scholarly journals SKELETON-BASED SHAPE CLASSIFICATION USING PATH SIMILARITY

2008 ◽  
Vol 22 (04) ◽  
pp. 733-746 ◽  
Author(s):  
XIANG BAI ◽  
XINGWEI YANG ◽  
DEGUANG YU ◽  
LONGIN JAN LATECKI
Keyword(s):  
Open Problem ◽  
Shape Representation ◽  
Bayesian Classifier ◽  
New Method ◽  
Shape Similarity ◽  
Shape Classification ◽  
Traditional Methods ◽  
Pruning Method

Most of the traditional methods for shape classification are based on contour. They often encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. It is well-known that shape representation based on skeletons is superior to contour based representation in such situations. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons, and matching of skeleton graphs is still an open problem. Using a new skeleton pruning method, we are able to obtain stable pruned skeletons even in the presence of significant contour distortions. We also propose a new method for matching of skeleton graphs. In contrast to most existing methods, it does not require converting of skeleton graphs to trees and it does not require any graph editing. Shape classification is done with Bayesian classifier. We present excellent classification results for complete shapes.

Shape Retrieval and Classification Based on Geodesic Paths in Skeleton Graphs

2012 ◽  
pp. 190-215
Author(s):  
Xiang Bai ◽  
Chunyuan Li ◽  
Xingwei Yang ◽  
Longin Jan Latecki
Keyword(s):  
Open Problem ◽  
Graph Matching ◽  
Shape Retrieval ◽  
Shape Similarity ◽  
Shape Classification ◽  
Graph Structure ◽  
Topological Graph ◽  
Direct Graph ◽  
Skeleton Graph ◽  
Geodesic Paths

Skeleton- is well-known to be superior to contour-based representation when shapes have large nonlinear variability, especially articulation. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons, and matching of skeleton graphs is still an open problem. To deal with this problem for shape retrieval, the authors first propose to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, they do not explicitly consider the topological graph structure. Their approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures, while the paths between their end nodes still remain similar. The proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases. The experimental results demonstrate that the method is able to produce correct results in the presence of articulations, stretching, and contour deformations. The authors also utilize the geodesic skeleton paths for shape classification. Similar to shape retrieval, direct graph matching algorithms like graph edit distance have great difficulties with the instability of the skeleton graph structure. In contrast, the representation based on skeleton paths remains stable. Therefore, a simple Bayesian classifier is able to obtain excellent shape classification results.

Binaural speech-to-noise loudness ratio at the speech reception threshold in vehicles

2021 ◽  
Vol 69 (2) ◽  
pp. 173-179
Author(s):  
Nilolina Samardzic ◽  
Brian C.J. Moore
Keyword(s):  
Speech Intelligibility ◽  
New Method ◽  
Traditional Methods ◽  
Speech Reception ◽  
Typical Error ◽  
Transmission Index ◽  
Speech Transmission ◽  
Articulation Index ◽  
Speech Intelligibility Index ◽  
Binaural Listening

Traditional methods for predicting the intelligibility of speech in the presence of noise inside a vehicle, such as the Articulation Index (AI), the Speech Intelligibility Index (SII), and the Speech Transmission Index (STI), are not accurate, probably because they do not take binaural listening into account; the signals reaching the two ears can differ markedly depending on the positions of the talker and listener. We propose a new method for predicting the intelligibility of speech in a vehicle, based on the ratio of the binaural loudness of the speech to the binaural loudness of the noise, each calculated using the method specified in ISO 532-2 (2017). The method was found to give accurate predictions of the speech reception threshold (SRT) measured under a variety of conditions and for different positions of the talker and listener in a car. The typical error in the predicted SRT was 1.3 dB, which is markedly smaller than estimated using the SII and STI (2.0 dB and 2.1 dB, respectively).

scholarly journals The Integration of Radiocarbon Dating in Archaeology

Radiocarbon ◽  
1983 ◽  
Vol 25 (2) ◽  
pp. 639-644 ◽  
Author(s):  
H T Waterbolk
Keyword(s):  
Radiocarbon Dating ◽  
The Other ◽  
New Method ◽  
Time Range ◽  
Traditional Methods ◽  
Radiocarbon Dates ◽  
Fully Integrated ◽  
Expected Time ◽  
The Past

