A Novel Approach to Pattern Recognition Based on PCA-ANN in Spectroscopy

2006 ◽  
pp. 525-532 ◽  
Author(s):  
Xiaoli Li ◽  
Yong He
Keyword(s):  
Pattern Recognition ◽  
Novel Approach

Graph Embedding Using Dissimilarities with Applications in Classification

2013 ◽  
pp. 363-380
Author(s):  
Horst Bunke ◽  
Kaspar Riesen
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Graph Matching ◽  
Graph Embedding ◽  
Mathematical Structure ◽  
Object Representations ◽  
Mathematical Concepts ◽  
Novel Approach ◽  
Representational Power ◽  
Mathematical Operations

The domain of graphs contains only little mathematical structure. That is, most of the basic mathematical operations, actually required by many standard computer vision and pattern recognition algorithms, are not available for graphs. One of the few mathematical concepts that has been successfully transferred from the vector space to the graph domain is distance computation between graphs, commonly referred to as graph matching. Yet, distance-based pattern recognition is basically limited to nearest-neighbor classification. The present chapter reviews a novel approach for graph embedding in vector spaces built upon the concept of graph matching. The key-idea of the proposed embedding method is to use the distances of an input graph to a number of training graphs, termed prototypes, as vectorial description of the graph. That is, all graph matching procedures proposed in the literature during the last decades can be employed in this embedding framework. The rationale for such a graph embedding is to bridge the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. Hence, the proposed framework can be considered a contribution towards unifying the domains of structural and statistical pattern recognition.

To what degree of voice perturbation are jitter measurements valid? A novel approach with synthesized vowels and visuo-perceptual pattern recognition

2012 ◽  
Vol 7 (1) ◽  
pp. 37-42 ◽  
Author(s):  
P.H. Dejonckere ◽  
A. Giordano ◽  
J. Schoentgen ◽  
S. Fraj ◽  
L. Bocchi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Novel Approach

A NOVEL APPROACH TO IMAGE THRESHOLDING BASED ON 2D HOMOGENEITY HISTOGRAM AND MAXIMUM FUZZY ENTROPY

2011 ◽  
Vol 07 (01) ◽  
pp. 105-133 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Entropy ◽  
Image Thresholding ◽  
Novel Approach ◽  
Entropy Principle

Image thresholding is an important topic for image processing, pattern recognition and computer vision. Fuzzy set theory has been successfully applied to many areas, and it is generally believed that image processing bears some fuzziness in nature. In this paper, we employ the newly proposed 2D homogeneity histogram (homogram) and the maximum fuzzy entropy principle to perform thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it not only can process "clean" images, but also can process images with different kinds of noises and images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding. It will be useful for applications in computer vision and image processing.

WAVELET ORTHONORMAL DECOMPOSITIONS FOR EXTRACTING FEATURES IN PATTERN RECOGNITION

1999 ◽  
Vol 13 (06) ◽  
pp. 803-831 ◽  
Author(s):  
YUAN Y. TANG ◽  
JIMING LIU ◽  
HONG MA ◽  
BING F. LI
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Two Dimensional ◽  
Classification Rate ◽  
One Dimensional ◽  
Feature Vectors ◽  
Novel Approach

In this paper, a novel approach based on the wavelet orthonormal decomposition is presented to extract features in pattern recognition. The proposed approach first reduces the dimensionality of a two-dimensional pattern, and thereafter performs wavelet transform on the derived one-dimensional pattern to generate a set of wavelet transform subpatterns, namely, several uncorrelated functions. Based on these functions, new features are readily computed to represent the original two-dimensional pattern. As an application, experiments were conducted using a set of printed characters with varying orientations and fonts. The results obtained from these experiments have consistently shown that the proposed feature vectors can yield an excellent classification rate in pattern recognition.

The PIC-TDD Framework of Test Data Design for Pattern Recognition Systems

2014 ◽  
Vol 6 (4) ◽  
pp. 43-62
Author(s):  
Xiangdong Wang ◽  
Ying Yang ◽  
Hong Liu ◽  
Yueliang Qian
Keyword(s):  
Pattern Recognition ◽  
Test Data ◽  
Random Sampling ◽  
Random Variable ◽  
Unbiased Estimation ◽  
Novel Approach ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
News Segmentation ◽  
Data Design

In this paper, a new approach is proposed for the design of test data for pattern recognition systems. In the theoretical framework put forward, performance on the population of data is viewed as expectation of a random variable, and the purpose of test is to estimate the parameter. While the most popular method of test data design is random sampling, a novel approach based on performance influencing classes is proposed, which can achieve unbiased estimation and the variance of estimation is much lower than that from random sample. The method is applied to the evaluation of systems for broadcasting news segmentation, and experimental results show the advantages over the random sampling approach.

