Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as cloth handling, cable routing, surgical operation etc. The sensing in DOM is now considered as one of the major challenges in robotics due to the complex dynamics and high degree of freedom of deformable objects. One challenge is to find a suitable representation with low dimensionality and reliable accuracy. The aim of this thesis to develop an algorithm to represent the state of the deformable objects like cloth in low-dimensional vectors, together with a framework based on visual servoing to flatten cloth-like objects. We present a novel pipeline for cloth flattening, which determines a stretching direction (in 2D vector) and an operation point for the robot to removes the wrinkles. The performance of the perception algorithm is validated in simulation and real-world experiment. The whole framework is evaluated in the real-world experiment, which is compared with a human operator. The results show that our framework efficiently determines the direction of wrinkles on the cloth in the simulation as well as the real robot experiment. Besides, the proposed framework has a good performance close to that of a human operator in terms of cloth flattening tasks.