IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Camera Response from Images: What Is Knowable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
RGB calibration for color image analysis in machine vision
IEEE Transactions on Image Processing
A novel spatio-temporal approach to handle occlusions in vehicle tracking
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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Imaging system (camera) noise represents a well known source of disturbing artifacts in many algorithms dealing with shape from shading and motion detection, for instance. Some approaches are known to infer the camera noise characteristics. However, all of them rely on a priori assumptions which yield methods depending on the imaging device. In this paper we present a simple method to infer a non-parametric statistical model of the temporal camera noise, based on a inblack-boxli modelling of the imaging system. The model is extracted directly from the pixel intensity variations measured along a short sequence of an arbitrary scene. Extensive experiments accomplished on different sequences acquired with the same camera show that the extracted noise model is strongly scene-independent, thus validating the approach.