Use Of Optical Flow Research Articles

Oscillations in Corn Seedling Growth as Measured by Optical Flow

Growth of corn seedlings during the coleoptile stage was measured using optical flow. The measurement system was comprised of a digital camera, computer, and related software and measured growth in a continuous, noncontact manner. The use of optical flow to measure shoot elongation, i.e., image motion of the elongating seedling, was most easily computed when there were large spatiotemporal variations of the motion of the corn seedling against the background. The sensitivity of the measurement technique was in the micron per second range. Seedling growth did not occur in a smooth even manner, rather, growth was a series of varying bursts or waves of expansion that appeared to be affected by the physical growth or development of the leaves. Spectral analysis techniques were applied to extract the underlying signal from the observed time series of seedling growth rate and angle.

Optic flow and autonomous navigation.

Many animals, especially insects, compute and use optic flow to control their motion direction and to avoid obstacles. Recent advances in computer vision have shown that an adequate optic flow can be computed from image sequences. Therefore studying whether artificial systems, such as robots, can use optic flow for similar purposes is of particular interest. Experiments are reviewed that suggest the possible use of optic flow for the navigation of a robot moving in indoor and outdoor environments. The optic flow is used to detect and localise obstacles in indoor scenes, such as corridors, offices, and laboratories. These routines are based on the computation of a reduced optic flow. The robot is usually able to avoid large obstacles such as a chair or a person. The avoidance performances of the proposed algorithm critically depend on the optomotor reaction of the robot. The optic flow can be used to understand the ego-motion in outdoor scenes, that is, to obtain information on the absolute velocity of the moving vehicle and to detect the presence of other moving objects. A critical step is the correction of the optic flow for shocks and vibrations present during image acquisition. The results obtained suggest that optic flow can be successfully used by biological and artificial systems to control their navigation. Moreover, both systems require fast and accurate optomotor reactions and need to compensate for the instability of the viewed world.

Motion analysis of long image sequence flow

There is much evidence to support the validity of the use of optical flow as a basis for many computational vision tasks. However, optical flow methods are problematic due to inherent errors in the computational methods involved in the processing. Many of the problems associated with the analysis of image flow can be alleviated if information extracted from a long sequences of image is utilized as a basis for the derivation of the flow information, rather than simple between-frame processing. We present discussion of work undertaken to study implementation of a method to provide the necessary long-sequence information. Using an algorithm providing results similar to those achieved in multi-image, overlay photography, we combine features from several sequential images, with each pair of successive images separated by a short time interval. Features are then associated according to the object features that generated them, and the resulting point lists are then analyzed to determine the long-sequence flow information. We apply flow-based computational vision methods (previously implemented using only simple flow) to the long-sequence flow. Since the long-sequence flow overcomes many of the effects of noise and quantization errors, the results is the derivation of robust visual information.

The role of optical velocity in the control of stance.

Two experiments were designed to investigate the use of optical flow for postural control as a function of its velocity, geometry, and retinal placement. In the first experiment, subjects were exposed to a room that moved at a velocity below the reported threshold for detection of point-light motion. In the second, the room was moved so that optical velocities were above this threshold. Compensatory sway was assessed for both lamellar and radial flow to central and peripheral areas of the retina. Compensatory sway was elicited with optical velocities below the point-light motion threshold, suggesting that this threshold is not relevant to the detection of egomotion. The results also indicate that the retinal periphery does not detect posture-relevant information from flow fields with a radial dynamic structure. Since the higher velocities used in the second experiment exceeded those generated by natural postural instabilities, it was concluded that radial flow in the retinal periphery is not used as a source of information for normal postural maintenance, and therefore that flow structure is an important factor in the detection and control of egomotion.