The edge‐aware bilateral filter has been demonstrated to be effective for preserving depth edges, and disparity maps obtained from Fast Bilateral Stereo (FBS) have enhanced the efficiency of algorithm and the robustness to noise. However, they also lead to a non‐perfect localisation of discontinuities. To overcome this issue, a new bilateral filtering based cost aggregation utilising colour statistical classification and similarity measurement within annular blocks is proposed in this study. We have adopted the similarity of histograms evaluated by Earth Mover Distance (EMD) to obtain the raw matching cost in the raised annular block, since histograms are very effective and efficient in capturing the distribution characteristics of visual features. For the weights aggregation, the spatial weight is assumed to be a constant. The colour weight is calculated by using a cluster‐mean‐value strategy, which is implemented by the local colour histogram. It improves the accuracy in the discontinuous areas. Computation redundancy is reduced by disparity candidate selection using the local minimal relevancy in the corresponding annular blocks. We use the efficiency and accuracy to demonstrate the performance of our proposed method. Experimental results have shown that the proposed method reduces the mismatch at depth discontinuous and the computation complexity significantly.

A new method to construct mary (for any) symmetric interpolatory subdivision scheme for curves design based on the orthonormal refinable function is introduced. In this paper, we study the properties of the autocorrelation function of a m-band compactly supported orthonormal refinable function, then we construct a m-ary interpolatory subdivision scheme based on these properties. The family of the subdivision schemes which we construct is convergent naturally. The feasibility of our method is exposed in two examples.

In this paper, we present an efficient 3-DOF registration method to align two overlapping point clouds captured by a range sensor that experiences locally planar motion such as in many robotics applications. The algorithm follows the RANSAC framework and is based on finding a pair of congruent triangles in the source and target clouds. With the assumption of planar sensor motion, corresponding vertices of the congruent triangles must have similar elevation values, allowing our algorithm to identify them efficiently. Specifically, given a triangle in the source cloud, our algorithm first finds two vertices of a candidate congruent triangle in the target cloud and then uses the third vertex to verify, minimizing the complexity of the geometric base as well as the expected time of sampling successfully a matching congruent triangle. To improve the performance of our algorithm further, our verification of a hypothetical alignment transformation proceeds first locally by using only the points near the vertices of the congruent triangles before involving the entire cloud. Our experimental results show that the proposed algorithm outperforms state-of-the-art registration algorithms in the case of 3-DOF planar sensor motion.

A recognition algorithm for a type of coded target widely used in photogrammetry is presented in this paper. An efficient feature vector is first proposed to describe the targets, then mathematical morphological operations are used for clustering to location the the targets in the image. Finally, by ellipse fitting, each coded target in the image is recognized. Experimental results in both the better and worse case demonstrate the effectiveness of our approach.