Face recognition is one of the most effective image-processing applications and is essential in the technological era. The identification of the facial image is a current problem for authentication purposes, particula...
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Face recognition is a rapidly advancing field with numerous applications in security, surveillance, biometrics, and human-computer interaction. This paper presents an innovative approach for automatic face recognition...
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In this research, we suggested a novel texture descriptor distance-based Adjacent local binary pattern AdLBP based on the adjacent neighbor window and the relationships among the sequential neighbors pixel value with ...
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This paper proposes a new hyperspectral texture descriptor, which is a variant of local binary pattern (LBP) for hyperspectral imaging. This descriptor effectively describes the texture information in hyperspectral im...
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Identifying a person and recognizing their individuality has been the most important necessity in any community or organization. So, attendance management is a necessary tool in any environment where maintaining atten...
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The work presented in this research outlines a new method for detecting bone fractures, which is based on a machine learning model. To achieve this goal, Support Vector Machine (SVM) classifiers are trained on a datas...
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local binary pattern (LBP) has received widespread attention since proposed, especially in the fields of texture classification and face recognition. Most of the currently proposed LBP variants are still improved base...
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Purpose This paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing i...
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Purpose This paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image. Design/methodology/approach The structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged. Findings The performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique. Originality/value The proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.
Aiming at the problem of low resolution and small sample size of pollen images, this paper proposes a pollen image classification method based on localbinary mode. This method first performs preprocessing such as sha...
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ISBN:
(纸本)9783030971243;9783030971236
Aiming at the problem of low resolution and small sample size of pollen images, this paper proposes a pollen image classification method based on localbinary mode. This method first performs preprocessing such as sharpening and normalization on the pollen image. For the preprocessed image, calculate the local binary pattern. Then extract the directional gradient histogram operator of the local binary pattern calculation result as the identification feature. And finally, use the SVM as the classifier for the classification and recognition of the three-dimensional pollen image. Through the experiment on the European Confocal standard pollen database, the results show that the recognition rate of this method can exceed 95% at the highest, and at the same time, it has better robustness to the proportion and pose changes of pollen images, and has better recognition effect than traditional methods.
In this chapter, local binary pattern (LBP) based extreme learning machine (ELM) is presented for identification of high-dimensional face images of different resolution. In this scheme, LBP is used for the representat...
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