3D Texture Feature Extraction and Classification using the BM3DELBP approach

Barburiceanu, Stefania; Terebes, Romulus; Meza, Serban

doi:10.1109/iccp51029.2020.9266264

Cited by 2 publications

(2 citation statements)

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“…The proposed method covered in the article extends our prior contributions validated in the two conference papers [28] and [29] by fusing the two results and provides a complex means of feature description in 3D. In [28], we proposed the 3D version of BM3DELBP (BM3DELBP_3D) whose feature space is constructed based on the signs of differences between neighbouring pixels without any information regarding the amount of difference such as the contrast or degree of homogeneity in the analysed image. This knowledge is captured by the Improved 3D Gray-Level Co-Occurrence Matrix (IGLCM_3D) introduced by us in [29].…”

Section: The Proposed Methodsmentioning

confidence: 99%

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3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…We proposed in [28] the extension of the BM3DELBP operator to volumetric texture feature extraction. Figure 4 illustrates the functional block scheme describing the computation of this operator by considering only one observation scale.…”

Section: The Extension Of Bm3delbp To 3d (Bm3delbp_3d)mentioning

confidence: 99%