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Grasping objects is a task that humans do without major concerns. This
results from learning and observing other skilled humans doing such task
and with previous information, unconsciously, we know how to pick up
different types of objects. However, grasping novel objects in unknown
positions for a robot is a complex task which encounters many problems,
such as the performance rates that are not perfect and the time consumption.
In this paper we present a method that complements the state-ofthe-
art grasping by removing the largest planar surface of the image of
the world before the grasp detector receives them. The proposed method
improves the performance rate and is also capable of reducing the time
consumption.