A point cloud as an information-intensive 3D representation usually requires a large amount of transmission, storage and computing resources, which seriously hinder its usage in many emerging fields. In this paper, we propose a novel point cloud simplification method, Approximate Intrinsic Voxel Structure (AIVS), to meet the diverse demands in real-world application scenarios. The method includes point cloud pre-processing (denoising and down-sampling), AIVS-based realization for isotropic simplification and flexible simplification with intrinsic control of point distance. To demonstrate the effectiveness of the proposed AIVS-based method, we conducted extensive experiments by comparing it with several relevant point cloud simplification methods on three public datasets, including Stanford, SHREC, and RGB-D scene models. The experimental results indicate that AIVS has great advantages over peers in terms of moving least squares (MLS) surface approximation quality, curvature-sensitive sampling, sharp-feature keeping and processing speed. The source code of the proposed method is publicly available. ( https://github.com/vvvwo/AIVS-project ).

Approximate Intrinsic Voxel Structure for Point Cloud Simplification

A New Point Cloud Simplification Method with Feature and Integrity Preservation by Partition Strategy

This paper proposed a new approach used for tracking multi-pith within one mixture speech signal. In this method, we employed a novel continuous correlation feature for calculating pitch model. This feature not only represents the harmonicity but also includes the information of spectral continuity, and hence improving the accuracy of the multi-pitch estimate. A DBNs and HMM hybrid model was further utilized to construct pitch models for determining pitch states and search for the best pitch state sequence. The new approach has been evaluated on mixture speech data and the results demonstrated its efficiency.

Multipitch tracking with continuous correlation feature and hybrid DBNS/HMM model

There exists two shortcomings in the process of mining association rules from AIS data of ships by traditional method based on support and confidence: First, the types and quantities of ships involved in the AIS database are numerous. The period of broadcasting AIS may be different from each ship, and the number of generated transactions may also be different. if the count of the transaction containing one itemset is less but the relationship between items in it is very close, the criterion with high support threshold may overlook it directly. However, this kind of itemsets is significantly more representative and valuable. Conversely, if the itemset occurs more frequently but the relationship between items of it is not close, the traditional criterion method will also discriminate it as a frequent itemset. But this kind of itemset is of little significance and credibility; Second, The association rule mining method based on support and confidence may obtain the rules that users are not interested in or even inaccurate. To solve these problems more effectively, the ships' association rule mining algorithm based on Adaptive Support Threshold and Arcsin Interestingness (ASM-AI Algorithm) is presented in this paper. On the one hand, each candidate itemset will have the different adaptive support threshold to solve the two misjudgment problems, and the paper demonstrates that the new discrimination constraint has the antimonotonicity. On the other hand, the concept of Minimum Lift Function is introduced as an index to evaluate the credibility of association rule discrimination method and the Arcsin Interestingness is proposed as a new interestingness measure. It is proved by mathematical deduction that compared with some existing research results, this method can improve the reliability of association rules. Furthermore, the concepts of Lift are proposed to evaluate the performance of the algorithm's result. Finally, the real AIS data is used to test algorithm performance and the result shows that the new discrimination criteria of the ASM-AI Algorithm is more stringent, the more reliable association rules can be found out and the lift of association rules obtained by ASM-AI Algorithm is generally larger than the existing research's results.

Ships' Association Rule Mining Based on Adaptive Support Threshold and Arcsin Interestingness

A method and an apparatus for uncertainty modeling of a point cloud and a non-transient storage medium are provided. The method finds the neighboring points for each point; fits a local quadratic surface for each point; and generates a Gaussian Model (GM) for representing the uncertainty modeling of the local surface at each point. The GM is constructed in a piece-wise manner on the underlying surface of the point cloud, which will accurately model the surface uncertainty.

Method and apparatus for uncertainty modeling of point cloud

In order to highlight the characteristic region of 3D models, this paper proposes a point cloud data simplification algorithm based on Gaussian curvature. In order to find the nearest K points, we need to divide the space of point cloud according to 3D cube algorithm firstly. Secondly, we build a curved surface based on normal constraints so as to get the Gaussian curvature of the nearest K points. Then we simplify cloud point based on evaluation function which is derived by Gaussian curvature. At last, we construct 3D model according to the simplified point data. Experiments show that our method can well preserve the point cloud in characteristic area.

Simplification of point cloud data based on Gaussian curvature

A pitch tracking algorithm combining both spectral and temporal method is presented in this paper. The algorithm is robust for speech in various kinds of noisy environment at different signal to noise ratios. In frequency domain the low frequency region energy ratio was computed for voiced and unvoiced determination, correlation of multiple harmonic peaks was computed to find pitch candidate. Low frequency energy ratio and candidates obtained from frequency domain were used to guide the pitch candidate estimation in time domain which was performed both on the filtered speech and the filtered squared speech using normalized cross correlation function. Merit values associate with every candidate computed according to different conditions were computed. Then dynamic programming will used on these pitch merit pairs to find best pitch tracking. Performance of the method on different noisy speech was also evaluated in this paper.

A robust pitch tracking method in noisy environment

How to use the data mining technology, to discover the potential mass audit data in doubt is the hot spot of audit technology research. This paper proposes a new association rules mining algorithm based on interest measure, to set up a new interest model, to improve the accuracy of the strong association rules, and apply it to the audit of the business data of medical fees, to realize the application of data mining technology in the medical audit.

http://ieeexplore.ieee.org/iel7/6725841/6737779/06737820.pdf

Haishan Han

Papers

Simplification of point cloud data based on Gaussian curvature

A robust pitch tracking method in noisy environment

Association rules mining algorithm based on interest measure and its application in medical audit