scholarly journals Object Detection Based on Minimum Convex Hull and Generalized Hough Transform

2020 ◽  
Vol 768 ◽  
pp. 072058
Author(s):  
Tong Zhang ◽  
Chenfei Song
Keyword(s):  
Convex Hull ◽  
Object Detection ◽  
Hough Transform ◽  
Generalized Hough Transform

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.

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