International audience

The paper presents a video surveillance system which is able to measure the load displacements in an aircraft cargo area as well as the appearance of fires (hot surfaces, flame and smoke). To benefit from both measurements and cost savings, the system is based on an uncooled CCD camera operating in a near infrared (NIR) spectral band. In this article, we focus on load displacement measurements. The cargo compartment is not pressurized and the temperature is not controlled, thus it may vary from -55/spl deg/C to 85/spl deg/C. The influence of spectral band and temperature variations on a spatial resolution camera is studied using the physical models of a CCD camera. The classical camera geometric model is modified to take into account these variations. After the load detection, the geometric model is used to localize the load in the cargo area. Finally, we present the image processing software which computes the 3D load displacement.

An important task in most 3D vision systems is camera calibration. Many camera models, numerical methods and experimental set-ups have been proposed in the literature to solve the calibration problem. We have analysed and tried many methods, and we conclude that the main problems lie in the choice of the numerical methods and on the calibration object. We propose in this paper a method which is based on a camera model that incorporates lens distortion, and involves a nonlinear minimization technique which can be performed using multiple views of a single 2D object and subpixel feature extraction. We present an application for which only a 2D calibration object can be used.

The authors show how color and texture image segmentation, automatic image classification, camera calibration, and 3D scene representation can cooperate to solve a complex problem such as selective cutting. Attention is focused on the field of ore recognition, where significant improvement has been obtained by texture information. The research project considered involves the automation of the cutting operation of a roadheader for selective cutting in an underground potash mine near Barcelona in Spain. The system described is based on the use of computer vision to discriminate the different ore types found in the face (sylvinite, carnalite, and salt). Using the information about the ore distribution, paths are then planned for the computer-controlled cutting boom. It was shown that color information was important for minerals identification but texture information was absolutely necessary to get a good identification in all cases. The two kinds of information cooperate in an automatic image classification algorithm, which has been validated on many images of the mine face.>