Textured areas detection and segmentation in circular harmonic functions domain

Costantini, Luca; Capodiferro, Licia; Carli, Marco; Neri, Alessandro

doi:10.1117/12.908123

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…We use the term non-uniform in a broad sense to refer to regions containing sufficient details, to allow texture descriptors to work effectively. This is different from the standard texture/non-texture classification [27,28] so we develop a simple approach for our purpose. In uniform regions, the luminance of pixels is evenly distributed, and the intensity feature f I can represent these regions well.…”

Section: Mrf-based Automatic Feature Selectionmentioning

confidence: 99%

Example-based image colorization via automatic feature selection and fusion

Lai

Rosin

2017

Neurocomputing

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Image colorization is an important and difficult problem in image processing with various applications including image stylization and heritage restoration. Most existing image colorization methods utilize feature matching between the reference color image and the target grayscale image. The effectiveness of features is often significantly affected by the characteristics of the local image region. Traditional methods usually combine multiple features to improve the matching performance. However, the same set of features is still applied to the whole images. In this paper, based on the observation that local regions have different characteristics and hence different features may work more effectively, we propose a novel image colorization method using automatic feature selection with the results fused via a Markov Random Field (MRF) model for improved consistency. More specifically, the proposed algorithm automatically classifies image regions as either uniform or non-uniform, and selects a suitable feature vector for each local patch of the target image to determine the colorization results. For this purpose, a descriptor based on luminance deviation is used to estimate the probability of each patch being uniform or non-uniform, and the same descriptor is also used for calculating the label cost of the MRF model to determine which feature vector should be selected for each patch. In addition, the similarity between the luminance of the neighborhood is used as the smoothness cost for the MRF model which

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Section: Mrf-based Automatic Feature Selectionmentioning

confidence: 99%

Example-based image colorization via automatic feature selection and fusion

Lai

Rosin

2017

Neurocomputing

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“…Zernike polynomials belong to the class of circular harmonic functions, such as the Laguerre Gauss functions; they are widely used as descriptors of 2D patches by the computation of the Zernike moments, 14,15 and in many applications of image processing. 16,17 Let f (x) be any continuous function, the Zernike moments are computed as follows…”

Section: Space-time Zernike Descriptorsmentioning

confidence: 99%

A comparison between space-time video descriptors

2013

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The description of space-time patches is a fundamental task in many applications such as video retrieval or classification. Each space-time patch can be described by using a set of orthogonal functions that represent a subspace, for example a sphere or a cylinder, within the patch. In this work, our aim is to investigate the differences between the spherical descriptors and the cylindrical descriptors. In order to compute the descriptors, the 3D spherical and cylindrical Zernike polynomials are employed. This is important because both the functions are based on the same family of polynomials, and only the symmetry is different. Our experimental results show that the cylindrical descriptor outperforms the spherical descriptor. However, the performances of the two descriptors are similar

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Textured areas detection and segmentation in circular harmonic functions domain

Cited by 2 publications

References 9 publications

Example-based image colorization via automatic feature selection and fusion

Example-based image colorization via automatic feature selection and fusion

A comparison between space-time video descriptors

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