Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion recovery from image sequences using only first order optical flow information
International Journal of Computer Vision
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Performance of optical flow techniques
International Journal of Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
Robot Vision
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Optical flow estimation is a recurrent problem in several disciplines and assumes a primary importance in a number of applicative fields such as medical imaging [12], computer vision [6], productive process control [4], etc. In this paper, a differential method for optical flow evaluation is being presented. It employs a new error formulation that ensures a more than satisfactory image reconstruction in those points which are free of motion discontinuity. A dynamic scheme of brightness-sample processing has been used to regularise the motion field. A technique based on the concurrent processing of sequences with multiple pairs of images has also been developed for improving detection and resolution of mobile objects on the scene, if they exist. This approach permits to detect motions ranging from a fraction of a pixel to a few pixels per frame. Good results, even on noisy sequences and without the need of a filtering pre-processing stage, can be achieved. The intrinsic method structure can be exploited for favourable implementation on multi-processor systems with a scalable degree of parallelism. Several sequences, some with noise and presenting various types of motions, have been used for evaluating the performances and the effectiveness of the method.