Abstract. Robust stereo and optical flow disparity matching is essential for computer vision applications with varying illumination conditions. Most robust disparity matching algorithms rely on computationally expensive normalized variants of the brightness constancy assumption to compute the matching criterion. In this paper, we reinvestigate the removal of global and large area illumination artifacts, such as vignetting, camera gain, and shading reflections, by directly modifying the input images. We show that this significantly reduces violations of the brightness constancy assumption, while maintaining the information content in the images. In particular, we define metrics and perform a methodical evaluation to firstly identify the loss of information in the images, and secondly determine the reduction of brightness constancy violations. Thirdly, we experimentally validate that modifying the input images yields robustness against illumination artifacts for optical flow disparity matching. 1

Abstract. Optical flow is a highly researched area in low-level computer vision. It is a complex problem which tries to solve a 2D search in continuous space, while the input data is 2D discrete data. Furthermore, the latest representations of optical flow use Hue-Saturation-Value (HSV) colour circles, to effectively convey direction and magnitude of vectors. The major assumption in most optical flow applications is the intensity consistency assumption, introduced by Horn and Schunck. This constraint is often violated in practice. This paper proposes and generalises one such approach; using residual images (high-frequencies) of images, to remove the illumination differences between corresponding images. 1

The paper discusses various options for testing correspondence algorithms in stereo or motion analysis, designed or considered for vision-based driver assistance. The main focus is on testing on video sequences of real-world data. The authors suggest the classification of recorded video data into situations, defined by a co-occurrence of some events in recorded traffic scenes. About 100 to 200 frames (or 4 to 8 seconds of recording) are considered to be a basic sequence, to be identified with one particular situation. Future testing is expected to be on data that is reporting on hours of driving; multiple hours long video data may be segmented into basic sequences, and classified into situations. The paper prepares for this expected development. The paper explains the use of currently already available EISATS test data, aiming for such “wide-angle ” tests. The paper uses three different evaluation approaches for demonstrating (by means of EISATS examples) ideas, difficulties, and possible ways in this future field of extensive performance tests in vision-based driver assistance. The paper also contributes with proposals of evaluation techniques, especially in the case of lack of ground truth. The study shows that the complexity of real-world data often does not support an identification of absolute rankings of correspondence techniques; this is true already for a small set of selected situations. It is suggested that correspondence techniques need to be adaptively chosen in real time using fast situation classifiers (e.g., based on a few feature distributions).