Corners are an important feature of objects in images and they are widely used. In the field of computer vision. There are many applications that rely on the successful detection of corners

such as image matching

panoramic stitching

3D modelling

object recognition

and motion tracking. The multi-scale algorithm based on curvature scale space is generally regarded as an effective method for finding corners in images. It can detect both fine and coarse features. However

different options of the selected scale will result in the leakage detection of the corner or even the wrong corner. Therefore

we describe a new corner detection method based on the curvature scale space and the direction of Freeman code statistics. This corner detection method is based on the improved curvature scale space corner detection algorithm proposed by He et al. First

it uses the Canny edge detector to get the binary edge map

and computes the curvature at a relatively low scale for each edge. It can pick the candidate corners set in this relatively low scale. In this way

it picks enough corners

unfortunately also containing wrong corners

such as round corners and false corners which are caused by the sharp variation edges. Then

in order to remove those wrong corners

we propose an adaptive threshold and statistical direction of chain code method. We compute a threshold adaptively according to the mean curvature within a specific region. Round corners are removed by comparing the curvature of the corner candidates with the adaptive threshold. We also eliminate the false corners by evaluating the difference of each candidate corner

the difference evaluating by using the Freeman code direction statistics. In the conventional corner detection based on curvature scale space

different options of the selected scale will result in the leakage detection of the corner or even the wrong corner. For example using the high scale easily leads to real corners missing

using the low scale will detect more wrong corners. In order to find more real corners

this method uses the low scale to detect corners and proposes a novel method to remove those wrong corners. The evaluation of this algorithm is taken from several aspects

such as the accuracy of detection

the wrong detection

miss detection and the computing time.The experimental results show that this proposed corner detection method overcomes others methods weakness such as accuracy

false corners and so on. Compared with other algorithms

the new method has high accuracy

low error rate and there is not a significant increase in computing time. We propose a novel method for detecting corners. This method adopts the low curvature scale and has the capacity of detecting more corners and accordingly reducing corner leakage detection rate;by calculating elliptical corner adaptive threshold

it can delete elliptical corner;by using the Freeman code direction statistics

it can also eliminate the false corner;thus undoubtedly improving the accuracy of corner detection. The experiments have fully prored that the corner detection algorithm we proposed in this paper has high efficiency and accuracy compared with other methods.