Research On Three Dimensions Object Recognition From Range Images

Making use of three-dimensional target data obtained from the three-dimensional laser radar to recognize three-dimensional object is the core process of lasted precision guided weapon technology. In three-dimensional computer vision, three-dimensional object recognition plays a prominent role and is a hard and challenging task. This thesis takes three-dimensional object recognition as the study's core and has a detailed in-depth study to three-dimensional object recognition based on range images. The main work is summarized as follows:(1) In three-dimensional computer vision range image's representation is the base of three dimensional object recognition. These thesis summaries the range image's representation as three types, these are pseudo-gray representation, gird representation and point cloud representation.(2) According to the properties of range image with pseudo-gray representation, this thesis studies three methods of calculation of curvature properties, they are respectively direct calculation method; Numerical estimates method and Surface Fitting methods. since the existing fitting methods are local fitting methods, this thesis presents a new global surface fitting scheme by using moving least squares methods. Compared with the standard least squares, moving least squares method can obtain an implicit general representation and better fitting accuracy. This thesis generalizes the moving least squares methods and deduces a general model frame of surface fitting. This frame not only demonstrates the Theoretical difference of all the surface fitting methods and also indicates a direction to design new surface fitting method.(3) As we all know, the existing representations of free-form 3D objects are local representations .In view of this, this thesis proposes a new three-dimensional object recognition method based on shape index histogram and range histogram. This method not only avoids the search of characteristic corresponding points and satisfies to a certain extent rotation invariance, translation invariance and scale invariance.(4) In three-dimensional computer vision constructing pure invariant is very difficult. In view of this, the paper defines a correlation invariability with a relaxative standard and proposes a new three-dimensional object recognition method based on Tsallis entropy and spin images. This method achieve the rapid decay of data and avoid search of characteristic corresponding points, moreover this representation possesses rotation invariance, translation invariance and scale invariance. Theoretical analysis and simulations prove this method's high performance in three dimensional object recognition.