Lighting Uneven Conditions Of Optical Flow Measurement

In the fields of computer vision, many studies have been carried out to obtain information on the three-dimensional(3-D)environment from image sequences.One of the most important problems is to determine. First of all, we must distinguish between two concepts:optical flow field and motion field. When the object moves in front of the camera, or the camera moves in the environment, we will find that image is changing. Motion field depicts a 2-D projection of the instantaneous 3-D velocity of the corresponding point in the scene on the imaging surface (true motion velocity). Optical flow, on the other hand, is the distribution of apparent velocities of the moving brightness patterns in an image sequence. Generally, motion field and optical flow field are not equal. What we want to obtain is motion field,not optical flow.The motion field can be useful for realizing human visual functions such as target detection and tracking, segmentation and qualitative shape analysis. Motion compensation has received increasing attention recently in the areas of video communication and medical imaging. Motion compensation is useful in enhancement and restoration of image sequence, and in image compression. In scientific measurements, motion analysis is a powerful tool for extracting physical information from the visualized data. Additional quantities such as divergence, vorticity and pressure distribution can be calculated from the velocity data. In meteorology, information on wind vector is necessary for numerical forecasting of the weather, which can be estimated from satellite image data by using a motion analysis method.In 1981, Horn and Schunck first introduct the basic optical flow constraint equation and the basic algorithm of optical flow estimation,which associated two-dimensional velocity field with the brightness. Barron and others summarized the optical flow technology and divided them into in accordance with the difference between them is into four categories:gradient-based approach, gradient-based, correlation-based, energy-based and phase-based methods by theoretical basis and mathematical methods. In the actual scene analysis however, the performance of conventional methods is not satisfactory.There exists the influence of non-ideal conditions in the actual scene.For example:non-uniform illumination, occlusions,multiple optical flow, non-rigid motion of object, and diffusion. If we want to obtain reliable optical flow, we should take into account such problems. This dissertation is an attempt to develop a new framework for optical flow computation under non-uniform illumination (true motion fields).The new framework developed in this dissertation views the problem of recovering motion fields from a sequence of images under non-uniform illumination.We proposed one approaches. The method introduces the extended constraint equation with spatio-temporal optimization(including local and global optimization). The performance of the proposed methods is confirmed by comparison with conventional optical flow computation techniques on a series of synthetic and real image sequences.