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In this brief, a homography-based adaptive visual servo controller is developed to enable a robot end-effector to track a desired Euclidean trajectory as determined by a sequence of images for both the camera-in-hand and fixed-camera configurations. To achieve the objectives, a Lyapunov-based adaptive control strategy is employed to actively compensate for the lack of unknown depth measurements and the lack of an object model. The error systems are constructed as a hybrid of pixel information and reconstructed Euclidean variables obtained by comparing the images and decomposing a homographic relationship. Simulation results are provided to demonstrate the performance of the developed controller for the fixed camera configuration.