Comparison Between Gabor Filters and Wavelets Transform for Classification of Textured Images

The quality of olive fruit and its virgin olive oil is a main concern for consumers and fruit industrial companies. The effectiveness and fast detection of olive’s skin defects is the most decisive factor in determining its quality. It is necessary to design and implement image processing tools for segmentation and correct classification of the different fresh incoming olive batches. In this paper, we propose a new automatic image segmentation algorithm, based on discrete wavelets transform. The aim of the segmentation algorithm is to discriminate between olives and the background with the challenge of irregular and dispersive lesion borders, low contrast, artifacts in the olive fruit and variety of colors within the interest region. The second part of our work proposes a scheme for olive fruit classification. The classifier first identifies the olive fruit color and then, based upon discrete wavelets transform and Tamura statistical texture features, the healthy olive fruit is distinguished from the damaged one. The new texture feature vector is, then, compared with the robust Local Binary Pattern feature vector. The simplicity of our segmentation and classification algorithms makes them appropriate for designing a productive and profitable computer vision machine.

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Thermographic image analysis for classification of ACL rupture disease, bone cancer, and feline hyperthyroid, with Gabor filters

10.1117/12.2261753 ◽

2017 ◽

Author(s):

Mehrdad Alvandipour ◽

Scott E. Umbaugh ◽

Deependra K. Mishra ◽

Rohini Dahal ◽

Norsang Lama ◽

...

Keyword(s):

Image Analysis ◽

Bone Cancer ◽

Gabor Filters ◽

Acl Rupture ◽

Thermographic Image

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Features Extraction for Pollen Recognition Using Gabor Filters

Food Science and Applied Biotechnology ◽

10.30721/fsab2018.v1.i2.11 ◽

2018 ◽

Vol 1 (2) ◽

pp. 86

Author(s):

Dimitar Nikolov Nikolov ◽

Diana Dimitrova Tsankova

Keyword(s):

Filter Bank ◽

Recognition System ◽

Gabor Filters ◽

Features Extraction ◽

Botanical Origin ◽

Biologically Inspired ◽

Microscope Images ◽

European Pollen Database ◽

Components Analysis

The aim of the article is to investigate the features extraction from microscope images of pollens for a classification of honey on the base of its botanical origin. A filter-bank of Gabor filters (as a biologically inspired recognition system) is used to obtain features, which are then post-processed using normalization, down-sampling (by bicubic interpolation), and principal components analysis (PCA). PCA is used for reducing the features size and a proper visualization of the features extraction results. Microscope images from the European pollen database, including pollen images of linden, acacia, lavender, rapeseed, and thistle, are used to illustrate capabilities of the proposed features extraction approach. The performance of the proposed algorithm is evaluated by simulations in MATLAB environment.

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Classification of textures in satellite image with Gabor filters and a multi layer perceptron with back propagation algorithm obtaining high accuracy

International Journal of Energy and Environment ◽

10.5935/2076-2909.20150001 ◽

2015 ◽

Vol 6 (5) ◽

Cited By ~ 4

Author(s):

Adriano Beluco ◽

Paulo M. Engel ◽

Alexandre Beluco

Keyword(s):

Satellite Image ◽

Back Propagation ◽

Gabor Filters ◽

High Accuracy ◽

Multi Layer Perceptron ◽

Back Propagation Algorithm ◽

Propagation Algorithm

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Vibration Monitoring Using Wavelets Transform Feature Extraction Algorithm and Technique

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.666.256 ◽

2014 ◽

Vol 666 ◽

pp. 256-266

Author(s):

Okuwobi Idowu Paul ◽

Yong Hua Lu

Keyword(s):

Wavelet Packet ◽

Human Interaction ◽

Vibration Monitoring ◽

Systematic Method ◽

Vibration Signals ◽

Extraction Algorithm ◽

Wavelets Transform ◽

The Fourier Transform ◽

Gravel Road

Vibration is a mechanical phenomenon whereby oscillations occur about an equilibrium point. The oscillation may be periods such as the motion of a pendulum or random such as the movement of tire on a gravel road. Vibration causes waste of energy and creates unwanted sound-noise. Monitoring such process generally possess a big problem especially for a system. The present traditional single resolution techniques could not solve this problem, coupled with the Fourier transform which seems to be one of the best method in analyzing and monitoring vibration in machineries or machinery components.This paper present a new algorithm using wavelet- packet based feature in classification of vibration signals. This study explores the feasibility of the wavelet packet transform as a tool in search for features that may be used in the detection and classification of machinery vibration signals. By formulating a systematic method of determining wavelet packet based features that exploit class specific differences among interested signals, which avoid human interaction. This new algorithm provide more effective method to achieve robust classification than traditional single resolution techniques. The new algorithm in wavelet transform techniques proved to be more efficient, better analysis, and provides better results with minimum error than any existing method.

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