This paper presents a robust and rotation invariant local surface descriptor by encoding the position angles of neighboring points with a stable and unique local reference frame (LRF) into a 1D histogram. The whole procedure includes two stages: the first stage is to construct a unique LRF by performing eigenvalue decomposition on the covariance matrix formed using all the neighboring points on the local surface. On the second stage, the sphere support field of a key point was divided along the radius into several sphere shells which is similar with the Signature of Histograms OrienTations (SHOT). In each sphere shell, we calculate the cosine of the angles between the neighboring points and the x-axis, z-axis respectively to form two 1D histograms. Finally, all the 1D histograms were stitched together followed by a normalization to generate the local surface descriptor. Experiment results show that our proposed local feature descriptor is robust to noise and varying mesh-resolutions. Moreover, our local feature descriptor based 3D object recognition algorithm achieved a high average recognition rate of 98.9% on the whole UWA dataset.
Shape Matching under Affine Transformation (SMAT) is an important issue in shape analysis. Most of the existing SMAT methods are sensitive to noise or complicated because they usually need to extract the edge points or compute the high order function of the shape. To solve these problems, a new SMAT method which combines the low order shape normalization and the multi-scale area integral features is proposed. First, the shapes with affine transformation are normalized into their orthogonal representations according to the moments and an equivalent resample. This procedure transforms the shape by several linear operations: translations, scaling, and rotation, following by a resample operation. Second, the Multi-Scale Area Integral Features (MSAIF) of the shapes which are invariant to the orthogonal transformation (rotation and reflection transformation) are extracted. The MSAIF is a signature achieved through concatenating the area integral feature at a range of scales from fine to coarse. The area integral feature is an integration of the feature values, which are computed by convoluting the shape with an isotropic kernel and taking the complement, over the shape domain following by the normalization using the area of the shape. Finally, the matching of different shapes is performed according to the dissimilarity which is measured with the optimal transport distance. The performance of the proposed method is tested on the car dataset and the multi-view curve dataset. Experimental results show that the proposed method is efficient and robust, and can be used in many shape analysis works.
Template matching is a significant approach in machine vision due to its effectiveness and robustness. However, most of the template matching methods are so time consuming that they can’t be used to many real time applications. The closed contour matching method is a popular kind of template matching methods. This paper presents a new closed contour template matching method which is suitable for two dimensional objects. Coarse-to-fine searching strategy is used to improve the matching efficiency and a partial computation elimination scheme is proposed to further speed up the searching process. The method consists of offline model construction and online matching. In the process of model construction, triples and distance image are obtained from the template image. A certain number of triples which are composed by three points are created from the contour information that is extracted from the template image. The rule to select the three points is that the template contour is divided equally into three parts by these points. The distance image is obtained here by distance transform. Each point on the distance image represents the nearest distance between current point and the points on the template contour. During the process of matching, triples of the searching image are created with the same rule as the triples of the model. Through the similarity that is invariant to rotation, translation and scaling between triangles, the triples corresponding to the triples of the model are found. Then we can obtain the initial RST (rotation, translation and scaling) parameters mapping the searching contour to the template contour. In order to speed up the searching process, the points on the searching contour are sampled to reduce the number of the triples. To verify the RST parameters, the searching contour is projected into the distance image, and the mean distance can be computed rapidly by simple operations of addition and multiplication. In the fine searching process, the initial RST parameters are discrete to obtain the final accurate pose of the object. Experimental results show that the proposed method is reasonable and efficient, and can be used in many real time applications.