The Viola-Jones face detection algorithm was (and still is) a quite popular face detector. In spite of the numerous face detection techniques that have been recently presented, there are many research works that are still based on the Viola-Jones algorithm because of its simplicity. In this paper, we study the influence of a set of blind pre-processing methods on the face detection rate using the Viola-Jones algorithm. We focus on two aspects of improvement, specifically badly illuminated faces and blurred faces. Many methods for lighting invariant and deblurring are used in order to improve the detection accuracy. We want to avoid using blind pre-processing methods that may obstruct the face detector. To that end, we perform two sets of experiments. The first set is performed to avoid any blind pre-processing method that may hurt the face detector. The second set is performed to study the effect of the selected pre-processing methods on images that suffer from hard conditions. We present two manners of applying the pre-processing method to the image prior
In the film industry, many tricks have been employed using the integration of a 3D object with a real actor. Usually, attaching a 3D object with a real actor is a costly process because of the usage of an expensive motion capture system. This paper presents a system using a medium-cost motion capture system and a chroma-keying technique for generating a video footage of an actor with an integrated 3D object (e.g. amputated arm). The result of the proposed system shows the attaching process of different 3D objects with a real actor who is combined with a new background scene in the same viewpoint.
Human-robot interaction is an evolving area of research in the past few years. Human-robot interaction deals with how humans can interact with, send data to, or receive data from robots. One of the major obstacles in this field is how the robot can obtain the depth information of the surrounding objects. Few years ago, Microsoft has released a depth sensor that computes the depth information using IR rays. Many researches are conducted to control robots using depth sensors, such as Microsoft Kinect and Asus Xition. Although depth sensors are considered to be low cost, it may be unavailable for many users. In this work, we develop a lowcost system for controlling robots (iRobot) with a web-cam and just red markers on the user's hands. Our system requires no extra devices or hardware or other complex technologies. Experimental results of the proposed system demonstrate good results compared to those provided by depth sensors.
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