Face tracking using a hyperbolic catadioptric omnidirectional system

In the first part of this paper, we present a brief review on catadioptric omnidirectional systems. The special case of the hyperbolic omnidirectional system is analysed in depth. The literature shows that a hyperboloidal mirror has two clear advantages over alternative geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the image resolution is uniformly distributed along the mirror’s radius [2]. In the second part of this paper we show empirical results for the detection and tracking of faces from the omnidirectional images using Viola-Jones method. Both panoramic and perspective projections, extracted from the omnidirectional image, were used for that purpose. The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection of the image. In order to avoid losing or duplicating detections, the panoramic projection was extended horizontally. Duplications were eliminated based on the ROIs established by previous detections. After a confirmed detection, faces were tracked from perspective projections (which are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt of each virtual camera was determined by the ROIs previously computed on the panoramic image. The results show that, when using a careful combination of the two projections, good frame rates can be achieved in the task of tracking faces reliably

A Fisher-Rao metric for paracatadioptric images of lines

In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied. The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and based on the Hough transform

Capturing Panoramic Depth Images with a Single Standard Camera

In this paper we present a panoramic depth imaging system. The system is mosaic-based which means that we use a single rotating camera and assemble the captured images in a mosaic. Due to a setoff of the camera’s optical center from the rotational center of the system we are able to capture the motion parallax effect which enables the stereo reconstruction. The camera is rotating on a circular path with the step defined by an angle equivalent to one column of the captured image. The equation for depth estimation can be easily extracted from system geometry. To find the corresponding points on a stereo pair of panoramic images the epipolar geometry needs to be determined. It can be shown that the epipolar geometry is very simple if we are doing the reconstruction based on a symmetric pair of stereo panoramic images. We get a symmetric pair of stereo panoramic images when we take symmetric columns on the left and on the right side from the captured image center column. Epipolar lines of the symmetrical pair of panoramic images are image rows. We focused mainly on the system analysis. The system performs well in the reconstruction of small indoor spaces

04251 -- Imaging Beyond the Pinhole Camera

From 13.06.04 to 18.06.04, the Dagstuhl Seminar 04251 ``Imaging Beyond the Pin-hole Camera. 12th Seminar on Theoretical Foundations of Computer Vision\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Dynamic Programming and Skyline Extraction in Catadioptric Infrared Images

International audienceUnmanned Aerial Vehicles (UAV) are the subject of an increasing interest in many applications and a key requirement for autonomous navigation is the attitude/position stabilization of the vehicle. Some previous works have suggested using catadioptric vision, instead of traditional perspective cameras, in order to gather much more information from the environment and therefore improve the robustness of the UAV attitude/position estimation. This paper belongs to a series of recent publications of our research group concerning catadioptric vision for UAVs. Currently, we focus on the extraction of skyline in catadioptric images since it provides important information about the attitude/position of the UAV. For example, the DEM-based methods can match the extracted skyline with a Digital Elevation Map (DEM) by process of registration, which permits to estimate the attitude and the position of the camera. Like any standard cameras, catadioptric systems cannot work in low luminosity situations because they are based on visible light. To overcome this important limitation, in this paper, we propose using a catadioptric infrared camera and extending one of our methods of skyline detection towards catadioptric infrared images. The task of extracting the best skyline in images is usually converted in an energy minimization problem that can be solved by dynamic programming. The major contribution of this paper is the extension of dynamic programming for catadioptric images using an adapted neighborhood and an appropriate scanning direction. Finally, we present some experimental results to demonstrate the validity of our approach