracking in tabletop augmented reality

We propose in this paper a hand interaction approach to Augmented Reality Tabletop applications. We detect the user’s hands using haar-like feature classifiers and correlate its positions with the fixed markers on the table. This gives the user the possibility to move, rotate and resize the virtual objects located over the table with their bare hands.Postprint (published version

Video-based assistance system for training in minimally invasive surgery

In this paper, the development of an assisting system for laparoscopic surgical training is presented. With this system, we expect to facilitate the training process at the first stages of training in laparoscopic surgery and to contribute to an objective evaluation of surgical skills. To achieve this, we propose the insertion of multimedia contents and outlines of work adapted to the level of experience of trainees and the detection of the movements of the laparoscopic instrument into the monitored image. A module to track the instrument is implemented focusing on the tip of the laparoscopic tool. This tracking method does not need the presence of artificial marks or special colours to distinguish the instruments. Similarly, the system has another method based on visual tracking to localize support multimedia content in a stable position of the field of vision. Therefore, this position of the support content is adapted to the movements of the camera or the working area. Experimental results are presented to show the feasibility of the proposed system for assisting in laparoscopic surgical training