Proceedings ArticleDOI
Rapid Texture Identification
Kenneth I. Laws
- Vol. 0238, pp 376-381
TLDR
In this article, the texture energy approach requires only a few convolutions with small (typically 5x5) integer coefficient masks, followed by a moving-window absolute average operation.Abstract:
A method is presented for classifying each pixel of a textured image, and thus for segmenting the scene. The "texture energy" approach requires only a few convolutions with small (typically 5x5) integer coefficient masks, followed by a moving-window absolute average operation. Normalization by the local mean and standard deviation eliminates the need for histogram equalization. Rotation-invariance can also be achieved by using averages of the texture energy features. The convolution masks are separable, and can be implemented with 1-dimensional (vertical and horizontal) or multipass 3x3 convolutions. Special techniques permit rapid processing on general-purpose digital computers.read more
Citations
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Image Segmentation Using Wavelet Packet Frames and Neuro-fuzzy Tools
Mausumi Acharyya,Malay K. Kundu +1 more
TL;DR: The present article describes a image segmenta- tion technique using M -band wavelet packet frames features that are evaluated and selected using an efficient neuro-fuzzy feature evaluation techniqu, which is demonstrated on IRS-1A and SPOT images.
Journal ArticleDOI
Comparison of wavelet-SVM and wavelet-adaptive network based fuzzy inference system for texture classification
Ibrahim Turkoglu,Engin Avci +1 more
TL;DR: In this paper, a comparison of wavelet-support vector machine (W-SVM) and wave let-adaptive network based fuzzy inference system ( W-ANFIS) approaches for texture image classification is presented.
Journal ArticleDOI
Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection
V. Sharma,K.C. Juglan +1 more
TL;DR: An attempt has been made to present a CAD system for the classification of liver ultrasound images that is able to give 95.55% accuracy and sensitivity with the 20 best features selected by the MI feature selection technique.
Journal ArticleDOI
Classification of honeybee pollen using a multiscale texture filtering scheme
TL;DR: This work presents an automatic methodology to discriminate pollen loads based on texture image classification using a multiscale filtering scheme and a statistical evaluation of the algorithm is provided and discussed.
Coupled modelling of land surface microwave interactions using ENVISAT ASAR data
Alexander Loew,Wolfram Mauser +1 more
TL;DR: In this article, a microwave backscattering model is used to estimate the surface roughness of the ground surface of a maize field from multitemporal SAR images, which can be used to quantify the influence of the canopy over the vegetation.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
ReportDOI
Textured Image Segmentation
TL;DR: In this article, texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values, leading to a simple class of texture energy transforms, which perform better than any of the preceding methods.