Mosaic Models for Image Analysis and Synthesis.

Ahuja, Narendra

doi:10.21236/ada050100

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…The pattern of all surface or profile points in a particle image may be considered as the particle texture (Ahuja, 1973). This pattern may be defined as a series of textural parameters capable of representing the statistical properties of the colour-level distribution of the pixels that form each particle (T A) ( As may be observed for the particles shown in Figure. 5, these parameters appear to be insufficient to identify particle textures.…”

Section: Textural Analysismentioning

confidence: 99%

Particle Identification by Image Processing

Bonifazi

Massacci

1996

KONA

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Section: Textural Analysismentioning

confidence: 99%

Particle Identification by Image Processing

Bonifazi

Massacci

1996

KONA

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“…The four visual features describe image characters variously, with both advantage and disadvantage in retrieval. Based on the statistical analysis of the experiment systems, color feature (HSV space color histogram), texture feature (co-occurrence matrix [1] ), shape feature (moment invariant [2] based-on threshold optimization), spatial relationship feature (based-on the Markov chains [3] ) extracted by the system are summarized as follows. 1.…”

Section: Introductionmentioning

confidence: 99%

An Image Retrieval Method Using Combined Features Based on Feature Statistic

Liu

Jia

2007

Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007)

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How to use low-level image features such as color, texture, shape, spatial relationship etc. logically in retrieval is an important problem for both retrieval system and users. An image retrieval method based on the four kinds of image low-level features is proposed in this paper. According to image texture characteristic, a kind of image feature statistic is defined. By using feature weight assignment operators designed here, the method can assign weight to the above four features adaptively and realize image retrieval based on combined image features. The experiment results show that the method mentioned above is more efficient than those based on only one kind visual feature.

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17 Image models

Ahuja¹,

Rosenfeld²

1982

Handbook of Statistics

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Mosaic Models for Image Analysis and Synthesis.

Cited by 8 publications

References 11 publications

Particle Identification by Image Processing

Particle Identification by Image Processing

An Image Retrieval Method Using Combined Features Based on Feature Statistic

17 Image models

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