OBJECT RECOGNITION BY A ROBUST MATCHING TECHNIQUE

1993 ◽  
pp. 481-495
Author(s):  
R. SALZBRUNN ◽  
H. NIEMANN ◽  
M. HARBECK ◽  
A. WINZEN
Keyword(s):  
Object Recognition ◽  
Matching Technique

THE ROLE OF RELATIONAL CONSTRAINTS IN REGION MATCHING

2004 ◽  
Vol 18 (03) ◽  
pp. 443-454 ◽  
Author(s):  
ALIREZA AHMADYFARD ◽  
JOSEF KITTLER
Keyword(s):  
Object Recognition ◽  
Graph Matching ◽  
Original Method ◽  
Misclassification Rate ◽  
Shape Descriptors ◽  
Recognition Method ◽  
Region Matching ◽  
Matching Technique ◽  
Elliptic Region

We propose a graph-based representation for the elliptic region shape descriptors introduced by Tuytelaars et al.13 In this representation we use image profiles to describe the relation between a pair of image regions. This new representation and a graph matching technique proposed in Ref. 1 are the basis of an object recognition method. An experimental comparative study between the original method and the new graph-based method is carried out. The results show that the graph-based method is more robust to scaling than the original method. Moreover, the misclassification rate using the graph-based method is considerably lower than that yielded by the original method.

Quadrics-based matching technique for 3D object recognition

1992 ◽  
Vol 10 (9) ◽  
pp. 578-588 ◽  
Author(s):  
M Hanmandlu ◽  
C Rangaiah ◽  
KK Biswas
Keyword(s):  
Object Recognition ◽  
3D Object Recognition ◽  
3D Object ◽  
Matching Technique

scholarly journals Manufacture Automation: Model and Object Recognition by Using Object Position Auto Locating Algorithm and Object Comparison Model

2000 ◽  
Vol 5 (2) ◽  
pp. 61-65
Author(s):  
Ching-Liang Su
Keyword(s):  
Object Recognition ◽  
Test Object ◽  
Object Position ◽  
Second Moment ◽  
Comparison Model ◽  
Center Point ◽  
Geometry Matching ◽  
Matching Technique ◽  
Test Face

This research uses the geometry matching technique to identify the different objects. The object is extracted from the background. The second moment 6 is used to find the orientation and the center point of the extracted object. Since the second moment can find the orientations and the center point of the object, the perfect object and the test object can be aligned to the same orientation. Furthermore, these two images can be shifted to the same centroid. After this, the perfect object can be subtracted from the test face. By using the subtracted result, the objects can be classified. The techniques used in this research can very accurately classify different objects.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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