Three-dimensional projective invariants of points from multiple images

2008 ◽  
Vol 47 (11) ◽  
pp. 117203 ◽  
Author(s):  
Xiao Chen
Keyword(s):  
Three Dimensional ◽  
Multiple Images ◽  
Projective Invariants

GEOMETRIC INVARIANTS CONSTRUCTION FROM MULTIPLE VIEWS

2011 ◽  
Vol 02 (02) ◽  
pp. 195-206
Author(s):  
CHENG JIN
Keyword(s):  
Three Dimensional ◽  
Ground Truth ◽  
Multiple Views ◽  
Multiple Images ◽  
Object Structure ◽  
Projective Invariants ◽  
Geometric Invariants ◽  
Image Pairs ◽  
Fundamental Matrices ◽  
Novel Algorithm

Geometric invariants have wide applications in computer vision and their precision has long been a hot topic. In most of the existing methods, three-dimensional (3D) invariants have been obtained by reconstruction of the object structure, where fundamental matrices between image pairs should be first established. Consequently, there are additional errors introduced during invariants construction and could be very time consuming. In this paper, a novel algorithm to calculate 3D projective invariants from multiple images has been proposed, without reconstructing the object structures explicitly. We have employed the geometric configuration of points and lines in general position to deduce the formulation of 3D invariants. It has been verified in our experiments that our proposed method is considerably accurate when compared with the ground truth, and more efficient when compared with reconstruction based methods.

Multi-perspective measurement of yarn hairiness using mirrored images

2016 ◽  
Vol 88 (6) ◽  
pp. 621-629 ◽  
Author(s):  
Lei Wang ◽  
Bugao Xu ◽  
Weidong Gao
Keyword(s):  
3D Model ◽  
Three Dimensional ◽  
Digital Camera ◽  
3D Measurement ◽  
Multiple Perspectives ◽  
Multiple Images ◽  
Image Processing Techniques ◽  
Yarn Hairiness ◽  
Processing Techniques ◽  
Planar Mirrors

Most photoelectric and imaging methods for yarn hairiness measurements often provide underestimated data of hairy fibers measured from light projection, which ignores the spatial orientations and shapes of protruding fibers. In this project, a three-dimensional (3D) system was developed to detect hairy fibers from multiple perspectives and to reconstruct a 3D model for the yarn that permits fibers to be traced spatially. The system utilized two angled planar mirrors to view a yarn from five different perspectives simultaneously, and a digital camera to capture the multiple images in one panoramic picture. The image-processing techniques were used to dissect the panoramic picture into five sub-images containing separate views of the yarn, and to segment the sub-images to obtain yarn silhouettes showing the edges of the yarn and hairy fibers. A 3D model of the yarn could be built by merging the five silhouettes with the angles defined by the scene geometry of the dual mirrors. From the 3D model, hairy fibers protruding from the yarn core could be traced in the space for accurate length measurements. The system represents a simple and practical solution for the 3D measurement of yarn hairiness.

ANALYSIS AND COMPUTATION OF PROJECTIVE INVARIANTS FROM MULTIPLE VIEWS IN THE GEOMETRIC ALGEBRA FRAMEWORKS

1999 ◽  
Vol 13 (08) ◽  
pp. 1105-1121 ◽  
Author(s):  
JOAN LASENBY ◽  
EDUARDO BAYRO-CORROCHANO
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Geometric Algebra ◽  
Fundamental Matrix ◽  
Real Data ◽  
Multiple Views ◽  
Multiple Images ◽  
Projective Invariants ◽  
Lighting Conditions ◽  
Complete Framework

A central task of computer vision is to automatically recognize objects in real-world scenes. The parameters defining image and object spaces can vary due to lighting conditions, camera calibration and viewing positions. It is therefore desirable to look for geometric properties of the object which remain invariant under such changes. In this paper we present geometric algebra as a complete framework for the theory and computation of projective invariants formed from points and lines in computer vision. We will look at the formation of 3D projective invariants from multiple images, show how they can be formed from image coordinates and estimated tensors (F, fundamental matrix and T, trilinear tensor) and give results on simulated and real data.

Three-dimensional shape rendering from multiple images

2005 ◽  
Vol 67 (4) ◽  
pp. 332-346 ◽  
Author(s):  
Alberto Bartesaghi ◽  
Guillermo Sapiro ◽  
Tom Malzbender ◽  
Dan Gelb
Keyword(s):  
Three Dimensional ◽  
Multiple Images ◽  
Dimensional Shape ◽  
Three Dimensional Shape
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