Combining wavelet transforms and neural networks for image classification

Lotfi, Mehdi; Solimani, Ali; Dargazany, Aras; Afzal, Hooman; Bandarabadi, Mojtaba

doi:10.1109/ssst.2009.4806819

Cited by 23 publications

(13 citation statements)

References 10 publications

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“…The prospect move in the direction of its base on the shout of the low decree payment picture and the resultant high occurrence sub-band images obtain by Discrete Wavelet Transform (DWT). New results on both 2D and 3D images show how our technique enhances the image's particulars and conserve edges [2,8].…”

Section: Introductionmentioning

confidence: 85%

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Adaptive analysis & reconstruction of 3D DICOM images using enhancement based SBIR algorithm over MRI

Gr¹,

He²,

Edara³

et al. 2018

biomedicalresearch

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The images can be visualized in three dimensional (3D) by means of different technique, these 3D techniques are used to improve the view of images. Converting two dimensional (2D) images into 3D images is a significant part of image application. A resourceful approach of 3D image reconstruction is implementing to execute a certain procedure that applied to X-ray medical image. The paper deals with the DICOM images reconstruction which is originally very helpful for manufacture of modified anatomical implant by using rapid prototyping technology. The researchers are gradually focusing on biomedical 3Dimaging improvement. The MAT LAB image processing has been powerfully developed and almost implements in every modern topographical modalities. The CT slices of involved region of modified implant in DICOM arrangement are initially pre-processed using developed MATLAB code. These files can be additional used for Rapid Prototyping. This project in biomechanical area has been urbanized to give the ability in very cheap cost with highest technological support moderately with expensive import facilities and software. This approach is implemented via four steps preprocessing, image enhancement, image contour then image reconstruction and visualization. Suggest a proposed researcher is based on novel analytical model which can put to gather in sequence from any source (i.e.) Slicer Based Image Retrieval (SBIR) mechanism to get back applicable medical imagery bottom on their features.

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Section: Introductionmentioning

confidence: 85%

“…Then, the routine slicer X&Y view is in progress and afterwards review by the client [4,8,11]. These consequences addicted to the modification stage where the mat lab tools under Slicer are used to right the automatic segmentation consequence.…”

Section: Flow Chart For Slicer View In 3dmentioning

confidence: 99%

Adaptive analysis & reconstruction of 3D DICOM images using enhancement based SBIR algorithm over MRI

Gr¹,

He²,

Edara³

et al. 2018

biomedicalresearch

View full text Add to dashboard Cite

show abstract

“…The age groups were determined using, various algorithms like Radial Basis Function (RBF), presented by (Yazdi et al, 2012), Back Propagation Network presented by (Mehdi et al, 2009) and Support Vector Machine (SVM)-Sequential Minimum Optimization (SMO), which includes mathematical techniques related to facial features. A combination of Discrete Wavelet Transform and Gradient Orientation pyramid for extracting the facial features was proposed by (Saeid and Leila, 2012).…”

Section: Previous Workmentioning

confidence: 99%

A Gaussian Mixture Model for Classifying the Human Age Using DWT and Sammon Map

Nithyashri¹,

Kulanthaivel²

2014

Journal of Computer Science

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The appearance of a human face rigorously changes with respect to age that makes Age Classification as a more challenging task. The algorithms such as, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Radial Basis Function (RBF), motivated many Face Researchers to focus their attention in classifying the human age into various age groups. The Classification rate produced by these existing algorithms is not significant indeed. In this study, Gaussian Mixture Models (GMM) is used for classifying the facial images into different age groups. A combination of Discrete Wavelet Transformation (DWT) and Sammon Map are used to extract the facial features. The performance of this approach is tested using Album-2 of MORPH database. A maximum classification rate of 99.52% is achieved in stage-1, whereas 99.46% is achieved in stage-2 using GMM. Also the accuracy achieved using Gaussian Mixture Model, is comparatively greater than K-NN.

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“…Wavelet transformed values are separately classified using multilayer Neural Network (NN) [5] with back-propagation training algorithm to identify rock type. Configuration of each NN used is three layer feed-forward type connections and consists of input, hidden and output layers.…”

Section: Neural Network Architecturementioning

confidence: 99%