To address the drawbacks of existing secret data hiding techniques, such as their high computational demands for training deep neural networks or their limited hiding capacity, we present a new approach. Our proposed ...
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This study aims to improve the accuracy of coffee bean classification by utilizing local binary pattern (LBP) extraction with Modular Neural Network (MNN). Coffee, one of Indonesia's leading commodities, plays a v...
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Facial recognition technology allows both the government and the general public to identify individuals based on their facial features, even in cases of significant changes. However, low lighting during the facial rec...
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Recently, the use of artificial intelligence to improve the efficiency of Covid-19 diagnosis has become a trend due to the spread and proliferation of Covid-19 and the fact that healthcare professionals alone are no l...
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The local binary pattern (LBP) is an effective feature, describing the size relationship between the neighboring pixels and the current pixel. While individual LBP-based methods yield good results, co-occurrence LBP-b...
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The local binary pattern (LBP) is an effective feature, describing the size relationship between the neighboring pixels and the current pixel. While individual LBP-based methods yield good results, co-occurrence LBP-based methods exhibit a better ability to extract structural information. However, most of the co-occurrence LBP-based methods excel mainly in dealing with rotated images, exhibiting limitations in preserving performance for scaled images. To address the issue, a cross-scale co-occurrence LBP (CS-CoLBP) is proposed. Initially, we construct an LBP co-occurrence space to capture robust structural features by simulating scale transformation. Subsequently, we use Cross-Scale Co-occurrence pairs (CS-Co pairs) to extract the structural features, keeping robust descriptions even in the presence of scaling. Finally, we refine these CS-Co pairs through Rotation Consistency Adjustment (RCA) to bolster their rotation invariance, thereby making the proposed CS-CoLBP as powerful as existing co-occurrence LBP-based methods for rotated image description. While keeping the desired geometric invariance, the proposed CS-CoLBP maintains a modest feature dimension. Empirical evaluations across several datasets demonstrate that CS-CoLBP outperforms the existing state-of-the-art LBP-based methods even in the presence of geometric transformations and image manipulations.
S R C Texture features play a vital role in content-based image retrieval (CBIR) applications. Most texture extraction methods have a low accuracy and high feature vector length. This paper presents a novel hexagonal ...
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S R C Texture features play a vital role in content-based image retrieval (CBIR) applications. Most texture extraction methods have a low accuracy and high feature vector length. This paper presents a novel hexagonal local binary pattern (HLBP) to extract more informative and compact features from images. To have robust patterns against rotation, rotation invariant hexagonal patterns are presented using cyclic set theory. Texture feature vector is extracted from hexagonal images based on proposed patterns and used in CBIR application. To evaluate proposed method, experiments are performed in five datasets Corel-1k, Brodatz, VisTex, Corel-10k, and STex. The proposed HLBP method outperforms square local binary pattern (SLBP) in images with noise in the terms of precision. The feature vector length of the proposed method is 64, which is much shorter than those in competitive methods and leads to high speed in retrieval phase. The best performance of the proposed method is revealed in texture datasets which achieved the highest precision among all competitive methods.
In recent days, local binary pattern and their variants plays a vital role in classification of EEG signals. Hence, in this paper a novel method for classification of EEG signals in accordance with localbinary patter...
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In recent days, local binary pattern and their variants plays a vital role in classification of EEG signals. Hence, in this paper a novel method for classification of EEG signals in accordance with local binary pattern is proposed. Initially, the EEG signal with 9 points is considered and then the average of the signal points existing at various distances such as 1, 2 and 3 are computed. Then, the computed average value for an EEG segment is compared with the center signal point of an EEG segment and thus yields the binary value 0 or 1. Later, the majority rule is employed resulting in 8 bit binarypattern. Also, the average of EEG segment is compared with center point. The decimal equivalent of the generated binary code is referred as the proposed label. Later, the histogram is generated involving the proposed label and then this histogram are used as the feature for an EEG signal. The experimental results on Bonn and Freiburg dataset shows that the proposed method achieves 75.81% and 94.43%, 72.43% and 89.30% in sensitivity and specificity, respectively. Also, the performance is evaluated in terms of classification accuracy with KNN and SVMclassifier by changing the training and testing data. The results indicate that the proposed method could distinguish the seizure and seizure free EEG signals evidently.
In recent years, with the rapid growth of digital communication, the protection of image privacy has become a critical concern. Traditional image encryption methods may attract attacker attention due to the noise-like...
