A new technique for texture segmentation is presented. The method is based on the use of Laguerre Gauss (LG) functions, which allow an efficient representation of textures. In particular, the marginal densities of the LG expansion coefficients are approximated by the generalized Gaussian densities, which are completely described by two parameters. The classification and the segmentation steps are performed by using a modified k -means algorithm exploiting the Kullback–Leibler divergence as similarity metric. This clustering method is a more efficient system for texture comparison, thus resulting in a more accurate segmentation. The effectiveness of the proposed method is evaluated by using mosaic image sets created by using the Brodatz dataset, and real images.
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.
In this work a novel technique for detecting and segmenting textured areas in natural images is presented.
The method is based on the circular harmonic function, and, in particular, on the Laguerre Gauss functions.
The detection of the textured areas is performed by analyzing the mean, the mode, and the skewness of the
marginal densities of the Laguerre Gauss coefficients. By using these parameters a classification of the patch
and of the pixel, is performed. The feature vectors representing the textures are built using the parameters of
the Generalized Gaussian Densities that approximate the marginal densities of the Laguerre Gauss functions
computed at three different resolutions. The feature vectors are clustered by using the K-means algorithm in
which the symmetric Kullback-Leibler distance is adopted. The experimental results, obtained by using a set of
natural images, show the effectiveness of the proposed technique.
In this paper a novel scheme for extracting the global features from an image. Usually the features are extracted from the
whole image. In the proposed approach, only the image regions conveying information are considered. The two steps
procedure is based on the Fisher's information evaluation computed by linear combination of Zernike expansion
coefficients. Then, by using the region growing algorithm, only high information rate regions are considered. The
considered features are texture, edges, and color. The performances of the proposed scheme has been evaluated by using
the retrieval rate. Experimental results show an increase in the retrieval rate with respect to use the same features
computed on whole image.
A novel technique for searching for complex patterns in large multimedia databases is presented, based on rotation independent template matching. To handle objects of arbitrary shape while reducing the computational workload, the pattern to be localized is partitioned into small square blocks of sizes adapted to the local image content using quadtree decomposition. The use of Zernike polynomials for representing each block allows the design of a fast and effective maximum likelihood matching procedure to sequentially verify whether the target image contains each block of the quadtree. State of the art methods usually represent the whole pattern by using an orthogonal basis and extracting an invariant feature vector from the representation coefficients. In the proposed scheme, the use of the quadtree decomposition allows us to bind the number of terms of the truncated expansions, still guaranteeing a precise image representation.
In this work a novel technique for selecting key points is proposed. Key points are used in many image processing
applications and should be robust with respect to noise, rotation, blurring, and so on. The selection is based on the
amount of local Fisher's information about location, orientation and scale. Based on the relationship between Taylor
polynomials in Cartesian coordinates and Zernike polynomials in polar coordinates, the Fisher's information matrix can
be written in terms of the image Zernike's expansion coefficients, which can be easily computed by means of a bank of
filters. To evaluate the performances of the proposed method we consider four different distortions at three levels.
Experimental results show that the performances, in terms of repeatability rate, are better that the performances obtained
by the conventional Harris detector.
In this work a novel technique for color texture representations and classifications is presented. We assume that a color
texture can be mainly characterized by two components: structure and color. Concerning the structure, it is analyzed by
using the Laguerre-Gauss circular harmonic wavelet decomposition of the luminance channel. At this aim, the marginal
density of the wavelet coefficients is modeled by Generalized Gaussian Density (GGD), and the similarity is based on
the Kullback-Leibler divergence (KLD) between two GGDs. The color is characterized by the moments computed on the
chromatic channels, and the similarity is evaluated by using the Euclidean distance. The overall similarity is obtained by
linearly combining the two individual measures. Experimental results on a data set of 640 color texture images, extracted
from the "Vision Texture" database, show that the retrieval rates is about 81% when only the structural component is
employed, and it rises up to 87% when using both structural and color components.
In this paper a novel technique for rotation independent template matching via Quadtree Zernike decomposition
is presented. Both the template and the target image are decomposed by using a complex polynomial basis.
The template is analyzed in block-based manner by using a quad tree decomposition. This allows the system to
better identify the object features.
Searching for a complex pattern into a large multimedia database is based on a sequential procedure that
verifies whether the candidate image contains each square of the ranked quadtree list and refining, step-by-step,
the location and orientation estimate.