No abstract

Many objects in real world have circular feature. It is a difficult task to obtain the 2D-3D pose estimation using circular feature in challenging scenarios. This paper proposes a method to incorporate elliptic shape prior for object pose estimation using a level set method. The relationship between the projection of the circular feature of a 3D object and the signed distance function corresponding to it is analyzed to yield a 2D elliptic shape prior. The method employs the combination of the grayscale histogram, the intensities of edge, and the smoothness distribution as main image feature descriptors that define the image statistical measure model. The elliptic shape prior combined with the image statistical measure energy model drives the elliptic shape contour to the projection of the circular feature of the 3D object with the current pose into the image plane. These works effectively reduce the impacts of the challenging scenarios on the pose estimate results. In addition, the method utilizes particle filters that take into account the motion dynamics of the object among scene frames, and this work provides the robust method for object 2D-3D pose estimation using circular feature in a challenging environment. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.

The conventional Harris corner detection operator is improved to enhance the missing rate and the detection capability of false corners in this paper. With the materials recognition on automated logistics and packaging line as an example, the acquired images were firstly pretreated to achieve the grayscale images. The rotations of four different angles were performed by the steerable filter based on the grayscale and the corner points were detected. Finally the authenticity corner points were determined through the integrated logic operations. The image data pre-processed was detected using the improved Harris corner operator and compared with the data by the traditional corner detection operator. The false detection rate was decreased to 1.3 % and the missing rate reduced to 2.9% in the experiment. The results show that the improved operator has a strong capability of discerning authenticity angular point and this method can effectively improve the recognition accuracy of corner detection operator.

No abstract