Image Velocity Field Research Articles

Effect of tracking strategies on the velocity structure of two-dimensional image sequences

We investigate the effect of different tracking strategies, such as local and full-field tracking, on the mean and variance of the image velocity field. We show that while local tracking reduces the velocity variability in an eccentricity-dependent manner, full-field tracking reduces velocity variability equally across the image. We test our predictions with digitized image sequences.

The solar activity-oriented Japanese programme

There are two activity-oriented programmes in Japan; i) space programme, and ii) the programme of ground-based optical and radio observations. The space programme is carried out with Solar-A satellite, which will be launched in August/September 1991, and four instruments such as Hard X-ray Telescope (HXT), Soft X-ray Telescope (SXT), Bragg Crystal Spectrometer (BCS), and Wide-Band Spectrometer (WBS) are on board Solar-A satellite. A flare telescope is newly set up at Mitaka in order to observe magnetic field, velocity field, Hα features, and white light images in a flaring region. A radio heliograph is constructing at Nobeyama in order to observe flares at 17GHz with a spatial resolution of 7.3 arc sec × 16.5 arc sec and a time resolution of 50 ms.

Velocity field computation using neural networks

A new approach for optical flow (image velocity) fields computation is presented using computational neural networks. The computational procedure consists of three stages: estimation of the parameters of the neural network model, dynamic measurement of the perpendicular velocity components of the contours or region boundaries and computation of the image velocity fields. The parameters are estimated by comparing the energy function of the neural network with a constrained error function. The nonlinear velocity fields computation method is then carried out iteratively by using a dynamic algorithm to minimise the energy function simultaneously with the dynamic measurement of the perpendicular velocity components by a dynamic procedure. Experiments generate velocity fields that are meaningful and consistent with visual perception.

Dynamic estimation of optical flow field using objective functions

Optical flow (image velocity) fields are computed and interpreted from an image sequence by incorporating knowledge of object kinematics. Linear and quadratic objective functions are defined by considering the kinematics, and the function parameters are estimated simultaneously with the computation of the velocity field by relaxation. The objective functions provide an interpretation of the dynamic scenes and, at the same time, serve as the smoothness constraints. The computation is initially based on measured perpendicular velocity components of contours or region boundaries which, due to the ‘aperture problem’, are theoretically not the true perpendicular velocity components. This difficulty is alleviated by introducing a dynamic procedure for the measurement of the perpendicular components. Experiments on using objective functions for synthetic and real images of translating and rotating objects generated velocity fields that are meaningful and consistent with visual perception.

Image flow segmentation and estimation by constraint line clustering

Image flow is the velocity field in the image plane caused by the motion of the observer, objects in the scene, or apparent motion, and can contain discontinuities due to object occlusion in the scene. An algorithm that can estimate the image flow velocity field when there are discontinuities due to occlusions is described. The constraint line clustering algorithm uses a statistical test to estimate the image flow velocity field in the presence of step discontinuities in the image irradiance or velocity field. Particular emphasis is placed on motion estimation and segmentation in situations such as random dot patterns where motion is the only cue to segmentation. Experimental results on a demanding synthetic test case and a real image are presented. A smoothing algorithm for improving the velocity field estimate is also described. The smoothing algorithm constructs a smooth estimate of the velocity field by approximating a surface between step discontinuities. It is noted that the velocity field estimate can be improved using surface reconstruction between velocity field boundaries. >