In the past 30 years many hundreds of archaeologic samples have been dated by radiocarbon laboratories. Yet, one cannot say that 14C dating is fully integrated into archaeology. For many archaeologists, a 14C date is an outside expertise, for which they are grateful, when it provides the answer to an otherwise insoluble chronologic problem and when it falls within the expected time range. But if a 14C date contradicts other chronologic evidence, they often find the ‘solution’ inexplicable. Some archaeologists are so impressed by the new method, that they neglect the other evidence; others simply reject problematic 14C dates as archaeologically unacceptable. Frequently, excavation reports are provided with an appendix listing the relevant 14C dates with little or no discussion of their implication. It is rare, indeed, to see in archaeologic reports a careful weighing of the various types of chronologic evidence. Yet, this is precisely what the archaeologist is accustomed to do with the evidence from his traditional methods for building up a chronology: typology and stratigraphy. Why should he not be able to include radiocarbon dates in the same way in his considerations?

scholarly journals MeshNet: Mesh Neural Network for 3D Shape Representation

2019 ◽  
Vol 33 ◽  
pp. 8279-8286 ◽  
Author(s):  
Yutong Feng ◽  
Yifan Feng ◽  
Haoxuan You ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Point Cloud ◽  
State Of The Art ◽  
Shape Representation ◽  
Shape Classification ◽  
Retrieval Performance ◽  
3D Shape ◽  
General Architecture ◽  
3D Shapes

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

Joint Parameters Identification of a Structure With Large Number of Discrete Joints

2003 ◽  
Author(s):  
J. H. Wang ◽  
S. C. Chuang
Keyword(s):  
Frequency Response ◽  
Dynamic Behavior ◽  
Error Function ◽  
Parameters Identification ◽  
New Method ◽  
Response Functions ◽  
Traditional Methods ◽  
Measured Frequency Response ◽  
Joint Parameters ◽  
Better Than

The joint parameters of a structure with a large number of discrete joints generally are very difficult to identify accurately. The difficulty is due to the fact that the dynamic behavior of a structure becomes more complex with more number of joints. A new identification method which uses the measured frequency response functions (FRFs) to identify the joint parameters is proposed in this work to overcome this difficulty. The new method uses an error function to select different best data to identify different joints so that the accuracy of the identification can be improved. The accuracy of the new method and other two traditional methods is compared in this work. The results show that the accuracy of the proposed new method is far better than other two previous methods. The proposed new method has special advantage when (1) the number of joints is large, (2) the orders of magnitude of the joint parameters are different significantly.

A New Method for Eliminating False Lock in TT&C

2014 ◽  
Vol 701-702 ◽  
pp. 895-901
Author(s):  
Cheng Yi Li ◽  
Chun Hui Wang ◽  
Xiao Jun Jin ◽  
Zhong He Jin
Keyword(s):  
Experimental Results ◽  
New Method ◽  
Carrier Recovery ◽  
Traditional Methods ◽  
Signal Detector ◽  
Lock In

False lock is a fatal problem in TT&C due to the inevitable excessive delay.It can lead to false carrier acquisition.Based on the knowledge that false lock will not occur in the fast capture bandwidth,a new method for eliminating false lock is proposed.The carrier recovery loop closes only when the uplink carrier signal is detected in fast capture bandwidth by using signal detector and lock detector,then the false lock can be avoid.Compared with traditional methods,this solution is easier to realize and suitable for any type of loop. The experimental results reveal that the proposed method can eliminate false lock effectively.

Study on the Firing Table Compiling of a New Type of the Very Low Altitude Interception Device

2012 ◽  
Vol 424-425 ◽  
pp. 464-470
Author(s):  
Zhou Zhong ◽  
Yi Jiang ◽  
Yong Yuan Li ◽  
Chong Zhang
Keyword(s):  
Drag Coefficient ◽  
New Method ◽  
Correction Function ◽  
Traditional Methods ◽  
Coefficient Identification ◽  
New Type ◽  
Trajectory Correction ◽  
Low Altitude ◽  
Date Processing

The Firing Tables Compiling of a new type of the very low altitude interception device is studied in this paper. Based on the analysis of drag coefficient identification during the date processing of the Firing Tables experiment, and the theoretical trajectory correction, then according to the defects of the traditional methods for the Compilation of Firing Tables, a new method of determining the correction data is developed which replaces the simple data with the correction function. This method will reduce the error to an obvious degree.

