Motion estimation is a key problem in the analysis of image sequences. From a sequence of images we can only estimate an approximation of the image motion field called optical flow. We propose to improve optical flow estimation by including information from images of textural features. We compute the optical flow from intensity and textural images from first-order derivatives, then combine esti...

Large-eddy simulations are performed for compressible flow over a cylindrical turret with a flat optical window, with and without passive control in the upstream boundary layer. Statistics of the flow field and optical distortions are computed and compared with experimental measurements. Results show reasonable agreement with experimental data for the baseline case without control. When passive...

Accurate optical flow estimation of motion fields is crucial for video super-resolution algorithms. Existing algorithms for optical flow calculation may produce erroneous motion fields on complex dynamic scenes with multiple moving, occluding non-rigid objects. In this paper we propose to use so called optical flow reliability weights in order to reduce impact of erroneous motion vector estimat...

The seminal work of Horn and Schunck is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image...

The notion “Optical flow” refers to the apparent motion in the image plane produced by the projection of the real 3D motion onto the 2D image plane. The thesis at hand addresses postprocessing and restoration methods for arbitrarily computed optical flow fields. Many motion estimators have been proposed during the last three decades, but all of them suffer from shortcomings in difficult situati...

We describe how to build a VIDEOPLACE-like vision-driven user interface using “optical-flow” measurements. The optical-flow denotes the estimated movement of an image patch between two consecutive frames from a video sequence. Similar framework is used in a number of commercial visiondriven interactive computer games but the motion of the users is detected by examining the difference between tw...

Optical flow routing is an optical networks that technique used to ensure that the packet is always in the sequence order. This research is intended to ensure that the network traffic is enhanced of the quality of service (QoS). An analytical method is used in order to show its effectiveness of flow routing. There are three cases that have been investigated in this research investigation namely...

In this paper, we propose a new method to estimate the FOE (Focus of Expansion) from moving camera image sequence for vehicle motion understanding, which does not rely on optical flow vectors. Motion understanding of the camera is very important in many applications. Most of the conventional methods for detection and estimation of the position of FOE rely on estimation of optical flow vector in...

Determining optical flow, the pattern of apparent motion of objects caused by the relative motion between observer and objects in the scene, is a fundamental problem in computer vision. Given two images, goal is to compute the 2D motion field a projection of 3D velocities of surface points onto the imaging surface. Optical flow can be used in a wide range of higher level computer vision tasks, ...

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from mo...