Wavelet transforms on vector spaces as a method of multispectral image characterisation

Watson, Graham H.; Watson, Sharon K.

doi:10.1049/cp:19950653

Cited by 2 publications

(2 citation statements)

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“…We consider the imagery in multi-dimensional vector space [7]. That is, at each pixel location we have a 3-dimensional vector for 3-bands of the videography imagery.…”

Section: Multiresolution Classification Schemementioning

confidence: 99%

“…In lowlow, low-high, high-low and high-high subspaces, we first compute the mean absolute distance, assuming we get M types after thresholding, that is: ml m= 1,2,.. .M (7) o, (m) where dLLp is the distance in low-low subband of band p , .Cup(i,j) is the coefficient at location (i,j) in low-low subband of band p , pu,,(m) and ou,,(m) are the mean and standard deviation in low-low subband of band p for type m. Then the distance in the low-low subspace is defined as: p=l Similarly, we can compute dLH, dHL and dHH. Since most of the useful information is in low-low subspace, we can not just add these four distance together to be the total distance.…”

Section: A Unsupervised Classificationmentioning

confidence: 99%

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Multi-resolution vegetation classification along lower Rio Grande

Zhang

Desai²,

Lonard³

et al.

1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)

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We present a quasi-wavelet based approach for multiresolution unsupervised and supervised vegetation classification of videography imagery. Due to certain limitations of the traditional wavelet decomposition, we investigate a new quasi-wavelet decomposition to generate an alternative feature space for vegetation classification. Most existing wavelet-based classification approaches only consider the information on low-low frequency subspace at each decomposed stage. However, both low and high frequency features are applied in our algorithms and are controlled by weight setting on the vector feature space. Thresholding method is used for generating original class center to achieve convergent result for unsupervised classification. The results on lower Rio Grande Valley videography imagery are presented.

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“…We consider the imagery in multi-dimensional vector space [7]. That is, at each pixel location we have a 3-dimensional vector for 3-bands of the videography imagery.…”

Section: Multiresolution Classification Schemementioning

confidence: 99%

Section: A Unsupervised Classificationmentioning

confidence: 99%

Multi-resolution vegetation classification along lower Rio Grande

Zhang

Desai²,

Lonard³

et al.

1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)

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show abstract

Thresholding wavelets for image compression

Albanesi

Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171)

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The paper addresses the problem of thresholding Wavelet coefficients in a transform-based algorithm for still image compression. Processing data before the quantization phase is a crucial step in a compression algorithm, especially in applications which require high compression ratios. In the paper, after a review on the applications of Wavelets to image compression, a new solution to the problem of an accurate choice of thresholds is presented.It is based on the concept of local contrast and exploits the localization properties of Wavelets and a maximization of the entropy to find the optimal threshold for the Wavelet coefficients. The results are compared with standard thresholding techniques which do not include considerations about local distribution of pixel information within the image. At the end, examples of compression are given, where the algorithm includes the complete processing of transform coefficients (thresholding, quantization and coding).

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Wavelet transforms on vector spaces as a method of multispectral image characterisation

Cited by 2 publications

References 0 publications

Multi-resolution vegetation classification along lower Rio Grande

Multi-resolution vegetation classification along lower Rio Grande

Thresholding wavelets for image compression

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