scholarly journals ACTIVE NOISE CONTROL USING NEURAL SYSTEM

2010 ◽  
Vol 13 (3) ◽  
pp. 67-74
Author(s):  
Tuan Van Huynh ◽  
Nghĩa Hoai Duong
Keyword(s):  
Noise Control ◽  
Acoustic Noise ◽  
Active Noise Control ◽  
Neural System ◽  
Control Area ◽  
New Method ◽  
Traditional Methods ◽  
Active Noise ◽  
Opposite Phase ◽  
Simulation Results

The principle of active noise control (ANC) is to produce a secondary acoustic noise which has the same magnitude as the unwanted primary noise but with opposite phase. The sum of these two signals reduces acoustic noise in the noise control area. In this paper we present a new ANC method using neural system. Moreover a new method for compensating the saturation of the power applifier is also introduced. The performance of the proposed method is compared to that of traditional methods. Simulation results are provided for illustration.

scholarly journals Mining of High Average-Utility Pattern Using Multiple Minimum Thresholds in Big Data

2019 ◽  
Vol 8 (S2) ◽  
pp. 57-60
Author(s):  
R. Vasumathi ◽  
S. Murugan
Keyword(s):  
Big Data ◽  
Single Machine ◽  
New Method ◽  
Map Reduce ◽  
Itemset Mining ◽  
The Past ◽  
Pruning Method ◽  
Average Utility ◽  
Pruning Methods

In the past years most of the research have been conducted on high average-utility itemset mining (HAUIM) with wide applications. However, most of the methods are used for centralized databases with a single machine performing the mining job. Existing algorithms cannot be applied for big data. We try to solve this issue, by developing a new method for mining high average-utility itemset mining in big data. Map Reduce also used in this paper. Many algorithms were proposed only mine HAUIs using a single minimum high average-utility threshold. In this paper we also try solve this by mining HAUIs multiple minimum high average-utility thresholds. We have developed two pruning methods namely Reduction of utility co-occurrence pruning Method (RUCPM) and Pruning without Scanning Database (PWSD).

A simple protein evolutionary classification method based on the mutual relations between protein sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Xiaogeng Wan ◽  
Xinying Tan
Keyword(s):  
Mutual Information ◽  
Protein Sequences ◽  
New Method ◽  
Traditional Methods ◽  
Simple Method ◽  
Information Rates ◽  
Normalized Mutual Information ◽  
Natural Vector ◽  
Protein Map ◽  
Mutual Relations

Aims: This paper presents a simple method that is efficient for protein evolutionary classification. Background: Proteins are diverse with their sequences, structures and functions. It is important to understand the relations between the sequences, structures and functions of proteins. Many methods have been developed for protein evolutionaryclassifications, these methods include machine learning methods such as the LibSVM, feature methods such as the natural vector method and the protein map. Machine learning methods use pre-labeled training sets to classify protein sequences into disjoint classes. Feature methods such as the natural vector and the protein map convert protein sequences into feature vectors and use polygenetic-trees to classify on the distance between the feature vectors. In this paper, we propose a simple method that classify the evolutionary relations of protein sequences using the distance maps on the mutual relations between protein sequences. The new method is unsupervised and model-free, which is efficient in the evolutionary classifications of proteins. Objective: In this paper, we propose a simple method that classify the evolutionary relations of protein sequences using the distance maps on the mutual relations between protein sequences. The new method is unsupervised and model-free, which is efficient in the evolutionary classifications of proteins.methodTo quantify the mutual relations and the homology of protein sequences, we use the normalized mutual information rates on protein sequences, and we define two distance maps that convert the normalized mutual information rates into 'distances', and use UPGMA trees to present the evolutionary classifications of proteins. Method:: To quantify the mutual relations and the homology of protein sequences, we use the normalized mutual information rates on protein sequences, and we define two distance maps that convert the normalized mutual information rates into 'distances', and use UPGMA trees to present the evolutionary classifications of proteins. Result: We use four classifical protein evolutionary classification examples to demonstrate the new method, where the results are compared with traditional methods such as the natural vector and the protein maps. We use the AUPRC curves to evaluate the classification qualities of the new method and the traditional methods. We found that the new method with the two distance maps is efficient in the evolutionary classification of the classical examples, and it outperforms the natural vector and the protein maps in the evolutionary classifications. Conclusion: The normalized mutual information rates with the two distance maps are efficient in protein evolutionary classifications, which outperform some classifical methods in the evolutionary classifications. Other: The results are compared with traditional protein evolutionary classification methods such as the natural vector and the protein map, and the method of AUPRC curves is applied to the new method and the traditional methods to inspect the classification accuracies.

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