Beyond morphological size distribution

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001

AbstractÐThis paper proposes new descriptors for binary and gray-scale images based on newly defined spatial size distributions (SSD). The main idea consists of combining a granulometric analysis of the image with a comparison between the geometric covariograms for binary images or the auto-correlation function for gray-scale images of the original image and its granulometric transformation; the usual granulometric size distribution then arises as a particular case of this formulation. Examples are given to show that in those cases in which a finer description of the image is required, the more complex descriptors generated from the SSD could be advantageously used. It is also shown that the new descriptors are probability distributions so their intuitive interpretation and properties can be appropriately studied from the probabilistic point of view. The usefulness of these descriptors in shape analysis is illustrated by some synthetic examples and their use in texture analysis is studied by doing an experiment of texture classification on a standard texture database. A comparison is perfomed among various cases of the SSD and several former methods for texture classification in terms of percentages of correct classification and the number of features used.

2002

Current content-based image retrieval techniques can typically perform efficient and effective searches on heterogeneous image databases. This contribution deals with an approach based on the integration of color and texture description which is applied to a very homogeneous database: a blood image bank. The content of images is very similar and therefore it becomes imperative to use very precise descriptors: the color is described by classical color distributions (histograms) and for the texture, we introduce the morphological color size distributions. The similarity is measured by computing distance metrics between histograms. In order to increase the accuracy of retrieval, the results of color-based and texture-based retrieval are integrated by combining the associated dissimilarity values. The effects of different integration methods on classification performance are explained by means of experimental tests in a database of 123 cell images (leukocyte color images). After learning processing, where different feature selection and classifier definition alternatives are tested, a definitive integrated approach is proposed (precision ¢£¤££¥ ).

IAPR International Workshop on Pattern Recognition in Information Systems.(June 2007)

Morphological signatures are powerful descriptions of the image content which are based on the framework of mathematical morphology. These signatures can be computed on a global or local scale: they are called pattern spectra (or granulometries and antigranulometries) when measured on the complete images and morphological profiles when related to single pixels. Their goal is to measure shape distribution instead of intensity distribution, thus they can be considered as a relevant alternative to classical intensity histograms, in the context of visual pattern recognition. A morphological signature (either a pattern spectrum or a morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. Even if it can be used directly to solve various pattern recognition problems related to image data, the simple definitions given in the binary and grayscale cases limit its usefulness in many applications. In this paper, we introduce several 2-D extensions to the classical 1-D morphological signature. More precisely, we elaborate morphological signatures which try to gather more image information and do not only include a dimension related to the object size, but also consider on a second dimension a complementary information relative to size, intensity or spectral information. Each of the 2-D morphological signature proposed in this paper can be defined either on a global or local scale and for a particular kind of images among the most commonly ones (binary, grayscale or multispectral images). We also illustrate these signatures by several real-life applications related to object recognition and remote sensing.

Signal, Image and Video Processing, 2015

In this paper, we explore an original way to compute texture features for color images in a vector process. Using a dedicated approach for color ordering, we produce a complete framework for color mathematical morphology adapted to human visual system characteristics. Then, morphological multiscale texture features are defined. To understand the texture feature behavior, we present the feature response to basic images variations. Finally, we compare the texture feature performance in front of a classical classification task using Outex database.

2019

Placed within the context of content-based image retrieval, we study in this paper the potential of morphological operators as far as color description is concerned, a booming field to which the morphological framework, however, has only recently started to be applied. More precisely, we present three morphology-based approaches, one making use of granulometries independently computed for each subquantized color and two employing the principle of multiresolution histograms for describing color, using respectively morphological levelings and watersheds. These new morphological color descriptors are subsequently compared against known alternatives in a series of experiments, the results of which assert the practical interest of the proposed methods.

Digital Signal Processing, 2003

The purpose of this paper is twofold. First, we provide an extensive review of the state-of-theart in scale-space generation techniques for image processing, including linear methods, diffusionbased methods, and emphasizing morphological methods. Then, we introduce a new morphological approach to scale-space, called the lomo scale-space. The technique introduces a novel twodimensional generalization of the concept of locally monotonic (lomo) signals. The lomo scalespace is a sequence of locally monotonic image representations where the scale is specified by the spatial extent or degree of local monotonicity. The morphological process used to generate the lomo scale-space retains many desirable properties of other morphological methods, such as edge localization and smoothing of extrema. In contrast to previous morphological scale-space methods, the filters employed here are self-dual, and thus do not induce a gray-level bias into the scaled signal representations. The scale-space methods reviewed and introduced in this paper are applicable to several multiscale image processing tasks such as segmentation, object-based image compression, content-based retrieval, and video tracking.

2005

2007

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

In this paper, we describe a multiscale and multishape morphological method for pattern-based analysis and classification of gray-scale images using connected operators. Compared with existing methods, which use structuring elements, our method has three advantages. First, in our method, the time needed for computing pattern spectra does not depend on the number of scales or shapes used, i.e., the computation time is independent of the dimensions of the pattern spectrum. Second, size and strict shape attributes can be computed, which we use for the construction of joint 2D shape-size pattern spectra. Third, our method is significantly less sensitive to noise and is rotation-invariant. Although rotation invariance can also be approximated by methods using structuring elements at different angles, this tends to be computationally intensive. The classification performance of these methods is discussed using four image sets: Brodatz, COIL-20, COIL-100, and diatoms. The new method obtains better or equal classification performance to the best competitor with a 5 to 9-fold speed gain.

2009

Abstract Placed within the context of content-based image retrieval, we study in this paper the potential of morphological operators as far as color description is concerned, a booming field to which the morphological framework, however, has only recently started to be applied.