Multi Classification Method of Lane Arrow Markings Based on Support Vector Machines with Adaptive Partitioning Coding

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.

Download Full-text

Earthquake Damage Classification Method based on Support Vector Machines

Signal Processing, Pattern Recognition, and Applications / 722: Computer Graphics and Imaging ◽

10.2316/p.2011.721-023 ◽

2011 ◽

Cited By ~ 1

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

Download Full-text

Constructing Support Vector Machines Ensemble Classification Method for Imbalanced Datasets Based on Fuzzy Integral

Modern Advances in Applied Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-319-07455-9_8 ◽

2014 ◽

pp. 70-76 ◽

Cited By ~ 3

Author(s):

Pu Chen ◽

Dayong Zhang

Keyword(s):

Support Vector Machines ◽

Ensemble Classification ◽

Classification Method ◽

Support Vector ◽

Fuzzy Integral ◽

Imbalanced Datasets ◽

Vector Machines

Download Full-text

ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines

2010 International Conference on Electronics and Information Engineering ◽

10.1109/iceie.2010.5559841 ◽

2010 ◽

Cited By ~ 6

Author(s):

Dusit Thanapatay ◽

Chaiwat Suwansaroj ◽

Chusak Thanawattano

Keyword(s):

Support Vector Machines ◽

Classification Method ◽

Support Vector ◽

Principle Components Analysis ◽

Beat Classification ◽

Vector Machines ◽

Components Analysis

Download Full-text

A Classification Method of Traditional Decor Pattern Based on Support Vector Machines Approach and Gray Level Co-occurrence Matrix with Mean and F-Score

2019 International Seminar on Application for Technology of Information and Communication (iSemantic) ◽

10.1109/isemantic.2019.8884352 ◽

2019 ◽

Author(s):

Dhendra Marutho ◽

Muljono

Keyword(s):

Support Vector Machines ◽

Classification Method ◽

Support Vector ◽

Gray Level ◽

Vector Machines ◽

Occurrence Matrix

Download Full-text

MULTIPLE DECISION TEMPLATES WITH ADAPTIVE FEATURES FOR FINGERPRINT CLASSIFICATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407006009 ◽

2007 ◽

Vol 21 (08) ◽

pp. 1323-1338 ◽

Cited By ~ 4

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.

Download Full-text

A fast two-stage classification method of support vector machines

2008 International Conference on Information and Automation ◽

10.1109/icinfa.2008.4608121 ◽

2008 ◽

Author(s):

Jin Chen ◽

Cheng Wang ◽

Runsheng Wang

Keyword(s):

Support Vector Machines ◽

Classification Method ◽

Support Vector ◽

Two Stage ◽

Vector Machines ◽

Stage Classification

Download Full-text

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.293-294.373 ◽

2005 ◽

Vol 293-294 ◽

pp. 373-382 ◽

Cited By ~ 4

Author(s):

Qiao Hu ◽

Zheng Jia He ◽

Yanyang Zi ◽

Zhou Suo Zhang ◽

Yaguo Lei

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Support Vector Machines ◽

Empirical Mode Decomposition ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Turbo Generator ◽

Mode Decomposition ◽

Vector Machines ◽

Multi Classification

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

Download Full-text