Adaptive lifting for shape-based image retrieval

Oonincx, P.J.; Zeeuw, de Paul

doi:10.1016/s0031-3203(03)00167-5

Cited by 19 publications

(7 citation statements)

References 14 publications

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“…As we recall that both μ 00 and μ 20 + μ 02 are invariants with respect to rotation and reflection this demonstrates how to normalize the moments to achieve invariance under dilation. The first choice leads to the following new set of invariant generators [4] …”

Section: Eccentricity (X)mentioning

confidence: 99%

“…It may be more suitable (as a starting point) in case the density distribution corresponds to wavelet detail coefficients, see [4]. Finally, it is clear that the shape of the foreground in the binary image f should be invariant under a change in luminosity; in mathematical parlance: f −→ λf where λ > 0.…”

Section: Eccentricity (X)mentioning

confidence: 99%

“…This feature vectorĨ now satisfies the homogeneity condition [4,8] in that a rescaling of the luminosity now affects all components in a homogeneous fashion:…”

Section: Eccentricity (X)mentioning

confidence: 99%

See 2 more Smart Citations

Towards an Online Image-Based Tree Taxonomy

Zeeuw

Ranguelova

Pauwels

2007

Advances in Data Mining. Theoretical Aspects and Applications

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Abstract. This paper reports on a first implementation of a webservice that supports image-based queries within the domain of tree taxonomy. As such, it serves as an example relevant to many other possible applications within the field of biodiversity and photo-identification. Without any human intervention matching results are produced through a chain of computer vision and image processing techniques, including segmentation and automatic shape matching. A selection of shape features is described and the architecture of the webservice is explained. Classification techniques are presented and preliminary results shown with respect to the success rate. Necessary future enhancements are discussed. Benefits are highlighted that could result from redesigning image-based expert systems as web services, open to the public at large.

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Section: Eccentricity (X)mentioning

confidence: 99%

Section: Eccentricity (X)mentioning

confidence: 99%

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Towards an Online Image-Based Tree Taxonomy

Zeeuw

Ranguelova

Pauwels

2007

Advances in Data Mining. Theoretical Aspects and Applications

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“…The filters used in this scheme can be made grid-adaptive (i.e., non-stationary), nonlinear [1], or even data-adaptive [2,8,9]. Grid-adaptive, non-stationary filters [4,10,16] are interesting in the extension of wavelet transforms for data on non-equidistant grids.…”

Section: Introductionmentioning

confidence: 99%

Adaptive splitting for stabilizing 1-D wavelet decompositions on irregular grids

2006

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This paper proposes a scheme to improve the stability of wavelet decompositions on 1-D irregular grids. Wavelet transforms on irregular grids are constructed using the lifting scheme. The filters in this scheme take the structure of the grid into account. Nice as it is, however, we undoubtfully bump into numerical stability issues directly related to the irregularity of the grid. Existing stabilizing methods concentrate on the filters used in the lifting scheme itself. While this may be effective in reducing the instability, they are inadequate when a highly irregular grid is involved. The approach presented in this paper is different, as it concentrates on the subsampling or subdivision. Grid locations in the multiscale transform are inserted in such an order that the irregularity of the grid at coarse scales is kept under control. This way, the proposed algorithm prevents instability at coarse scales, rather than healing it. Simulations illustrate that the proposed multiscale decomposition scheme is much more stable than the currently available transforms, especially at coarse scales, where effects of instability have a wide range.

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“…This is the retrieval of images on the basis of features automatically derived from the images themselves. The features most widely used are texture [1][2][3], color [4][5][6] and shape [7][8][9]. A plethora of texture features extraction algorithms exists, such as wavelets [10][11][12], mathematical morphology [13] and stochastic models [14], to mention few.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System for Aerial Image Retrieval and Classification

Γαστεράτος

Zafeiridis

Andreadis

2004

Methods and Applications of Artificial Intelligence

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Abstract. Content based image retrieval is an active research area of pattern recognition. A new method of extracting global texture energy descriptors is proposed and it is combined with features describing the color aspect of texture, suitable for image retrieval. The same features are also used for image classification, by its semantic content. An exemplar fuzzy system for aerial image retrieval and classification is proposed. The fuzzy system calculates the degree that a class, such as sea, clouds, desert, forests and plantations, participates in the input image. Target applications include remote sensing, computer vision, forestry, fishery, agricultures, oceanography and weather forecasting.

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Adaptive lifting for shape-based image retrieval

Cited by 19 publications

References 14 publications

Towards an Online Image-Based Tree Taxonomy

Towards an Online Image-Based Tree Taxonomy

Adaptive splitting for stabilizing 1-D wavelet decompositions on irregular grids

An Intelligent System for Aerial Image Retrieval and Classification

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