Earthquake Damage Classification Method based on Support Vector Machines

2011 ◽  
Author(s):  
Petros-Fotios Alvanitopoulos ◽  
John M. Konstantinides ◽  
Ioannis Andreadis ◽  
Anaxagoras Elenas
Keyword(s):  
Support Vector Machines ◽  
Earthquake Damage ◽  
Classification Method ◽  
Support Vector ◽  
Damage Classification ◽  
Vector Machines

scholarly journals Contact Lens Classification by Using Segmented Lens Boundary Features

2018 ◽  
Vol 11 (3) ◽  
pp. 1129
Author(s):  
Nur Ariffin Mohd Zin ◽  
Hishammuddin Asmuni ◽  
Haza Nuzly Abdul Hamed ◽  
Razib M. Othman ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Contact Lens ◽  
State Of The Art ◽  
Classification Method ◽  
Support Vector ◽  
Local Descriptors ◽  
Iris Image ◽  
Vector Machines ◽  
False Reject Rate ◽  
Better Than

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.

MULTIPLE DECISION TEMPLATES WITH ADAPTIVE FEATURES FOR FINGERPRINT CLASSIFICATION

2007 ◽  
Vol 21 (08) ◽  
pp. 1323-1338 ◽  
Author(s):  
JUN-KI MIN ◽  
SUNG-BAE CHO
Keyword(s):  
Support Vector Machines ◽  
Feature Vector ◽  
Experimental Results ◽  
Classification Method ◽  
Support Vector ◽  
Fingerprint Classification ◽  
Vector Machines ◽  
Fingerprint Database

This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

Support vector machines committee classification method for computer-aided polyp detection in CT colonography1

2005 ◽  
Vol 12 (4) ◽  
pp. 479-486 ◽  
Author(s):  
Anna K. Jerebko ◽  
James D. Malley ◽  
Marek Franaszek ◽  
Ronald M. Summers
Keyword(s):  
Support Vector Machines ◽  
Classification Method ◽  
Support Vector ◽  
Polyp Detection ◽  
Vector Machines ◽  
Computer Aided

Fault classification method for inverter based on hybrid support vector machines and wavelet analysis

2011 ◽  
Vol 9 (4) ◽  
pp. 797-804 ◽  
Author(s):  
Zhi-kun Hu ◽  
Wei-hua Gui ◽  
Chun-hua Yang ◽  
Peng-cheng Deng ◽  
Steven X. Ding
Keyword(s):  
Support Vector Machines ◽  
Wavelet Analysis ◽  
Classification Method ◽  
Fault Classification ◽  
Support Vector ◽  
Vector Machines

scholarly journals Optimal filter bank representation in texture classification using support vector machines

2021 ◽  
Author(s):  
Samsher Singh Sidhu
Keyword(s):  
Support Vector Machines ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Texture Classification ◽  
Classification Method ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Translation Invariant ◽  
Vector Machines ◽  
Wavelet Frame Transform

Texture analysis has been a field of study for over three decades in many fields including electrical engineering. Today, texture analysis plays a crucial role in many tasks ranging from remote sensing to medical imaging. Researchers in this field have dealt with many different approaches, all trying to achieve the goal of high classification accuracy. The main difficulty of texture analysis was the lack of ability of the tools to characterize adequately different scales of the textures effectively. The development in multi-resolution analysis such as Gabor and Wavelet Transform help to overcome this difficulty. This thesis describes the texture classification algorithm that uses the combination of statistical features and co-occurrence features of the Discrete Wavelet Transformed images. The classification accuracy is increased by using translation-invariant features generated from the Discrete Wavelet Frame Transform. The results are further improved by focussing on the transformed images used for feature extraction by using filters which essentially extract those areas of the image that discriminate themselves from other image classes. In effect, by reducing the spatial characteristics of images that contribute to the features, the texture classification method still has the ability to preserve the classification accuracy. Support Vector Machines has proved excellent performance in the area of pattern recognition problems. We have applied SVMs with the texture classification method described above and, when compared to traditional classifiers, SVM has produced more accurate classification results on the Brodatz texture album.

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