Pattern recognition with SVM and dual-tree complex wavelets

Chen, G. Y.; Xie, Wenfang

doi:10.1016/j.imavis.2006.07.009

Cited by 92 publications

(31 citation statements)

References 25 publications

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“…Kuo et al [19] combined the AIS algorithm with a fuzzy neural network (FNN) to increase the accuracy of an RFID-based positioning system. This work develops an efficient SVM [20][21][22][23][24][25] method that is based on the AIS algorithm (AISSVM) for diagnosing ultrasound images of breast tumors. The proposed CAD system simultaneously performs parameter tuning and feature selection, and thus achieves high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

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A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96. 67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

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Section: Introductionmentioning

confidence: 99%

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

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“…In this section, the dominant points are connected to each other by means of Delaunay triangulation in order to produce a unique [15,18,24,27], where the processing is achieved on which represents the Binary ROI image geometrical signature corresponding to the analyzed palmprint. In addition to the uniqueness of the Delaunay triangulation, another property of this triangulation is that it maximizes the smallest angle of all triangles.…”

Section: Feature Modeling Using Delaunay Triangulationmentioning

confidence: 99%

“…For example, Zhang et al [10] use a Gabor filter to extract what has been called "Palm code" [10]. Chen and Xie [15] use a dual tree complex wavelet to extract the textural energy of the palmprint. Lie and You have proposed a texture-based retrieval scheme palmprint using a layered search [22].…”

Section: Introductionmentioning

confidence: 99%

A Robust Technique to Characterize the Palmprint using Radon transform and Delaunay triangulation

Bouchemha¹,

Naït‐Ali²,

Doghmane³

2010

IJCA

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For the purpose of biometric applications, we explore in this paper a new robust approach to characterizing palmprint features. Instead of processing the acquired image in the spatial domain, the proposed technique extracts palmprint features using Radon transform and a geometric Delaunay triangulation jointly. In such a process, Radon transform enables the extraction of directional characteristics from the palm of the hand. Afterwards, the most significant information is structured using Delaunay triangulation, thus providing a specific palmprint signature. In order to compare the uniqueness as well as the stability of the palmprint signature, Hausdorff distance has been used as a criterion of similarity. As will be shown in this paper, the palmprint signature is very robust even when considering a low Signal-to-Noise Ratio (SNR). Promising results are obtained from a local database containing 200 palmprint images. This technique is mainly appropriate for authentication applications.

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“…It extracts a set of information associated in the pattern or from its sub regions for their representation [2][3][4][5]. The set of features (features vector) extracted represents the input pattern in minimum dimension and it reduces the complexity of classification and recognition system.…”

Section: Introductionmentioning

confidence: 99%

Kannada Handwritten Numeral Recognition System using Fuzzy Reasoning Technique

Ashoka¹,

Manjaiah²,

Bera³

2013

IJCA

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This paper presents Kannada handwritten numeral recognition system using fuzzy reasoning technique. The Kannada handwritten numeral database required for the experimentation is collected from the different individuals and preprocessed to obtain the binary images for feature extraction. The binary images are partitioned into a number of regions and extracted one feature from each by the zoning technique for their representation. The knowledge base is build using the statistical information involved in interclass region features. The features of unknown samples matched with the knowledge base and made the fuzzy reasoning for their classification. On experimentation with the training and testing samples, found better classification and recognition rate.

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Pattern recognition with SVM and dual-tree complex wavelets

Cited by 92 publications

References 25 publications

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

A Robust Technique to Characterize the Palmprint using Radon transform and Delaunay triangulation

Kannada Handwritten Numeral Recognition System using Fuzzy Reasoning Technique

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