Abstract 15541: Novel Molecular Entity for Effective Cardiac Regeneration: 5’ppp Microrna Induces Reprogramming and Accelerates Cardiomyocyte Maturation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Jiabiao Hu ◽  
Conrad Hodgkinson ◽  
Syeda S Baksh ◽  
Richard E Pratt ◽  
Victor J Dzau
Keyword(s):  
Pattern Recognition ◽  
Cardiac Regeneration ◽  
Molecular Entity ◽  
Rna Molecules ◽  
Novel Approach ◽  
Nfkb Activation ◽  
Recognition Receptor ◽  
Modest Effect

We have shown that miR combo directly reprograms fibroblasts into cardiomyocytes. However, both in vitro and in vivo, the generated cardiomyocytes are immature. Recently, we discovered that cardiomyocyte maturation is enhanced by NFkB activation. While miR combo initiates reprogramming, the effect on NFkB activity is modest. Therefore, miR combo only has a modest effect on maturation. We have now made an important discovery that 5’triphosphorylation (5’ppp) of RNA molecules markedly enhance NFkB activation; leading to increased and accelerated maturation of reprogramed cardiomyocytes. The addition of 5’ppp-RNA increased the number of mature cardiomyocytes by 4-fold (N=5, P<0.05). Moreover, 5’ppp-RNA also accelerated the appearance of mature cardiomyocyte mRNAs (1 day after transfection vs 4 days for unmodified RNA). Importantly, these effects were lost when the 5’ppp moiety was removed. Knockdown studies suggest that 5’ppp-RNA binds to the Pattern Recognition Receptor Rig1. MNase-seq and ChIP-seq analysis suggests that 5’ppp-RNA activates YY1 (N=4, P<0.001). We next asked if 5’ppp modification of the miRNAs within miR combo would also enhance cardiomyocyte maturation; thus, providing a single molecular entity that can initiate reprogramming and accelerate maturation of cardiomyocytes. Indeed, 5’ppp modification of the miR combo miR-1 increased the expression of the mature cardiomyocyte markers Actn2 (4-fold, N=3, P<0.05) and Myh6 (4-fold, N=3, P<0.05). In conclusion, 5’ppp-miR combo is a novel approach that has the advantage of both initiating reprogramming and accelerating cardiomyocyte maturation and should lead to more effective cardiac regeneration and restore cardiac function.

Pattern-Based Identification in P2P Systems

2009 ◽  
pp. 676-682
Author(s):  
Gábor Richly ◽  
Gábor Hosszú ◽  
Ferenc Kovács
Keyword(s):  
Pattern Recognition ◽  
Measurement Data ◽  
P2p Systems ◽  
Recognition Algorithms ◽  
Novel Approach ◽  
Experimental Media ◽  
Shared File ◽  
Audio Data ◽  
Searching Method ◽  
Media Stream

This article presents a novel approach to search in shared audio file storages such as P2P based systems. The proposed method is based on the recognition of specific patterns in the audio contents in such a way extending the searching possibility from the description based model to the content based model. The importance of the real-time pattern recognition algorithms that are used on audio data for content-based searching in streaming media is rapidly growing (Liu, Wang, & Chen, 1998). The main problem of such algorithms is the optimal selection of the reference patterns (soundprints) used in the recognition procedure. The proposed method is based on distance maximization and is able to quickly choose the pattern that later will be used as reference by the pattern recognition algorithms (Richly, Kozma, Kovács, & Hosszú, 2001). The presented method called EMESE (experimental media-stream recognizer) is an important part of a lightweight content-searching method, which is suitable for the investigation of the networkwide shared file storages. The experimental measurement data shown in the article demonstrate the efficiency of the proposed procedure.

Graph Embedding Using Dissimilarities with Applications in Classification

2012 ◽  
pp. 156-173
Author(s):  
Horst Bunke ◽  
Kaspar Riesen
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Graph Matching ◽  
Graph Embedding ◽  
Mathematical Structure ◽  
Object Representations ◽  
Mathematical Concepts ◽  
Novel Approach ◽  
Representational Power ◽  
Mathematical Operations

The domain of graphs contains only little mathematical structure. That is, most of the basic mathematical operations, actually required by many standard computer vision and pattern recognition algorithms, are not available for graphs. One of the few mathematical concepts that has been successfully transferred from the vector space to the graph domain is distance computation between graphs, commonly referred to as graph matching. Yet, distance-based pattern recognition is basically limited to nearest-neighbor classification. The present chapter reviews a novel approach for graph embedding in vector spaces built upon the concept of graph matching. The key-idea of the proposed embedding method is to use the distances of an input graph to a number of training graphs, termed prototypes, as vectorial description of the graph. That is, all graph matching procedures proposed in the literature during the last decades can be employed in this embedding framework. The rationale for such a graph embedding is to bridge the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. Hence, the proposed framework can be considered a contribution towards unifying the domains of structural and statistical pattern recognition.

scholarly journals Shape Matching and Recognition using Hybrid Features from Skeleton and Boundary

2018 ◽  
Vol 7 (2) ◽  
pp. 558-564
Author(s):  
Rama Mohan Babu Gatram ◽  
Dr. B. Raveendra Babu ◽  
Dr. A. Srikrishna ◽  
N. Venkateswara Rao
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Shape Matching ◽  
Maximum Length ◽  
Hybrid Features ◽  
End Points ◽  
Matching Process ◽  
Novel Approach ◽  
Junction Points ◽  
Single Pixel

This paper presents a novel approach for effective matching of similar shapes from skeleton and boundary features. The features identified from the shape are the junction points, end points, and maximum length from single pixel pruned skeleton of the shape. Another two features identified from the boundary are junctions and boundary length of the shape. These five features are then used for shape matching. We tested these features on Kimia shapes dataset and tools dataset. The matching process from these features has produced good results, showing the probable of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate these features are rotational and transform invariant.

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