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 Retrieval and Classification of Carotid Plaque Ultrasound Images
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Deep Learning-Based Methodology for Recognition of Fetal Brain Standard Scan Planes in 2D Ultrasound Images
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The Influence of Mean Reflectance on Perceived Print Mottle
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Designing texture filters with genetic algorithms: an application to medical images
TL;DR: Comparisons with established texture recognition techniques are presented, which show that the proposed method performs as well as, or better than, traditional techniques for the chosen instances of standard and anatomical texture and has the advantage of not having to decide which texture measure to use for a specific image structure.
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Quantitative assessment of liver fibrosis: a novel automated image analysis method.
TL;DR: Semiquantitative staging of liver fibrosis is a highly subjective procedure and may lead to an uncertainty in judgment regarding the degree of severity and hence the progression of the disease.
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.