Basic Matching Algorithm Research Articles

Learnable Chamfer Distance for point cloud reconstruction

As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules. However, the static matching rules may deviate from actual shape differences. Although some works propose dynamically-updated learnable structures to replace matching rules, they need more iterations to converge well. In this work, we propose a simple but effective reconstruction loss, named Learnable Chamfer Distance (LCD) by dynamically paying attention to matching distances with different weight distributions controlled with a group of learnable networks. By training with adversarial strategy, LCD learns to search defects in reconstructed results and overcomes the weaknesses of static matching rules, while the performances at low iterations can also be guaranteed by the basic matching algorithm. Experiments on multiple reconstruction networks confirm that LCD can help achieve better reconstruction performances and extract more representative representations with faster convergence and comparable training efficiency.

Formalization of basic pattern matching algorithms in production systems

Currently, there are several models of formalization of production systems. The formalization in terms of the first-order logic is the most universal. However, such formalization is not provided for all inference machinery in production systems. In particular, among the basic pattern matching algorithms, the generalized formal description was proposed only for the Rete algorithm. In the paper, the formalized description of the production system and basic matching algorithms (Rete, Treat) for further resource intensity assessment at the design stage was presented in a single format. A formal presentation of the compilation and implementation of the data flow network of the Treat algorithm was proposed. Based on the proposed formalization, the memory consumption calculation model for the Treat algorithm was extended due to taking into account memory consumption to preserve the conflict set and agree variables in the terminal vertices of the data flow network. In the future, the proposed single format of the presentation of the basic algorithms can be used for the resource intensity assessment of the Treat algorithm based on the mathematical models proposed for the Rete algorithm.

Using Local Planar Geometric Invariants to Match and Model Images of Line Segments

Image matching consists of finding features in different images that represent the same feature of the observed scene. It is a basic process in vision whenever several images are used. This paper describes a matching algorithm for lines segments in two images. The key idea of the algorithm is to assume that the apparent motion between the two images can be approximated by a planar geometric transformation (a similarity or an affine transformation) and to compute such an approximation. Under such an assumption, local planar invariants related the kind of transformation used as approximation, should have the same value in both images. Such invariants are computed for simple segment configurations in both images and matched according to their values. A global constraint is added to ensure a global coherency between all the possible matches: all the local matches must define approximately the same geometric transformation between the two images. These first matches are verified and completed using a better and more global approximation of the apparent motion by a planar homography and an estimate of the epipolar geometry. If more than two images are considered, they are initially matched pairwise; then global matches are deduced in a second step. Finally, from a set of images representing different aspects of an object, it is possible to compare them and to compute a model of each aspect using the matching algorithm. This work uses in a new way many elements already known in vision; some of the local planar invariants used here were presented as quasi-invariants by Binford and studied by Ben-Arie in his work on thepeaking effect. The algorithm itself uses other ideas coming from the geometric hashing and the Hough algorithms. Its main limitations come from the invariants used. They are really stable when they are computed for a planar object or for many man-made objects which contain many coplanar facets and elements. On the other hand, the algorithm will probably fail when used with images of very general polyhedrons. Its main advantages are that it still works even if the images are noisy and the polyhedral approximation of the contours is not exact, if the apparent motion between the images is not infinitesimal, if they are several different motions in the scene, and if the camera is uncalibrated and its motion unknown. The basic matching algorithm is presented in Section 2, the verification and completion stages in Section 3, the matching of several images is studied in Section 4 and the algorithm to model the different aspects of an object is presented in Section 5. Results obtained with the different algorithms are shown in the corresponding sections.