Comparative study of moment based parameterization for morphological texture description

Aptoula, Erchan

doi:10.1016/j.jvcir.2012.08.005

Cited by 15 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a methodology, it has been applied to almost all domains dealing with digital image processing. Consequently, it was only a matter of time before attempting to extend the same concepts to colour and more generally multispectral images [6][7][8][9][10].…”

Section: -Mathematical Morphology and Basic Morphological Operationsmentioning

confidence: 99%

Line Extraction and Player Tracking in Tennis Videos

Erol¹

2017

Preprint

View full text Add to dashboard Cite

Sports video analysis is an interesting and emerging field in the domain of computer vision. It has become a very popular subject among scholars all over the world in the last decade due to the need of automated classification of events such as offsides, in/out situations, goals and so on. The most important problem in sports video analysis is detection and tracking of ball and players. Extraction of court lines is also another important problem since it is necessary in the classification of aforementioned events. In this study, considering some of the previous work on the subject, we develop a method for tracking the ball and players in tennis videos where the camera position and orientation is supposed fixed.

show abstract

Section: -Mathematical Morphology and Basic Morphological Operationsmentioning

confidence: 99%

Line Extraction and Player Tracking in Tennis Videos

Erol¹

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…(7) and is given by Eq. (8). For the discrete curvelet transform instead of polar wedge windows shear windows are constructed from concentric squares for calculation convenience.…”

Section: Discrete Curvelet Transformmentioning

confidence: 99%

Content-based texture image retrieval by histogram of curvelets

Uslu¹,

Albayrak²

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Curvelet decomposition is a multiscale analysis method defined for 2D and 3D signals that can represent curve-like features with great sparsity. A genuine method based on histograms of curvelets is proposed for content-based texture image retrieval. The accuracy of the method is analyzed for rotation invariance, curvelet scale-orientation size, and bin size. The results are given with precision-recall graphs. Experimental results on the Brodatz database show promising results for the proposed method compared to curvelet subband statistical features.

show abstract

“…6). The function ξ commonly used is an erosion but the opening was also used [14]. The main writing form is the normalized one:…”

Section: Morphological Covariancementioning

confidence: 99%

Toward a full-band texture features for spectral images

Ledoux

Richard

Capelle-Laize

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Facing the increasing number of multi and hyperspectral image acquisitions, in particular for medical and industrial applications, we need accurate features to analyse and assess the content complexity in a metrological way. In this paper, we explore an original way to compute texture features for spectral images in a full-band and vector process. To do it, we developed a dedicated approach for Mathematical Morphology using distance function. Thanks to this, we extend the classical mathematical morphology to spectral images. We show in this paper the scientific construction and preliminary results.

show abstract

Comparative study of moment based parameterization for morphological texture description

Cited by 15 publications

References 31 publications

Line Extraction and Player Tracking in Tennis Videos

Line Extraction and Player Tracking in Tennis Videos

Content-based texture image retrieval by histogram of curvelets

Toward a full-band texture features for spectral images

Contact Info

Product

Resources

About