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In recent years, with the rapid growth of digital communication, the protection of image privacy has become a critical concern. Traditional image encryption methods may attract attacker attention due to the noise-like appearance of cipher image. To address the theft risk to the private image, a novel three-dimensional chaotic map of Chebyshev coupled Logistic with Sine map (3D-CCLSM) is designed, and then an image hiding algorithm based on local binary pattern (LBP) and compressive sensing (CS) is proposed, named ImHALC. By integrating LBP-based texture feature extraction and CS-based image compression, ImHALC aims to enhance both the security and imperceptibility for steganographic image. Especially, LBP is taken to connect the plain image and keystream, resulting a high security for ImHALC. Firstly, in stage of keystream generation, texture information of the plain image is extracted by using LBP and seen as input of hash function SHA-256, to produce corresponding hash values. Then, these hash values are used to generate the initial value of 3D-CCLSM by a new designed key transformation model (KAM), so as to get the keystream for encryption. Secondly, in stage of image compression, a measurement matrix is constructed by above keystream, and CS is applied to the plain image to get measurements. Thirdly, in stage of image encryption, measurements are confused and diffused to produce a cipher image. Finally, in stage of embedding, an embedding method using integer wavelet transformation (IWT) and 2k correction is presented, so as to embed the secrets (i.e., cipher image) into a given carrier image to obtain hiding performance, i.e., forming a carrier image hiding secrets (CHS). In particular, the ImHALC can achieve the effect of blind extraction. After adopting a two-dimensional projection gradient algorithm with embedded decryption (2DPG-ED), the reconstruction quality for the plain image is good for test images.
Facial recognition is a challenging pattern recognition problem in computer vision. This paper proposes a face recognition system that uses Empirical Mode Decomposition (EMD) and local binary pattern (LBP) based featu...
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Facial recognition is a challenging pattern recognition problem in computer vision. This paper proposes a face recognition system that uses Empirical Mode Decomposition (EMD) and local binary pattern (LBP) based feature extraction for a robust face recognition system. This scheme initially decomposes the image into 2N number of IMF (Intrinsic Mode Function) images, where N numbers of IMF images are estimated in the X direction and N number of IMF images are estimated in the Y direction. From the 2N number of IMFs, the M number of best matching IMF image pairs is estimated using 2D Discrete Fourier Transform (DFT). The IMF pair is added to extract the IC-LBP (Intensity Compensated-local binary pattern) features. The IC-LBP features are extracted from the IMF images such that the center intensity is adjusted based on an adaptive intensity threshold. The use of EMD and IC-LBP features uses the essential descriptors that best represent a facial image. The same process is repeated in the testing phase where the test image is categorized from the trained images using the Naive Bayes algorithm. The performance evaluation was done using the Yale, MORPH, and FGNET using metrics such as time complexity and recognition rate on different types of test face images like partial faces, different lightning, and rotation. Results show that the proposed face recognition system outperforms the traditional algorithms. Results show that the proposed face recognition system outperforms the traditional algorithms.
This investigation attempts to propose a novel Wavelet and local binary pattern-based Xception feature Descriptor (WLBPXD) framework, which uses a deep-learning model for classifying chronic infection amongst other in...
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This investigation attempts to propose a novel Wavelet and local binary pattern-based Xception feature Descriptor (WLBPXD) framework, which uses a deep-learning model for classifying chronic infection amongst other infections. Chronic infection (COVID-19 in this study) is identified via RT-PCR test, which is time-consuming and requires a dedicated laboratory (materials, equipment, etc.) to complete the clinical results. X-rays and computed tomography images from chest scans offer an alternative method for identifying chronic infections. It has been demonstrated that chronic infection can be diagnosed from X-ray images acquired in a real-world setting. The images are transformed using the discrete wavelet transform (DWT), combined with the local binary pattern (LBP) technique. Pre-trained deep-learning models, such as AlexNet, Xception, VGG-16 and Inception Resnet50, extract the features. Subsequently, the extracted features are fused using feature-fusion approaches and subjected to classification. The AlexNet, in conjunction with the DWT model, produced 99.7% accurate results, whereas the AlexNet and the LBP model produced 99.6% accurate results. Therefore, the proposed method is efficient as it offers a better detection accuracy and eventually enhances the scope of early detection, thus assisting the clinical perspectives.
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