Residues of morphological filtering by reconstruction for texture classification

doi:10.1016/S0031-3203(96)00146-X

Pattern Recognition

Volume 30, Issue 7, July 1997, Pages 1081-1093

https://doi.org/10.1016/S0031-3203(96)00146-X Get rights and content

Abstract

This paper presents a set of texture features which is based on morphological residues of opening and closing by reconstruction. In texture classification, this set of features is proven much more robust to noise than the feature set derived from traditional morphological residues. An optimization algorithm is established to search for the optimum feature subset. The robustness to noise of our feature set is investigated in detail qualitatively and quantitatively. In various noise circumstances as well as in image deformation, it is found that this feature set bears quite high texture classification accuracy compared to other texture classification methods.

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Cited by (12)

Image segmentation based on ultimate levelings: From attribute filters to machine learning strategies
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The residual value of these operators can reveal important contrast information in images. This class of operators includes some existing ones, such as maximum difference of openings (resp., closings) by reconstruction [19], differential morphological profiles [26], ultimate attribute openings [8,28], differential attribute profiles [10], shape ultimate attribute openings [16], and ultimate grain filters [4]. They have successfully been used in a large number of applications such as texture features extraction [19], segmentation of high-resolution satellite imagery [10], text location [3,28], segmentation of building façades [16], and plant bounding box detection [1,2].
Ultimate levelings are operators that extract important image contrast information from a scale-space based on levelings. Along with the residual extraction process, some residues usually come from undesirable regions, and they should be filtered out. For this, some strategies can be applied to filter these undesirable residues. In this paper, we introduce a new approach to detect desirable regions from ultimate levelings through a new hierarchical structure called residual tree. From this structure, we extract attribute vectors to build a machine learning model which gives a matching value between ground truth regions and residual tree nodes. Thus, from the selected residual tree nodes, we present a new approach to choose the best disjoint residual nodes which gives the regions of the ultimate levelings. Finally, from the ultimate levelings, we use its partition associated function to solve the segmentation problem. In order to evaluate our new approach, some experiments were carried out with a plant dataset and results report the state-of-the-art performance in plant segmentation.
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Furthermore, ultimate levelings are computationally efficient and their performance evaluations are comparable to the state of art methods for filtering and image segmentation. For example, some operators included in ultimate levelings class have been demonstrated to have good perfomance evaluation (Alves and Hashimoto, 2010; 2014; Hernández and Marcotegui, 2011; Li et al., 1997; Retornaz and Marcotegui, 2007). We also provide a plugin and source code for the popular free image processing software ImageJ (Rasband, 1997) that gives access for some ultimate levelings operators.
This work presents a new class of residual operators called ultimate levelings which are powerful image operators based on numerical residues. Within a multi-scale framework, these operators analyze a given image under a series of levelings. Thus, contrasted objects can be detected if a relevant residue is generated when they are filtered out by one of these levelings. Our approach consists of, firstly, (i) representing the input image as a morphological tree; then, (ii) showing that a certain operation on this tree results in a leveling operator; and finally (iii) demonstrating that a sequential application of this operation on the tree is able to produce a family of levelings that satisfies scale-space properties. Besides, other contributions of this paper include: (i) the statement of properties of ultimate levelings, (ii) the presentation of an efficient algorithm for their computation, (iii) the provision of strategies for choosing families of primitives, (iv) the presentation of strategies for filtering undesirable residues, and (v) the provision of some illustrative examples of application of ultimate levelings. Furthermore, ultimate levelings are computationally efficient and their performance evaluations are comparable to the state of art methods for filtering and image segmentation.
Comparative study of moment based parameterization for morphological texture description
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Citation Excerpt :
These variations mostly concern two basic variables: the chosen filter and the parameterization technique. As far as filters are concerned, the combined use of dual operators such as opening along with closing represents the most usual case [8], with their reconstruction [9], attribute [10] and area based [11] counterparts having also been investigated. However, although various statistical measures have been explored for post-processing the resulting numerical distributions in the context of local granulometries during the 1990s, global parameterization techniques have received somewhat less attention.
The two principal morphological texture descriptors, granulometry and morphological covariance, rely on the common principle of successive filtering of an image using a variety of structuring elements, from which feature vectors are subsequently computed. A crucial stage of their computation is the numerical characterization or parameterization of each of the filtered images. In this regard, the zero-th statistical moment is the traditional measure, while the use of higher order moments has also been reported. In this paper, we present the results of a comparative study, concentrating on the potential of various statistical moments for the task of parameterization, while additionally investigating the contribution of Fourier transform moments. The experiments are conducted with focus on texture description effectiveness and on noise robustness, using publicly available texture collections: Outex, CUReT and KTH-TIPS2b, where it is shown that the combination of moments leads to superior classification performance even at high noise levels.
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This paper presents a bibliography of nearly 1700 references related to computer vision and image analysis, arranged by subject matter. The topics covered include computational techniques; feature detection and segmentation; image and scene analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, including geometry and graphics, compression and processing, sensors and optics, visual perception, neural networks, artificial intelligence and pattern recognition, as well as on applications.
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Ultimate leveling based on Mumford-Shah energy functional applied to plant detection
2018, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Residues of morphological filtering by reconstruction for texture classification

Abstract

J. Visual Comm. Image Representation

Pattern Recognition

CVGIP: Graphical Models Image Process.

Comput. Vision Graphics Image Process.

Pattern Recognition Lett.

Signal Process.

Signal Process.

Digital Image Processing

Statistical and structural approaches to texture

Texture features for image classification

IEEE Trans. Systems Man Cybernet.

Comparison between co-occurrence matrices, local histograms and curvilinear integration for texture characterisation

A theory for multiresolution signal decomposition: the wavelet representation

IEEE Trans. Pattern Analysis Mach. Intell.