Monocular visual odometry as the front end of SLAM, its task is to estimate the movement of cameras between adjacent images. One of the most important steps in visual odometry is to find feature correspondence between neighboring frames. In finding the feature matches, the search region can not be too small in order to ensure a large number of matches to be found. On the other hand, it could not be too large, otherwise it would generate many false matches and will deteriorate the entire system performance. In this paper, we propose to add a dense optical flow computation step before the matching step. The dense optical flow is computed through optimization from the whole image. The image context will help to ensure that its result will not deviate too far away from the true position, and thus provides an initial position estimate for the feature matches. Experiments on KITTI dataset show that the proposed approach can indeed narrow down the search range for finding feature correspondence while still being able to find a large number of correct matches.