INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION

2007 ◽  
Vol 21 (08) ◽  
pp. 1339-1351 ◽  
Author(s):  
ZHI-YONG LIU ◽  
HONG QIAO ◽  
LEI XU
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Line Segment ◽  
Reconstruction Error ◽  
Mean Square ◽  
Noise Resistance ◽  
Generalized Hough Transform ◽  
The Mean ◽  
Sample Reconstruction

By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.

scholarly journals FACIAL FEATURES DETECTION USING TEXTURE HOUGH TRANSFORM

2015 ◽  
Vol XL-5/W6 ◽  
pp. 107-111 ◽  
Author(s):  
V. S. Gorbatsevich
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Main Tool ◽  
Original Method ◽  
Facial Features ◽  
Training Set ◽  
Generalized Hough Transform ◽  
Lbp Descriptor

The paper presents an original method for object detection. The “texture” Hough transform is used as the main tool in the search. Unlike classical generalized Hough transform, this variation uses texture LBP descriptor as a primitive for voting. The voting weight of each primitive is assumed by learning at a training set. This paper gives an overview of an original method for weights learning, and a number of ways to get the maximum searching algorithm speed on practice.

scholarly journals Enhanced Object Detection using Generalized Hough Transform and RANSAC

2019 ◽  
Vol 8 (12) ◽  
pp. 2046-2052 ◽  
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Features Extraction ◽  
Robust Algorithms ◽  
Detection Process ◽  
Generalized Hough Transform

In this paper object detection process has been implemented using the combination of some robust algorithms. The algorithms used are SIFT, GHT and RANSAC. Two models have been proposed which using two of the above mentioned algorithms. In model one features extraction by SIFT has been processed using Generalized Hough Transform (GHT) , where in other model improved RANSAC has been used to eliminate the mismatches in order to achieve better recognition resolution. Overall GHT is found to be superior technique to achieve object detection efficiently.

Online Monitoring Technology of Power Transformer based on Vibration Analysis

2021 ◽  
Vol 30 (1) ◽  
pp. 554-563
Author(s):  
Junhong Meng ◽  
Maninder Singh ◽  
Manish Sharma ◽  
Daljeet Singh ◽  
Preet Kaur ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Online Monitoring ◽  
Power Transformer ◽  
Vibration Signal ◽  
Reconstruction Error ◽  
Mean Square ◽  
Reconstructed Signal ◽  
The Mean ◽  
Online Monitoring System ◽  
Better Than

Abstract This paper presents a method for the study of the influence of stability of a power transformer on the power system based on the vibration principle. Traditionally, the EMD and EEMD algorithms are employed to test the box vibration signal data of the power transformer under three working conditions. The proposed method utilizes a partial EMD screening along with MPEEMD method for the online monitoring of power transformer. A complete online monitoring system is designed by using the STM32 processor and LabVIEW system. The proposed system is compared with EMD and EEMD algorithms in terms of the number of IMFs obtained by decomposition, maximum correlation coefficient, and mean square error. The inherent mode correlation, when compared with the mean square error of the reconstructed signal, shows that the reconstruction error of MPEEMD algorithm is 4.762×10−15 which is better than the traditional EMD algorithm. It is observed from the results that the proposed method outperforms both EMD and EEMD algorithms.

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