Image Inconsistency Detection Using Local Binary Pattern (LBP)

Mahale, Vivek; Ali, Mouad M. H.; Yannawar, Pravin; Gaikwad, Ashok T.

doi:10.1016/j.procs.2017.09.097

Cited by 17 publications

(4 citation statements)

References 12 publications

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“…Pattern and the hue of fruit imagery extracted with LBP were proven to be able to systematically extract differences in fruit peel of each cultivar. This is in line with research conducted by Kaewchote, Janyong, & Limprasert (2018), Kaplan, Kaya, Kuncan, &Ertunç (2020), andMahale, Ali, Yannawar, &Gaikwad (2017), where the use of LBP produces texture features that prove optimal as input in the classification process. The results of recognition using the shape feature although produces the lowest accuracy compared to the use of texture features, but the accuracy was still quite high which can reach 97.74% (unripe Ambon-Goroho).…”

Section: Test Results On Ambon-goroho-hijau Banana Groupsupporting

confidence: 86%

Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data

et al. 2022

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Visually differentiating banana cultivar with high similarity in shape, color and peel texture requires skill and experience during harvesting to reduce mistake on identifying cultivar. This study aims to identify automatically some similar banana cultivars using banana finger imagery and computer vision. The identification process was carried out to distinguish two groups of bananas with high similarities, namely group 1 (Ambon, Hijau, Goroho) and group 2 (Barlin, Mas). The test was conducted on the pair of datasets of unripe Ambon-Hijau-Goroho, ripe Hijau-Goroho, ripe and unripe Barlin-Mas. Testing was done to determine the performance of identification and to find out the most effective characteristics that could be used as cultivar identification. Results of classification using extreme learning machine (ELM) showed that texture features extracted from local binary pattern (LBP) could accurately distinguish unripe Ambon-Goroho, unripe Goroho-Hijau, ripe Goroho-Hijau with 100% accuracy. While unripe Ambon-Hijau, unripe Barlin-Mas and ripe Barlin-Mas could be optimally distinguished using a combination of shape and peel texture features with accuracy of 93.39%, 89.68%, 99.31% respectively. This result indicated that the proposed method could be used as an alternative of automatic banana sortation during post-harvest. The use of shape and peel texture features had shown effectively differentiating these high similarity banana cultivars.

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Section: Test Results On Ambon-goroho-hijau Banana Groupsupporting

confidence: 86%

Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data

et al. 2022

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“…The improved Local Binary Patterns has the following advantages: gray scale invariance; rotation invariance; multiresolution analysis; and, because of its low computational complexity, it is a very practical image processing method [21] [22]. Some LBP based improvement algorithms have also been introduced, Yuan et al [23] proposed Gabor filter and CS-LBP operator to extract the polarized image texture effectively.…”

Section: Introductionmentioning

confidence: 99%

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Wang

et al. 2019

Optik

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The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature.

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“…It is a [37] measure of the probability distribution shape of a random variable [36]. Mathematically kurtosis is given by Equation (12) \tilde{f}(x,y)=\frac{\frac{1}{mn-1}\sum _{(r,c)ϵW}\text{ }\left(\frac{1}{mn-1}\sum _{(r,c)ϵW}\text{ }g\right(r,c)-\frac{1}{mn-1}\sum _{(r,c)ϵW}\text{ }g(r,c){)}^{4}}{\frac{1}{mn-1}\sum _{(r,c)ϵW}\text{ }\left(\frac{1}{mn-1}\sum _{(r,c)ϵW}\text{ }g\right(r,c)-\frac{1}{mn-1}\sum _{(r,c)ϵW}\text{ }g(r,c){)}^{2}} 12…”

Section: Histogram Oriented Gradient (Hog)mentioning

confidence: 99%

“…The multi-branch regression tree is used in AdaBoost learning to reduce computation cost and takes less training time with the reduced feature set. Mahale et al, [12] introduced a framework for detecting inconsistency in the image using block a division strategy. Divided the image into blocks of equal size, Then the LBP value for each block is computed and added all the block values.…”

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confidence: 99%

Soft Computing based Artificial Neural Network and Multiple Feature set Intelligence system for Image Retrieval

Arunalatha

Vasanthakumar

et al. 2022

Preprint

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This paper proposes an Image Retrieval model using Multiple Feature Sets and Artificial Neural Network (IR-MFS-ANN), where the multiple features Histogram of oriented Gradient (HoG), Overlapping Local Binary Pattern (OLBP), Color and Statistical features are considered. Visual information is one of the most important data in the field of social networking, medicine, military, and these areas contain an enormous volume of semi-organized and organized heterogeneous information related to explicit subjects. However, retrieval and usage of suitable information from the comprehensive information archives are important to meet the content extraction and retrieval challenges. To improve retrieval performance, the image representation, modeling, scalable algorithm that permit accessing large archives are integrated into the retrieval framework. The proposed model utilizes ANN to find the distance between feature vectors. The proposed algorithm is tested and analyzed with various retrieval techniques and it is found that ANN-based image retrieval outperforms the state-of-the-art techniques [1–5]. The proposed method results in accuracy of 94%, 92%, 95%, and 94% for Wang, Cifar-10, Oxford Flower, and ImageNet standard databases respectively.

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Image Inconsistency Detection Using Local Binary Pattern (LBP)

Cited by 17 publications

References 12 publications

Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data

Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

Soft Computing based Artificial Neural Network and Multiple Feature set Intelligence system for Image Retrieval

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