Rotation Scale Invariant Texture Classification for a Computational Engine
نویسندگان
چکیده
منابع مشابه
Rotation Scale Invariant Texture Classification for a Computational Engine
Texture analysis is a highly significant area in the arena of computer vision and connected pitches. Not the least, classification is also equally important and laudable zone in the area of understanding the texture pattern and is gaining a lot of interest among the researchers in the field of computer vision. It finds a widespread application in area of pattern classification, robotic applicat...
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Texture classification is very important in image analysis. Content based image retrieval, inspection of surfaces, object recognition by texture, document segmentation are few examples where texture classification plays a major role. Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and class...
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The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most exi...
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Texture classification is one of the problems in the field of texture analysis. In this paper an efficient method of texture classification using Gabor transform is proposed, which considers the effect of rotation and scale variances of texture images. Due to its optimal localization properties in both spatial and frequency domain, the Gabor transform has been recognized as a very useful tool i...
متن کاملGabor Filters for Rotation Invariant Texture Classification
A Gabor filter based feature extraction scheme for texture classification is proposed. By using a novel set of circularly symmetric filters, rotation invariance is achieved. The scheme offers a high classification performance on textures at any orientation using both fewer features and a smaller area of analysis than most existing schemes. The performance of the scheme on noisy images is also i...
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ژورنال
عنوان ژورنال: Research Journal of Applied Sciences, Engineering and Technology
سال: 2014
ISSN: 2040-7459,2040-7467
DOI: 10.19026/rjaset.8.1135