The corner is an important local feature of image. To avoid the disadvantages of using the single feature to detect the corner points

a new algorithm based on multi feature is proposed in this paper. In this algorithm

intensity feature and edge feature are contributed to corner detection. First

a fast adaptive SUSAN principle

which utilizes the local gray level feature directly

is proposed for detecting the candidate corners. This improved method can detect features

such as corners

edges and intersections

in different contrast image automatically. For detecting the corners on blurry edges

the candidate corners would include some edge points as a result of reducing the detection threshold. These candidate corners

which include true corners

some edge points and a few false points

are arrayed along the boundary trend by the method of edge element. Through these arrayed points

the angles between approximate straight edge lines are calculated to be as the criterion of determining a corner. Those edge points are removed since they have not significant discontinuous changes in the direction of boundary

i.e. the angles of them are not acute enough

and the false corners due to quantization also are removed by our method. After these steps

the true corners are reserved. The experimental results showed this corner detection method having good capabilities of detection and localization in different contrast image.