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In [30], the authors proposed a region-based active contour model based on local image fitting to extract the local image information, with respect to intensity inhomogeneity.
Although most quantization-based global representations are based on local image descriptors (such as SIFT [2] and SURF [12]), a few important spatial relevant information of these local features are generated by the local feature detectors [13], such as coordinates, scale, orientation, or saliency of local feature points apart from the local feature descriptors.
Orientation assignment: Each keypoint is assigned one or more orientations based on local image gradient directions.
The specially designed and trained network provides adaptive filter coefficients based on local image properties with the ability to efficiently remove the coding artifacts while preserving image details.
The proposed quantization technique is based on local image complexity estimation employing the median algorithm and the standard deviation [13].
In the work of Oliver et al. [16], the individual microcalcification detection is based on local image features of the microcalcifications from a bank of filters.
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Especially, the blocking strategy is useful for those image hashing schemes based on local statistical characteristics of an image, such as the NMF-based hashing algorithm proposed in [20].
Subsequent to the image up-scaling, a fuzzy local adaptive Laplacian post-processing scheme is used which enhances the HF image details more than the low frequency image details based on local statistics in the up-scaled image.
Each enhanced palmprint image has been divided into sub-images and features are obtained based on local properties within the sub-image.
As summarized by [4], most of the existing algorithms follow a two-stage approach: conduct depth estimation based on local groups of input images; fuse the estimated depth values into a global watertight 3D surface estimation.
In particular, this kind of distribution is present in content-based video retrieval systems, image classification based on local descriptors, rotation-invariant shape matching, and others.
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based on local distance
based on local fee-for-service
based on local history
based on local land
based on local expert
based on local feature
based on local currency
based on clinical image
based on local illumination
based on local well
based on local produce
based on digital image
based on local probe
based on local knowledge
based on local tax
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com