D Calow | PubMed Health

PubMed2005

Local image structures and optic flow estimation.

Different kinds of local image structures (such as homogeneous, edge-like and junction-like patches) can be distinguished by the intrinsic dimensionality of the local signals. Intrinsic dimensionality makes use of variance from a point and a line in spectral representation of the signal in order to classify it as homogeneous, edge-like or junction-like. The concept of intrinsic dimensionality has been mostly exercised using discrete formulations; however, recent work has introduced a continuous definition. The current study analyzes the distribution of local patches in natural images according to this continuous understanding of intrinsic dimensionality. This distribution reveals specific patterns than can be also associated to local image structures established in computer vision and which can be related to orientation and optic flow features. In particular, we link quantitative and qualitative properties of optic-flow error estimates to these patterns. In this way, we also introduce a new tool for better analysis of optic flow algorithms.

Algorithms↗

PubMed2005

Biologically motivated space-variant filtering for robust optic flow processing.

We describe and test a biologically motivated space-variant filtering method for decreasing the noise in optic flow fields. Our filter model adopts certain properties of a particular motion-sensitive area of the brain (area MT), which averages the incoming motion signals over receptive fields, the sizes of which increase with the distance from the center of the projection. We use heading estimation from optic flow as a criterion to evaluate the improvement of the filtered flow field. The tests are conducted on flow fields calculated with a standard flow algorithm from image sequences. We use two different sets of image sequences. The first set is recorded by a camera which is installed in a moving car. The second set is derived from a database containing three dimensional data and reflectance information from natural scenes. The latter set guarantees full control of the camera motion and ground truth about the flow field and the heading. We test the space-variant filtering method by comparing heading estimation results between space-variant filtered flow, flow filtered by averaging over domains of the visual field with constant size (constant filtering) and raw unfiltered flow. Because of noise and the aperture problem the heading estimates obtained from the raw flows are often unreliable. Estimated heading differs widely for different sub-sampled calculations. In contrast, the results obtained from the filtered flows are much less variable and therefore more consistent. Furthermore, we find a significant improvement of the results obtained from the space-variant filtered flow compared to the constant filtered flow. We suggest extensions to the space-variant filtering procedure that take other properties of motion representation in area MT into account.

Algorithms↗