The viewfinder of a digital camera has traditionally been used for one purpose: to display to the user a preview of what is seen through the camera’s lens. High quality cameras are now available on devices such as mobile phones and PDAs, which provide a platform where the camera is a programmable device, enabling applications such as online computational photography, computer vision-based interactive gaming, and augmented reality. For such online applications, the camera viewfinder provides the user’s main interaction with the environment. In this paper, we describe an algorithm for aligning successive viewfinder frames. First, an estimate of inter-frame translation is computed by aligning integral projections of edges in two images. The estimate is then refined to compute a full 2D similarity transformation by aligning point features. Our algorithm is robust to noise, never requires storing more than one viewfinder frame in memory, and runs at 30 frames per second on standard smartphone hardware. We use viewfinder alignment for panorama capture, low-light photography, and a camera-based game controller.

The detection of a user’s hand gestures enables a natural interaction with digital content. Recently, wearable gesture detection systems have been presented which use a camera to visually detect the gestures and tiny projectors to augment nearby surfaces and real-world objects with digital information. Still, current approaches rely on laptop computers restricting the systems ’ mobility and usability. In this paper, we present a framework for spotting hand gestures that is based on a mobile phone, its built-in camera and an attached mobile projector as medium for visual feedback. Other existing mobile applications can simply connect to our framework and thus, become gestureaware. The proposed framework will allow us to easily and fast create gesture-enabled research prototypes shifting the user’s attention from the device to the content. 1.