Improving texture extraction and classification using smoothed morphological operators

Neiva, Mariane Barros; Vacavant, Antoine; Bruno, Odemir Martinez

doi:10.1016/j.dsp.2018.06.001

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…In addition, local and global features are extracted from each image. The results of our method were compared with those of several other feature extraction implementations on the Brodatz database with those published in References [1,27,28,31,43,44,50], on the Vistex database with those published in References [4,9,13,23,27,28,30,38,45,46,50], on the Outex database with those published in References [4,8,9,11,27,28,30,38,45,46,50], and on the KTH-TIPS2b with those published in References [7,8,31,34,35] (please see Tables 2, 4, 6 and 8). The proposed method generated better results than those that were published previously.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the color features of the image are represented in this histogram. Haralick's features [3] are often extracted to characterize textures that measure the grayscale distribution, as well as considering the spatial interactions between pixels [3,9,23,38]. Creating a training set is part of the strategy for obtaining local and global features that contain all the information, local and global, for achieving correct classification.…”

Section: Methodsmentioning

confidence: 99%

“…It can be observed in Table 4 that our post-processed method reached the highest results with 100%. [50] Deterministic walks' direction histogram 99.65% Cernadas et al, 2017 [30] Parallel vectors 99.5% Neiva et al, 2018 [23] Smoothed morphological operators (SMO) 99.54% Kalakech et al, 2018 [38] Adapted Laplace score 94.9%…”

Section: Brodatz Databasementioning

confidence: 99%

“…The adaptive integration of color and texture attributes into the development of complex image descriptors is an area of intensive research in computer vision [21]. Most of these investigations focused on the integration process in applications for digital image segmentation [20,22] or the aggregation of multiple preexisting image descriptors [23]. Deep learning was applied successfully to object or scene recognition [24] and scene classification [25], and the use of deep neural networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations was investigated [26].…”

Section: Introductionmentioning

confidence: 99%

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Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Navarro-Clavería¹

2019

Applied Sciences

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Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Brodatz Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Navarro-Clavería¹

2019

Applied Sciences

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“…However, authors there did not provide a quantity for comparing homogeneity among images, but a multiresolution representation that can be used for texture characterization. More recent methods dealing with texture classification can be seen in [17,20]. These works deal with texture classification by applying machine learning algorithms to a set of features obtained from the image.…”

Section: Related Workmentioning

confidence: 99%

Topological Homogeneity for Electron Microscopy Images

Molina‐Abril

Díaz-del-Río

Guerrero-Lebrero

et al. 2018

Computational Topology in Image Context

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Bullet Impact Detection in Silhouettes Using Mask R-CNN

Vilchez

Mauricio

2020

IEEE Access

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The bullet impact detection in the silhouette plays an important role in the institutions which have a shooting range, specially where are regulated the license to carry a gun and where the evaluation manual process could present distortion in the ability's evaluation of the shooter and like a consequence the license's issuance. This paper proposes a method for an automatic detection of the bullet impacts in silhouettes based on deep learning and image processing, which consist of the following steps: preprocessing, impacts detection, edge detection and evaluation results. The experiments about 600 silhouettes with 2401 bullet impacts images of the proposed and implemented method considering the models Resnet 50 and Resnet 101 for Mask R-CNN show that Resnet 50 get better results than Resnet 101, achieving 97.6 %, 99.5 %, and 97.9 %, of accuracy, precision and recall, respectively, above the methods Circular Hough Transform, Circlet Detection, Random Sample Consensus, Randomized Hough Transform, Randomized Circle Detection, Support Vector Machine, Faster R-CNN, MnasNet and YOLO. Also, the results show 100 % of effectiveness in the edge detection and the count of the detected bullet impacts.INDEX TERMS Edge detection, bullet impact detection, image processing, Mask R-CNN.

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Improving texture extraction and classification using smoothed morphological operators

Cited by 6 publications

References 20 publications

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Topological Homogeneity for Electron Microscopy Images

Bullet Impact Detection in Silhouettes Using Mask R-CNN

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