Improved Matching Method Research Articles

A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data.

Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method.

A One-Shot FPGA Based Depth Acquisition System

This paper introduces a field-programmable gate array (FPGA)-based one-shot depth acquisition system, which is a kind of Kinect-like Depth sensing. The proposed implementation integrates the random binary pattern with an improved matching method, which is able to obtain dense and precise depth maps. Scene images captured by a camera are transferred to FPGA (Xilinx Virtex7 XC7VX690T-2FFG1761C) through a CamerLink interface, and the acquired depth results are sent to host computer through a high-speed USB3.0 interface. An elaborate FPGA architecture is designed to realize efficient and accurate calculations. The proposed system outperforms the existing stereo systems, not only on the quality of depth acquisition, but also on the reconstruction speed for which our system achieves 90 fps at a resolution of $1280\times960$ . Besides, Kinect-like depth sensing has an inherent advantage in deployments, much easier than other kinds of systems. Our proposed system has extensive applications not only in the field of vision processing, but also in the fields where block matching is needed.

An improved binocular visual odometry algorithm based on the Random Sample Consensus in visual navigation systems

PurposeThe purpose of this paper is to propose a binocular visual odometry algorithm based on the Random Sample Consensus (RANSAC) in visual navigation systems.Design/methodology/approachThe authors propose a novel binocular visual odometry algorithm based on features from accelerated segment test (FAST) extractor and an improved matching method based on the RANSAC. Firstly, features are detected by utilizing the FAST extractor. Secondly, the detected features are roughly matched by utilizing the distance ration of the nearest neighbor and the second nearest neighbor. Finally, wrong matched feature pairs are removed by using the RANSAC method to reduce the interference of error matchings.FindingsThe performance of this new algorithm has been examined by an actual experiment data. The results shown that not only the robustness of feature detection and matching can be enhanced but also the positioning error can be significantly reduced by utilizing this novel binocular visual odometry algorithm. The feasibility and effectiveness of the proposed matching method and the improved binocular visual odometry algorithm were also verified in this paper.Practical implicationsThis paper presents an improved binocular visual odometry algorithm which has been tested by real data. This algorithm can be used for outdoor vehicle navigation.Originality/valueA binocular visual odometer algorithm based on FAST extractor and RANSAC methods is proposed to improve the positioning accuracy and robustness. Experiment results have verified the effectiveness of the present visual odometer algorithm.

Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data

Abstract. In the context of geo-data infrastructures users may want to combine data from different sources and expect consistent data. If both datasets are maintained separately, different capturing methods and intervals leads to inconsistencies in geometry and semantic, even if the same reality has been modelled. Our project aims to automatically harmonize such datasets and to allow an efficient actualisation of the semantics. The application domain in our project is cadastral and topographic datasets. To resolve geometric conflicts between topographic and cadastral data a local nearest neighbour method was used to identify perpendicular distances between a node in the topographic and an edge in the cadastral dataset. The perpendicular distances are reduced iteratively in a constraint least squares adjustment (LSA) process moving the coordinates from node and edge towards each other. The adjustment result has to be checked for conflicts caused by the movement of the coordinates in the LSA. The correct choice of matching partners has a major influence on the result of the LSA. If wrong matching partners are linked a wrong adaptation is derived. Therefore we present an improved matching method, where we take distance, orientation and semantic similarity of the neighbouring objects into account. Using Machine Learning techniques we obtain corresponding land-use classes. From these a measurement for the semantic distance is derived. It is combined with the orientation difference to generate a matching probability for the two matching candidates. Examples show the benefit of the proposed similarity measure.

실시간 다중 객체인식 알고리즘 구현

This thesis propose a improved matching method for implementing an ORB algorithm based multi-object recognition. SURF algorithm that is well known in the object recognition algorithms is robust in object recognition. However, there is a disadvantage for real time operation because, SURF implemention requires a lot of computation. Therefore we propose a modified ORB algorithm which shows the result of almost 70% speed improvement by improving matching part to recognize multi object on real time.

Automatic Online Educational Game Content Creation by Identifying Similar Chinese Characters with Radical Extraction and Graph Matching Algorithms

One of the difficulties in learning Chinese characters is distinguishing similar characters. This can cause misunderstanding and miscommunication in daily life. Thus, it is important for students learning the Chinese language to be able to distinguish similar characters and understand their proper usage. In this paper, the authors propose a game style framework to train students to distinguish similar characters. A major component in this framework is the search for similar Chinese characters in the system. From the authors’ prior work, they find the similar characters by the radical information and stroke correspondence determination. This paper improves the stroke correspondence determination by using the attributed relational graph (ARG) matching algorithm that considers both the stroke and spatial relationship during matching. The experimental results show that the new proposed method is more accurate in finding similar Chinese characters. Additionally, the authors have implemented online educational games to train students to distinguish similar Chinese characters and made use of the improved matching method for creating the game content automatically.

An improved matching method to solve the diffraction—radiation problem of a ship moving in waves

On the underlying assumption that the disturbance of the far-field velocity potential caused by the motion of a ship is small and may be linearized, an improved matching method is developed. Two arrays of fundamental singularities are placed inside the ship hull, which satisfy the linear free surface condition outside the truncated fluid domain and the far-field radiation condition. The choice of fundamental singularity depends on the problem under investigation (e.g. pulsating, translating or translating and pulsating source). The unknown strength of these singularities and the near-field velocity potential are determined in a coupled manner. It is shown from numerical examples that the present method provides a more efficient and accurate representation of waves in the far field than conventional matching methods.