Syntactic Algorithms for Image Segmentation and a Special Computer Architecture for Image Processing

Keng, Janmin; Fu, K. S.

doi:10.21236/ada052614

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…A large number of statistical texture features have been proposed, ranging from first order statistics to higher order statistics. Structural approach [11] defines a grammar for the way that the pattern of the texture produces structure. The texture is parsed to see if it matches the grammar.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Comparison of Texture based Approach for Identification of Regions in Satellite Image

Sharma¹,

Verma²

2013

IJCA

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Human vision is the most important resource of information used for object recognition and classification. Images having constant intensities can be easily represented by vision. Textures are one of the important features in computer vision as it identifies different regions of an image on the basis of texture properties. It is widely used in variety of applications. Identifying various regions in satellite image is one such application. There are numerous approaches based on texture classification that are mainly categorized as statistical, structural, model based and signal processing methods. The study involves the classification of LANDSAT ETM+ and MODIS satellite imagery datasets using texture based approaches i.e. Grey Level Co-occurrence Matrices (GLCM), Laws Energy Measure, Discrete Fourier Transform (DFT) and Gabor Filter. Relative performance comparison study of these approaches on the basis of standard deviation (statistical tool) has been carried out. GLCM shows best results among all other approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Comparison of Texture based Approach for Identification of Regions in Satellite Image

Sharma¹,

Verma²

2013

IJCA

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“…The classification can be done using multispectral discriminant analysis [Gupta74] Another form of editing uses initial region knowledge to guide a more sophisticated segmenter. It may be possible, by examining the initial edge and interior points, to infer a classifying rule or grammar [Keng77a,Keng77b]. This bootstrap information can then be used to resegment the scene or to segment other similar scenes.…”

Section: A 11 Segmentation Editingmentioning

confidence: 99%

Image Understanding Research and Its Application to Cartography and Computer-Based Analysis of Aerial Imagery

Fischler¹

1983

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A survey on image segmentation

Mui²

1981

Pattern Recognition

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340

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Syntactic Algorithms for Image Segmentation and a Special Computer Architecture for Image Processing

Cited by 4 publications

References 29 publications

Performance Comparison of Texture based Approach for Identification of Regions in Satellite Image

Performance Comparison of Texture based Approach for Identification of Regions in Satellite Image

Image Understanding Research and Its Application to Cartography and Computer-Based Analysis of Aerial Imagery

A survey on image segmentation

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